Applied sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Content

Bulletin of the Polish Academy of Sciences: Technical Sciences | 2021 | 69 | 4

Download PDF Download RIS Download Bibtex

Bibliography

  1.  Ł. Czekierda, F. Malawski, R. Straś, K. Zieliński, and S. Zieliński, “Leveraging cloud environment flexibility to smoothen the transition to remote teaching during covid-19 pandemic – a case study,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137934, 2021, doi: 10.24425/ bpasts.2021.137934.
  2.  A. Gryszczyńska, “The impact of the covid-19 pandemic on cybercrime,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137933, 2021, doi: 10.24425/bpasts.2021.137933.
  3.  W. Jamroga, D. Mestel, P. Roenne, P. Ryan, and M. Skrobot, “A survey of requirements for covid-19 mitigation strategies,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137724, 2021, doi: 10.24425/bpasts.2021.137724.
  4.  M. Gruda and M. Kędziora, “Analyzing and improving tools for supporting fighting against covid-19 based on prediction models and contact tracing,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137414, 2021, doi: 10.24425/bpasts.2021.137414.
  5.  A. Bobowski, J. Cichoń, and M. Kutyłowski, “Extensions for apple-google exposure notification mechanism,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137126, 2021, doi: 10.24425/bpasts.2021.137126.
  6.  M. Kozielski et al., “Enhancement of covid-19 symptom-based screening with quality-based classifier optimisation,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137349, 2021, doi: 10.24425/bpasts.2021.137349.
  7.  M. Paciorek, D. Poklewski-Koziełł, K. Racoń-Leja, A. Byrski, M. Gyurkovich, and W. Turek, “Microscopic simulation of pedestrian traffic in urban environment under epidemic conditions,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137725, 2021, doi: 10.24425/ bpasts.2021.137725.
  8.  M. Łoś, M. Woźniak, I. Muga, and M. Paszyński, “Threedimensional simulations of the airborne covid-19 pathogens using the advection- diffusion model and alternating-directions implicit solver,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, p. e137125, 2021, doi: 10.24425/ bpasts.2021.137125.
Go to article

Authors and Affiliations

Aneta Afelt
1
Aleksander Byrski
2
ORCID: ORCID
Victor Calo
3
Tyll Krüger
4
Lech Madeyski
4
ORCID: ORCID
Wojciech Penczek
5

  1. Institut de Recherche pour le Développement, Montpellier, France
  2. AGH University of Science and Technology, Krakow, Poland
  3. Curtin University, Perth, Australia
  4. Wroclaw University of Science and Technology, Wroclaw, Poland
  5. Institute of Computer Science, PAS, Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

Cloud-based computational environments can offer elastic and flexible services to wide audiences. Małopolska Educational Cloud was originally developed to support the day-to-day collaboration of geographically scattered schools with universities which organized online classes, led by university teachers, as an amendment to face-to-face teaching. Due to the centralized management and ubiquitous access, both the set of services provided by MEC and their usage patterns can be adjusted rapidly. In this paper we show how – during the COVID-19 pandemic – the flexibility of Małopolska Educational Cloud was leveraged to speed up the transition from in-class to remote teaching, both in the classes and schools which were already involved in the MEC project, and newly added ones. We also discuss the actions that were required to support the smooth transition and draw conclusions for the future.
Go to article

Bibliography

  1.  K. Zieliński, Ł. Czekierda, F. Malawski, R. Straś, and S. Zieliński, “Recognizing value of educational collaboration between high schools and universities facilitated by modern ICT,” J. Comput. Assisted Learn., vol. 33, no. 6, pp. 633–648, 2017.
  2.  Ł. Czekierda, K. Zieliński, and S. Zieliński, “Automated orchestration of online educational collaboration in cloud-based environments,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 17, no. 1, pp. 1–26, 2021.
  3.  “WHO coronavirus disease (COVID-19) pandemic,” https://www.who.int/emergencies/diseases/novel-coronavirus-2019, accessed: 2020-12- 30.
  4.  N. Fernandes, “Economic effects of coronavirus outbreak (COVID-19) on the world economy,” IESE Business School Working Paper No. WP-1240-E, 2020, doi: 10.2139/ssrn.3557504.
  5.  V. Venkatesh, “Impacts of COVID-19: A research agenda to support people in their fight,” Int. J. Inf. Manage., vol. 55, p. 102197, 2020.
  6.  A. Tubadji, F. Boy, and D.J. Webber, “Narrative economics, public policy and mental health,” Covid Econ., vol. 20, pp. 109–131, 2020.
  7.  “UNESCO COVID-19 impact on education,” https://en.unesco.org/covid19/educationresponse, accessed: 2020-12-30.
  8.  G. Vial, “Understanding digital transformation: A review and a research agenda,” J. Strategic Inf. Syst., vol. 28, no. 2, pp. 118–144, 2019.
  9.  Y.K. Dwivedi, D.L. Hughes, C. Coombs, I. Constantiou, Y. Duan, J.S. Edwards et al., “Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life,” Int. J. Inf. Manage., vol. 55, p. 102211, 2020.
  10.  T.D. Oyedotun, “Sudden change of pedagogy in education driven by COVID-19: Perspectives and evaluation from a developing country,” Res. Globalization, vol. 2, p. 100029, 2020.
  11.  M. Assunção Flores and M. Gago, “Teacher education in times of COVID-19 pandemic in Portugal: national, institutional and pedagogical responses,” J. Educ. Teach., vol. 46, no. 4, pp. 507–516, 2020.
  12.  N. Iivari, S. Sharma, and L. Ventä-Olkkonen, “Digital transformation of everyday life–how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care?” Int. J. Inf. Manage., vol. 55, p. 102183, 2020.
  13.  L. Mishra, T. Gupta, and A. Shree, “Online teachinglearning in higher education during lockdown period of COVID-19 pandemic,” Int. J. Educ. Res. Open, vol. 1, p. 100012, 2020.
  14.  A. Qazi, et al., “Conventional to online education during COVID-19 pandemic: Do develop and underdeveloped nations cope alike,” Child. Youth Serv. Rev., vol. 119, p. 105582, 2020.
  15.  I. Asanov, F. Flores, D. McKenzie, M. Mensmann, and M. Schulte, “Remote-learning, time-use, and mental health of Ecuadorian high- school students during the COVID-19 quarantine,” World Dev., vol. 138, p. 105225, 2021.
  16.  M. Adnan and K. Anwar, “Online learning amid the COVID-19 pandemic: Students’ perspectives,” J. Pedagogic. Sociol. Psychol., vol. 2, no. 1, pp. 45–51, 2020.
  17.  C.M. Toquero, “Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context,” Pedag. Res., vol. 5, no. 4, 2020.
  18.  K.H. Mok, W. Xiong, G. Ke, and J.O.W. Cheung, “Impact of COVID-19 pandemic on international higher education and student mobility: Student perspectives from mainland China and Hong Kong,” Int. J. Educ. Res., vol. 105, p. 101718, 2021.
  19.  S.P. Becker, R. Breaux, C.N. Cusick, M.R. Dvorsky, N.P. Marsh, E. Sciberras, and J.M. Langberg, “Remote learning during COVID-19: Examining school practices, service continuation, and difficulties for adolescents with and without attention-deficit/hyperactivity disorder,” J. Adolesc. Health, vol. 67, no. 6, pp. 769–777, 2020.
  20.  M. Kogan, S.E. Klein, C.P. Hannon, and M.T. Nolte, “Orthopaedic education during the COVID-19 pandemic,” J. Am. Acad. Orthop. Surg., vol. 28, no. 11, pp. e456–e464, 2020.
  21.  V.A. Jones, K.A. Clark, C. Puyana, and M.M. Tsoukas, “Rescuing medical education in times of COVID-19,” Clin. Dermatol., vol. 39, no. 1, pp. 33–40, 2021.
  22.  N. Carroll and K. Conboy, “Normalising the “new normal”: Changing tech-driven work practices under pandemic time pressure,” Int. J. Inf. Manage., vol. 55, p. 102186, 2020.
  23.  W. Ali, “Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic,” High. Educ. Stud., vol. 10, no. 3, 2020.
  24.  F.M. Reimers and A. Schleicher, “A framework to guide an education response to the COVID-19 pandemic of 2020,” OECD. Retrieved April, vol. 14, no. 2020, pp. 2020–04, 2020.
Go to article

Authors and Affiliations

Łukasz Czekierda
1
Filip Malawski
1
Robert Straś
1
Krzysztof Zieliński
1
ORCID: ORCID
Sławomir Zieliński
1

  1. AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Download PDF Download RIS Download Bibtex

Abstract

The COVID-19 pandemic is accompanied by a cyber pandemic, involving changes in the modi operandi of perpetrators of various crimes, and an infodemic, associated with the spread of disinformation. The article analyses the impact of the COVID-19 pandemic on cybercrime and presents the latest research on the number of cybercrime cases in Poland and their growth dynamics. It determines the factors that contribute to the commission of a crime and prevent easy identification of criminals. It also suggests the legal and organisational changes that could reduce the number and effects of the most frequently recorded cyberattacks at a time of COVID-19. Particular attention is paid to legal problems of the growing phenomenon of identity theft, and the need to ensure better protection of users from phishing, including through education and proactive security measures consisting in blocking Internet domains used for fraudulent attempts to obtain data and financial resources.
Go to article

Bibliography

  1.  “Agari H2 2020 Email Fraud Report”. [Online]. Available: https://www.agari.com/cyber-intelligence-research/e-books/agari-h2-2020-email- fraud-report.pdf [Accessed: 15-Jun-2021].
  2.  “IC3 Internet Crime Report 2019”, p. 9. [Online]. Available: https://www.ic3.gov/Media/PDF/AnnualReport/2019_IC3Report.pdf [Accessed: 15-Jun-2021].
  3.  “Internet Organised Crime Threat Assessment (IOCTA) 2020” [Online]. Available: https://www.europol.europa.eu/activities-services/ main-reports/internet-organised-crime-threat-assessment-iocta-2020 [Accessed: 15-Jun-2021], hereinafter as: IOCTA 2020.
  4.  “How COVID-19-related crime infected Europe during 2020” [Online]. Available: https://www.europol.europa.eu/publications-documents/ how-covid-19-related-crime-infected-europe-during-2020 [Accessed: 15-Jun-2021].
  5.  Rise of fake ‘corona cures’ revealed in global counterfeit medicine operation. [Online]. Available: https://www.europol.europa.eu/newsroom/ news/rise-of-fake-%E2%80%98corona-cures%E2%80 %99-revealed-in-global-counterfeit-medicine-operation [Accessed: 15-Jun-2021].
  6.  Warnings about fake online shops are published on consumer or cybersecurity websites. Sample fake online shop search engine: “Suspicious online shops!” [Online]. Available: https://www.legalniewsieci.pl/aktualnosci/podejrzane-sklepy-internetowe [Accessed: 15-Jun-2021], [in Polish].
  7.  Criminal Code of June 6, 1997 (Journal of Laws of 2020, item 1444, as amended), hereinafter CC.
  8.  Already on 16 March 2020, criminals created a fraudulent fundraiser in Poland at https://pomoc.siepomaga.net/koronawirus?SS52.
  9.  “Annual Report on the Activities of CERT Poland. Security Landscape of the Polish Internet”, 2018, pp. 59–67. [Online]. Available: https:// www.cert.pl/uploads/docs/Raport_CP_2018.pdf [Accessed: 15-Jun-2021], [in Polish].
  10.  In connection with the discovered insufficient implementation of technical and organisational measures to secure customer data, by a decision of 10 September 2019, Morele.net Sp. z o.o. was charged with an administrative fine of PLN 2.8 million (Decision of President of the Personal Data Protection Office of 10 September 2019, no. ZSPR.421.2.2019, subsequently upheld by a judgement of the Provincial Administrative Court in Warsaw of 3 September 2020, no. II SA/Wa 2559/19, [in Polish].
  11.  Regulation of the Minister of National Education of 11 March 2020 on the temporary restriction of the functioning of educational facilities in relation to preventing, counteracting and combating COVID- 19 (Journal of Laws item 410 as amended), [in Polish].
  12.  Government’s bill to amend the Law on special solutions to prevent, counteract and combat COVID-1  9, other communicable diseases and the resultant crises, and to amend certain other laws, form no. 299 of 26 March 2020. [Online]. Available: http://sejm.gov.pl/Sejm9. nsf/druk.xsp?nr=299 [in Polish].
  13.  A. Gryszczyńska, “The use of COVID- 19 in scenarios of social engineering attacks”, Maritime Security Yearbook, 2021, pp. 137‒161, [Online]. Available: https://wdiom.amw.gdynia.pl/wp-content/uploads/2021/06/PT2020v0.13.pdf [in Polish].
  14.  M.S. Islam et al., “COVID- 19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis”, Am. J. Trop. Med. Hyg. 103(4), 1621–1629, (2020), doi: 10.4269/ajtmh.20-0812.
  15.  J. Tidy, “Dr Reddy’s: Covid vaccine-maker suffers cyber-attack”, BBC, Oct. 22, 2020 [Online]. Available: https://www.bbc.com/news/ technology-54642870 [Accessed: 15-Jun-2021].
  16.  “Advisory: APT29 targets COVID- 19 vaccine development”. [Online]. Available: https://www.ncsc.gov.uk/files/Advisory-APT29-targets- COVID-19-vaccine-development.pdf [Accessed: 15-Jun-2021].
  17.  BBC News, “Pfizer/BioNTech vaccine docs hacked from European Medicines Agency”, BBC, Dec. 09, 2020 [Online]. Available: https:// www.bbc.com/news/technology-55249353 [Accessed: 15-Jun-2021].
  18.  “Pandemic profiteering: how criminals exploit the COVID- 19 crisis”. [Online]. Available: https://www.europol.europa.eu/publications- documents/pandemic-profiteering-how-criminals-exploit-covid-19-crisis [Accessed: 15-Jun-2021].
  19.  Wired, Sep. 19, 2020 [Online]. Available: https://www.wired.com/story/a-patient-dies-after-a-ransomware-attack-hits-a-hospital [Accessed: 15-Jun-2021].
  20.  “Annual Report on the Activities of CERT Poland. Security Landscape of the Polish Internet”, 2019. [Online]. Available: https://www. cert.pl/uploads/docs/Raport_CP_2019.pdf [Accessed: 15-Jun-2021], [in Polish].
  21.  “Report on the state of Poland’s cybersecurity in 2019”. [Online]. Available: https://csirt.gov.pl/cer/publikacje/raporty-o-stanie- bezpi/969,Raport-o-stanie-bezpieczenstwa-cyberprzestrzeni-RP-w-2019-roku.html [Accessed: 15-Jun-2021], [in Polish].
  22.  A. Pérez-Escoda, C. Jiménez-Narros, M. Perlado-Lamo-de-Espinosa, and L. Miguel Pedrero-Esteban, “Social Networks Engagement During the COVID-1  9 Pandemic in Spain: Health Media vs. Healthcare Professionals”, Int. J. Environ. Res. Public Health 17(14), (2020), doi: 10.3390/ijerph17145261.
  23.  GWI Coronavirus Research, March 2020 Series 2: Travel & Commuting, GWI Connecting the dots 2021; The biggest COVID- 19 trends that are here to stay. [Online]. Available: https://www.globalwebindex.com [Accessed: 15-Jun-2021].
  24.  “Information society in Poland in 2020”, Central Statistical Office, [Online]. Available: https://stat.gov.pl/obszary-tematyczne/nauka-i- technika-spoleczenstwo-informacyjne/spoleczenstwo-informacyjne/spoleczenstwo-informacyjne-w-polsce-w-2020 -roku,1,14.html [Accessed: 15-Jun-2021], [in Polish].
  25.  “IOCTA 2020”, pp. 6‒7, 13‒17 (2020). [Online]. Available: https://www.europol.europa.eu/sites/default/files/documents/internet_organised_ crime_threat_assessment_iocta_2020.pdf.
  26.  For example in 2013 the Silk Road has been seized: “Ross Ulbricht, the Creator and Owner of the Silk Road Website, Found Guilty in Manhattan Federal Court on All Counts — FBI”. [Online]. Available: https://www.fbi.gov/contact-us/field-offices/newyork/news/press- releases/ross-ulbricht-the-creator-and-owner-of-the-silk-road-website-found-guilty-in-manhattan-federal-court-on-all-counts [Accessed: 15-Jun-2021].
  27.  “Cybercriminals’ favourite VPN taken down in global action”. [Online]. Available: https://www.europol.europa.eu/newsroom/news/ cybercriminals%E2%80%99-favourite-vpn-taken-down-in-global-action [Accessed: 15-Jun-2021].
  28.  “21 arrests in nationwide cyber crackdown”. [Online]. Available: https://www.yorkpress.co.uk/news/18970445.21-arrests-nationwide- crackdown-website-selling-stolen-personal-data/ [Accessed: 15-Jun-2021].
  29.  Recommendation for a Council Decision authorising the opening of negotiations in view of an agreement between the European Union and the United States of America on cross-border access to electronic evidence for judicial cooperation in criminal matters, COM/2019/70 final.
  30.  Proposal for a Regulation of the European Parliament and of the Council on European Production and Preservation Orders for electronic evidence in criminal matters, COM/2018/225 final – 2018/0108 (COD).
  31.  Proposal for a Directive of the European Parliament and of the Council laying down harmonised rules on the appointment of legal representatives for the purpose of gathering evidence in criminal proceedings, COM/2018/226 final – 2018/0107 (COD).
  32.  A. Gryszczyńska, “Acquisition and analysis of data on cybersecurity incidents”, Internet. Data analyst, G. Szpor, Ed., C.H. Beck, Warsaw, 2019, pp. 296‒313, [in Polish].
  33.  “Information society in Poland in 2020”, p. 156, [in Polish].
  34.  “Comparative study on filtering, blocking and take-down of illegal content on the Internet”, Swiss Institute of Comparative Law, 2015, [Online]. Available: https://edoc.coe.int/en/internet/7289-pdf-comparative-study-on-blocking-filtering-and-take-down-of-illegal-internet- content-.html [Accessed: 15-Jun-2021].
  35.  Law of 24 May 2002 on the Internal Security Agency and the Intelligence Agency (Journal of Laws of 2020 item 27 as amended), [in Polish].
  36.  Law of 19 November 2009 on Gambling (Journal of Laws of 2020 item 2094), [in Polish].
  37.  P. Dęba, “Multi-vector protection of Internet users as illustrated by the Orange Cyber Shield”, presented at the 12th Scientific Conference Security in the Internet – Cyber Pandemic, UKSW, Warsaw, Oct. 22‒23, 2020, [in Polish].
  38.  Index of domains. [Online]. Available: https://hole.cert.pl/domains/ [Accessed: 15-Jun-2021].
Go to article

Authors and Affiliations

Agnieszka Gryszczyńska
Download PDF Download RIS Download Bibtex

Abstract

The COVID-19 pandemic has influenced virtually all aspects of our lives. Across the world, countries have applied various mitigation strategies, based on social, political, and technological instruments. We postulate that multi-agent systems can provide a common platform to study (and balance) their essential properties. We also show how to obtain a comprehensive list of the properties by “distilling” them from media snippets. Finally, we present a preliminary take on their formal specification, using ideas from multi-agent logics.
Go to article

Bibliography

  1.  A. Soltani, R. Calo, and C. Bergstrom, “Contacttracing apps are not a solution to the COVID-19 crisis,” Brookings Tech Stream, 27 April 2020. [Online]. Available: https://www.brookings.edu/techstream/inaccurate-and-insecure-why-contact-tracing-apps-could-be-a-disaster/.
  2.  J. Morley, J. Cowls, M. Taddeo, and L. Floridi, “Ethical guidelines for COVID-19 tracing apps,” Nat. Comment, pp. 29–31, 4 June 2020. [Online]. Available: https://www.nature.com/articles/d41586-020-01578-0.
  3.  A. Stollmeyer, M. Schaake, and F. Dignum, “The Dutch tracing app ’soap opera’ – lessons for Europe,” euobserver, 7 May 2020. [Online]. Available: https://euobserver.com/opinion/148265.
  4.  G. Weiss, Ed., Multiagent Systems. A Modern Approach to Distributed Artificial Intelligence. MIT Press: Cambridge, Mass, 1999.
  5.  Y. Shoham and K. Leyton-Brown, Multiagent Systems – Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, 2009.
  6.  A. Rao and M. Georgeff, “Modeling rational agents within a BDI-architecture,” in Proceedings of KR, 1991, pp. 473–484.
  7.  M.Wooldridge, Reasoning about Rational Agents. MIT Press : Cambridge, Mass, 2000.
  8.  M. Dastani, K. Hindriks, and J. Meyer, Eds., Specification and Verification of Multi-Agent Systems. Springer, 2010.
  9.  W. Jamroga, Logical Methods for Specification and Verification of Multi-Agent Systems. ICS PAS, 2015.
  10.  W. Jamroga, D. Mestel, P.B. Rønne, P.Y.A. Ryan, and M. Skrobot, “A survey of requirements for COVID-19 mitigation strategies. Part I: newspaper clips,” CoRR, vol. abs/2011.07887, 2020.
  11.  A. Lomuscio, H. Qu, and F. Raimondi, “MCMAS: An open-source model checker for the verification of multiagent systems,” Int. J. Software Tools Technol. Trans., vol. 19, no. 1, pp. 9–30, 2017.
  12.  G. Behrmann, A. David, and K. Larsen, “A tutorial on UPPAAL,” in Formal Methods for the Design of Real-Time Systems: SFM-RT, ser. LNCS, no. 3185. Springer, 2004, pp. 200–236.
  13.  G. Kant, A. Laarman, J. Meijer, J. van de Pol, S. Blom, and T. van Dijk, “LTSmin: High-performance languageindependent model checking,” in Proceedings of TACAS, ser. Lecture Notes in Computer Science, vol. 9035. Springer, 2015, pp. 692–707.
  14.  D. Kurpiewski, W. Jamroga, and M. Knapik, “STV: Model checking for strategies under imperfect information,” in Proceedings of AAMAS. IFAAMAS, 2019, pp. 2372–2374.
  15.  S. Woodhams, “COVID-19 digital rights tracker,” Top10VPN, 10 June 2020. [Online]. Available: https://www.top10vpn.com/research/ covid-19-digital-rights-tracker/.
  16.  AFP, “Major finding: Lockdowns averted 3 million deaths in 11 European nations: study,” RTL Today, 9 June 2020. [Online]. Available: https://today.rtl.lu/news/science-and-environment/a/ 1530963.html.
  17.  I. Ilves, “Why are Google and Apple dictating how European democracies fight coronavirus?” The Guardian, 16 June 2020. [Online]. Available: https://www.theguardian.com/commentisfree/2020/jun/16/google-apple-dictating-european-democracies-coronavirus.
  18.  “NHS COVID-19: the new contact-tracing app from the NHS,” NCSC, 14 May 2020. [Online]. Available: https://www.ncsc.gov.uk/ information/nhs-covid-19-app-explainer.
  19.  J. Steinhauer and A. Goodnough, “Contact tracing is failing in many states. Here’s why.” New York Times, 5 October 2020. [Online]. Available: https://www.nytimes.com/2020/07/31/health/covid-contact-tracing-tests.html.
  20.  S. Bicheno, “Unlike France, Germany decides to do smartphone contact tracing the Apple/Google way,” telecoms.com, 27 April 2020. [Online]. Available: https://telecoms.com/503931/unlike-france-germany-de cides-to-do-smartphone-contact-tracing-the-apple-goo gle- way/.
  21.  “Together we can fight coronavirus — Smittestopp,” helsenorge, 28 April 2020. [Online]. Available: https://helsenorge.no/coronavirus/ smittestopp?redirect=false.
  22.  P.H. O’Neill, T. Ryan-Mosley, and B. Johnson, “A flood of coronavirus apps are tracking us. now it’s time to keep track of them,” MIT Technol. Rev., 7 May 2020. [Online]. Available: https://www.technologyreview.com/2020/05/07/1000961/launching-mittr-covid-tracing- tracker/.
  23.  M. Zastrow, “Coronavirus contact-tracing apps: can they slow the spread of COVID-19?” Nature (Technol. Feature), 19 May 2020. [Online]. Available: https://www.nature.com/articles/d41586-020-01514-2.
  24.  J. Taylor, “How did the Covidsafe app go from being vital to almost irrelevant?” The Guardian, 23 May 2020. [Online]. Available: https://www.theguardian.com/world/2020/may/24/how-did-the-covidsafe-app-go-from-being-vital-to-almost-irrelevant.
  25.  D. Robertson, “Transparency key to uptake of coronavirus tracing app,” RMIT news, 27-April 2020. [Online]. Available: https://www. rmit.edu.au/news/all-news/2020/april/transparency-key-to-uptake-of-coronavirus-traci ng-app.
  26.  D. Tahir and C. Lima, “Google and Apple’s rules for virus tracking apps sow division among states,” Politico, 10 June 2020. [Online]. Available: https://www.politico.com/news/2020/06/10/google-and-apples-rules-for-virus-tracking-apps-sow-division-among-states-312199.
  27.  A. Clarance, “Aarogya Setu: Why India’s Covid-19 contact tracing app is controversial,” BBC News, 15 May 2020. [Online]. Available: https://www.bbc.com/news/world-asia-india-52659520.
  28.  J. Davies, “UK snubs Google and Apple privacy warning for contact tracing app,” telecoms.com, 28 April 2020. [Online]. Available: https://telecoms.com/503967/uk-s nubs-google-and-apple-privacy-warning-for-contact-tr acing-app/.
  29.  A. Eisenberg, “Privacy fears threaten New York City’s coronavirus tracing efforts,” Politico, 4 June 2020. [Online]. Available: https:// www.politico.com/states/new-york/albany/story/2020/06/04/privacy-fears-threaten-new-york-citys-coronavirus-tracing-efforts-1290657.
  30.  C. Timberg, “Most Americans are not willing or able to use an app tracking coronavirus infections. that’s a problem for Big Tech’s plan to slow the pandemic,” Washington Post, 29 April 2020. [Online]. Available: https://www.washingtonpost.com/technology/2020/04/ 29/ most-americans-are-not-willing-or-able-use-an-app-tracking-coronavirus-infections-thats-problem-big-tec hs-plan-slow-pandemic/.
  31.  M. Burgess, “Just how anonymous is the NHS Covid-19 contact tracing app?” Wired, 12 May 2020. [Online]. Available: https://www. wired.co.uk/article/nhs-covid-app-data-anonymous.
  32.  “Getting it right: States struggle with contact tracing push,” Politico, 17 May 2020. [Online]. Available: https://www.politico.com/ news/2020/05/17/privacy-coronavirus-tracing-261369.
  33.  S.L. Frasier, “Coronavirus antibody tests have a mathematical pitfall,” Sci. Am., 1 July 2020. [Online]. Available: https://www. scientificamerican.com/article/coronavirus-antibody-tests- have-a-mathematical-pitfall/.
  34.  M. Scott and Z. Wanat, “Poland’s coronavirus app offers playbook for other governments,” Politico, 2 April 2020. [Online]. Available: https://www.politico.eu/article/poland-coronavirus-app-offers-playbook-for-other-governments/.
  35.  K. McCarthy, “UK finds itself almost alone with centralized virus contact-tracing app that probably won’t work well, asks for your location, may be illegal,” The Register, 5 May 2020. [Online]. Available: https://www.theregister.com/2020/05/05/uk_coronavirus_app/.
  36.  “Legal advice on smartphone contact tracing published,” matrix chambers, 3 May 2020. [Online]. Available: https://www.matrixlaw.co.uk/ news/legal-advice-on-smartphone-contact-tracing-published/.
  37.  A. Hern, “UK abandons contact-tracing app for Apple and Google model,” The Guardian, 18 June 2020. [Online]. Available: https://www. theguardian.com/world/2020/jun/18/uk-poised-to-abandon-coronavirus-app-in-favour-of-apple-and-google-models.
  38.  “Coronavirus: Member States agree on an interoperability solution for mobile tracing and warning apps,” European Commission – Press release, 16 June 2020. [Online]. Available: https://digital-strategy.ec.europa.eu/en/news/coronavirus-member-states-agree-interoperability- solution-mobile-tracing-and-warning-apps.
  39.  A. Oslo, “Norway suspends virus-tracing app due to privacy concerns,” The Guardian, 15 June 2020. [Online]. Available: https://www. theguardian.com/world/2020/jun/15/norway-suspends-virus-tracing-app-due-to-privacy-concerns.
  40.  S. Wodinsky, “The UK’s contact-tracing app breaks the UK’s own privacy laws (and is just plain broken),” Gizmodo, 13 May 2020. [Online]. Available: https://gizmodo.com/the-uk-s-contact-tracing-app-breaks-the-uk-s-own-privac-1843439962.
  41.  R. Garthwaite and I. Anderson, “Coronavirus: Alarm over ’invasive’ Kuwait and Bahrain contact-tracing apps,” BBC News, 16 June 2020. [Online]. Available: https://www.bbc.com/news/world-middle-east-53052395.
  42.  “Coronavirus privacy: Are South Korea’s alerts too revealing?” BBC News, 5 March 2020. [Online]. Available: https://www.bbc.com/ news/amp/world-asia-51733145.
  43.  K. Szymielewicz, A. Obem, and T. Zieliński, “Jak Polska walczy z koronawirusem i dlaczego aplikacja nas przed nim nie ochroni [How Poland fights the corona, and why the app won’t protect us]?” Panoptykon, 5 May 2020. [Online]. Available: https://panoptykon.org/ protego-safe-ryzyka.
  44.  J.-M. Bezat, “L’application StopCovid, activée seulement par 2% de la population, connaît des débuts décevants,” Le Monde, 10 June 2020. [Online]. Available: https://www.lemonde.fr/pixels/article/2020/06/10/l-application-stopcovid-connait-des-debuts- decevants_6042404_4408996.html.
  45.  P.H. O’Neill, “No, coronavirus apps don’t need 60% adoption to be effective,” MIT Technol. Rev., 5 June 2020. [Online]. Available: https:// www.technologyreview.com/2020/06/05/1002775/covid-apps-effective-at-less-than-60-percent-download/.
  46.  R. Hinch et al., “Effective configurations of a digital contact tracing app: A report to NHSX,” Oxford University, Tech. Rep., 2020. [Online]. Available: https://github.com/BDI-pathogens/covid-19_instant_tracing/blob/master/Report-EffectiveConfigurationsofaDigitalC ontactTracingApp.pdf.
  47.  “Corona-app soll open source werden,” Süddeutsche Zeitung, 6 May 2020. [Online]. Available: https://www.sueddeutsche.de/digital/ corona-app-tracing-open-source-1.4899711.
  48.  “Cybernetica proposes privacy-preserving decentralised architecture for COVID-19 mobile application for Estonia,” Cybernetica, 6 May 2020. [Online]. Available: https://cyber.ee/news/2020/05-06/.
  49.  E. Emerson, “Temporal and modal logic,” in Handbook of Theoretical Computer Science, J. van Leeuwen, Ed. Elsevier, 1990, vol. B, pp. 995–1072.
  50.  R. Fagin, J.Y. Halpern, Y. Moses, and M.Y. Vardi, Reasoning about Knowledge. MIT Press, 1995.
  51.  J. Broersen, M. Dastani, Z. Huang, and L. van der Torre, “The BOID architecture: conflicts between beliefs, obligations, intentions and desires,” in Proceedings of the Fifth International Conference on Autonomous Agents. ACM Press, 2001, pp. 9–16.
  52.  R. Alur, T.A. Henzinger, and O. Kupferman, “Alternating-time Temporal Logic,” J. ACM, vol. 49, pp. 672–713, 2002.
  53.  N. Bulling, V. Goranko, andW. Jamroga, “Logics for reasoning about strategic abilities in multi-player games,” in Models of Strategic Reasoning. Logics, Games, and Communities, ser. Lecture Notes in Computer Science. Springer, 2015, vol. 8972, pp. 93–136.
  54.  F. Laroussinie and P. Schnoebelen, “A hierarchy of temporal logics with past,” Theoretical Computer Science, vol. 148, no. 2, pp. 303–324, 1995.
  55.  W. Penczek and A. Polrola, Advances in Verification of Time Petri Nets and Timed Automata: A Temporal Logic Approach, ser. Studies in Computational Intelligence. Springer, 2006, vol. 20.
  56.  M. Knapik, É. André, L. Petrucci, W. Jamroga, and W. Penczek, “Timed ATL: forget memory, just count,” J. Artif. Intell., vol. 66, pp. 197–223, 2019.
  57.  W. Jamroga, V. Malvone, and A. Murano, “Natural strategic ability,” Artif. Intell., vol. 277, 2019.
  58.  N. Alechina, B. Logan, H. Nguyen, and A. Rakib, “Resource-bounded alternating-time temporal logic,” in Proceedings of International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2010, pp. 481–488.
  59.  N. Bulling and B. Farwer, “Expressing properties of resource-bounded systems: The logics RTL* and RTL,” in Proceedings of CLIMA, ser. Lecture Notes in Computer Science, vol. 6214, 2010, pp. 22–45.
  60.  C. Baier and M. Z. Kwiatkowska, “Model checking for a probabilistic branching time logic with fairness,” Distributed Comput., vol. 11, no. 3, pp. 125–155, 1998.
  61.  T. Chen, V. Forejt, M. Kwiatkowska, D. Parker, and A. Simaitis, “PRISM-games: A model checker for stochastic multi-player games,” in Proceedings of TACAS, ser. Lecture Notes in Computer Science, vol. 7795. Springer, 2013, pp. 185–191.
  62.  M. Kwiatkowska, G. Norman, and D. Parker, “PRISM: probabilistic symbolic model checker,” in Proceedings of TOOLS, ser. Lecture Notes in Computer Science, vol. 2324. Springer, 2002, pp. 200–204.
  63.  N.M. Ferguson et al., “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand,” Imperial College London, Tech. Rep. 9 (16‒03‒2020), 2020.
  64.  B. Adamik et al., “Estimation of the severeness rate, death rate, household attack rate and the total number of COVID-19 cases based on 16 115 Polish surveillance records,” Prepr. Lancet, 2020.
  65.  W. Bock et al., “Mitigation and herd immunity strategy for COVID-19 is likely to fail,” medRxiv, 2020.
  66.  R. McCabe et al., “Modelling ICU capacity under different epidemiological scenarios of the COVID-19 pandemic in three western European countries,” Imperial College London, Tech. Rep. 36 (16‒11‒2020), 2020.
  67.  S. Zionts, “A multiple criteria method for choosing among discrete alternatives,” Eur. J. Oper. Res., vol. 7, no. 2, pp. 143–147, 1981, fourth EURO III Special Issue.
  68.  Y. Collette and P. Siarry, Multiobjective Optimization: Principles and Case Studies. Springer, 2004.
  69.  R. Radulescu, P. Mannion, D. M. Roijers, and A. Nowé, “Multi-objective multi-agent decision making: a utilitybased analysis and survey,” Auton. Agents Multi-Agent Syst., vol. 34, no. 1, p. 10, 2020.
Go to article

Authors and Affiliations

Wojciech Jamroga
1 2
David Mestel
1
Peter B. Roenne
1
Peter Y.A. Ryan
1
Marjan Skrobot
1

  1. Interdisciplinary Centre on Security, Reliability and Trust, SnT, University of Luxembourg
  2. Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

In the paper, we are analyzing and proposing an improvement to current tools and solutions for supporting fighting with COVID-19. We analyzed the most popular anti-covid tools and COVID prediction models. We addressed issues of secure data collection, prediction accuracy based on COVID models. What is most important, we proposed a solution for improving the prediction and contract tracing element in these applications. The proof of concept solution to support the fight against a global pandemic is presented, and the future possibilities for its development are discussed.
Go to article

Bibliography

  1.  V. Chamola, V. Hassija, V. Gupta, and M. Guizani, “A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact,” IEEE Access, vol. 8, pp. 90225–90265, 2020.
  2.  J. Stanley and J.S. Granick, “Aclu white paper: The limits of location tracking in an epidemic,” Am. Civ. Lib. Union, April 2020.
  3.  G. Dartmann, H. Song, and A. Schmeink, Big data analytics for cyber-physical systems: machine learning for the internet of things. Elsevier, 2019.
  4.  M. Zastrow, “South korea is reporting intimate details of COVID-19 cases: has it helped?” Nature, March 2020.
  5.  Ł. Apiecionek, J. Czerniak, M. Romantowski, D. Ewald, B. Tsizh, H. Zarzycki, and W. Dobrosielski, “Authentication over internet protocol,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, 2020.
  6.  E. Talhi, J.-C. Huet, V. Fortineau, and S. Lamouri, “A methodology for cloud manufacturing architecture in the context of Industry 4.0,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 271‒284, 2020.
  7.  L. Madeyski, T. Lewowski, and B. Kitchenham, “OECD Recommendation’s draft concerning access to research data from public funding: A review,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e135401, 2021.
  8.  A.I. Abubakar, K.G. Omeke, M. Öztürk, S. Hussain, and M.A. Imran, “The role of artificial intelligence driven 5G networks in COVID-19 outbreak: opportunities, challenges, and future outlook,” Front. Commun. Networks, vol. 1, p. 575065, 2020.
  9.  D. Ingram and J. Ward, “Behind the global efforts to make a privacy-first coronavirus tracking app,” NBC News, April 2020.
  10.  “Google API for exposure notifications,” [Online]. Available: https://developers.google.com/android/exposure-notifications. Accessed: 2020-11-06.
  11.  “COVID-19 screening tool website, apple website,” [Online]. Available: https://covid19.apple.com/screening/. Accessed: 2020-11-07.
  12.  “Center for disease control and prevention COVID-19 health bot website,” [Online]. Available: https://www.cdc.gov/code.html. Accessed: 2020-11-07.
  13.  “Project decode – data driven covid detection,” [Online]. Available: https://antycovid.ipipan.waw.pl/slides/p1130-3-Gruca.pdf. Accessed: 2020-11-07.
  14.  C. Menni, A. Valdes, and M. Freidin, “Real-time tracking of self-reported symptoms to predict potential COVID-19,” Nat. Med., April 2020.
  15.  D. Hauser, A.O. Obeng, K. Fei, M.A. Ramos, and C.R. Horowitz, “Views of primary care providers on testing patients for genetic risks for common chronic diseases,” Health Aff., vol. 37, no. 5, pp. 793–800, 2018.
  16.  T. Alashoor, S. Han, and R.C. Joseph, “Familiarity with big data, privacy concerns, and self-disclosure accuracy in social networking websites: an apco model,” Commun. Assoc. Inf. Syst., vol. 41, no. 1, p. 4, 2017.
  17.  M. Macenaite, “From universal towards child-specific protection of the right to privacy online: Dilemmas in the EU General Data Protection Regulation,” New Media Soc., vol. 19, no. 5, pp. 765–779, 2017.
  18.  N. Crepaz, T. Tang, G. Marks, M.J. Mugavero, L. Espinoza, and H.I. Hall, “Durable viral suppression and transmission risk potential among persons with diagnosed hiv infection: United states, 2012–2013,” Clin. Infect. Dis., vol. 63, no. 7, pp. 976– 983, 2016.
  19.  A.A. Hussain, O. Bouachir, F. Al-Turjman, and M. Aloqaily, “AI techniques for COVID-19,” IEEE Access, vol. 8, pp. 128776‒128795, 2020.
  20.  R. Vaishya, M. Javaid, I.H. Khan, and A. Haleem, “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr.: Clin. Res. Rev., 2020.
  21.  H. Dai and B. Zhao, “Association of the infection probability of COVID-19 with ventilation rates in confined spaces,” in Build. Simul., vol. 13. Springer, 2020, pp. 1321–1327.
  22.  J. Xie, Z. Tong, X. Guan, B. Du, H. Qiu, and A. S. Slutsky, “Critical care crisis and some recommendations during the COVID-19 epidemic in china,” Intensive Care Med., pp. 1‒4, 2020.
  23.  C. Prunean, “System and method for amplitude pre-distortion optimization for GPS signal constant envelope transmission,” Oct. 1 2019, US Patent 10,432,447.
  24.  P. Zhou, Y. Zheng, Z. Li, M. Li, and G. Shen, “IODetector: a generic service for indoor outdoor detection,” in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, ser. SenSys ’12. New York, NY, USA: Association for Computing Machinery, 2012, pp. 361–362. [Online]. Available: https://doi.org/10.1145/2426656.2426709.
  25.  J.A. Quiña-Mera, E.R. Saransig-Perugachi, D.J. Trejo- España, M.E. Naranjo-Toro, and C.P. Guevara-Vega, “Automation of the barter exchange management in ecuador applying Google V3 API for geolocation,” in International Conference on Information Technology & Systems. Springer, 2019, pp. 210‒219.
  26.  N. Ahmed, R.A. Michelin, W. Xue, S. Ruj, R. Malaney, S.S. Kanhere, A. Seneviratne, W. Hu, H. Janicke, and S.K. Jha, “A survey of COVID-19 contact tracing apps,” IEEE Access, vol. 8, pp. 134577–134601, 2020.
  27.  S. Jeschke, C. Brecher, H. Song, and D. Rawat, “Industrial internet of things: Foundations, principles and applications,” Cham, Switzerland: Springer, pp. 1–715, 2017.
  28.  “Emf explained 2.0,” [Online]. Available: http://www.emfexplained.info/pol/?id=25916. Accessed: 2020-12-02.
  29.  Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of things and big data analytics for smart and connected communities,” IEEE Access, vol. 4, pp. 766–773, 2016.
  30.  J. Zhang, E. Björnson, M. Matthaiou, D.W.K. Ng, H. Yang, and D.J. Love, “Prospective multiple antenna technologies for beyond 5G,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1637–1660, 2020.
  31.  Y. Siriwardhana, C. De Alwis, G. Gür, M. Ylianttila, and M. Liyanage, “The fight against the COVID-19 pandemic with 5G technologies,” IEEE Eng. Manage. Rev., vol. 48, no. 3, pp. 72–84, 2020.
  32.  K. Shafique, B.A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios,” IEEE Access, vol. 8, pp. 23022–23040, 2020.
  33.  M. Otoom, N. Otoum, M. A. Alzubaidi, Y. Etoom, and R. Banihani, “An iot-based framework for early identification and monitoring of COVID-19 cases,” Biomed. Signal Process. Control, vol. 62, p. 102149, 2020.
  34.  S. Jaafari, A. Alhasani, S.M. Almutairi et al., “Certain investigations on iot system for COVID-19,” in 2020 International Conference on Computing and Information Technology (ICCIT- 1441). IEEE, 2020, pp. 1–4.
Go to article

Authors and Affiliations

Martyna Gruda
1
Michal Kedziora
1

  1. Wroclaw University of Science and Technology, ul. Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wroclaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

We analyze the Google-Apple exposure notification mechanism designed by the Apple-Google consortium and deployed on a large number of Corona-warn apps. At the time of designing it, the most important issue was time-to-market and strict compliance with the privacy protection rules of GDPR. This resulted in a plain but elegant scheme with a high level of privacy protection. In this paper we go into details and propose some extensions of the original design addressing practical issues. Firstly, we point to the danger of a malicious cryptographic random number generator (CRNG) and resulting possibility of unrestricted user tracing. We propose an update that enables verification of unlinkability of pseudonymous identifiers directly by the user. Secondly, we show how to solve the problem of verifying the “same household” situation justifying exempts from distancing rules. We present a solution with MIN-sketches based on rolling proximity identifiers from the Apple-Google scheme. Thirdly, we examine the strategies for revealing temporary exposure keys. We have detected some unexpected phenomena regarding the number of keys for unbalanced binary trees of a small size. These observations may be used in case that the size of the lists of diagnosis keys has to be optimized.
Go to article

Bibliography

  1. Ministry of Health and Government Technology Agency (GovTech), Trace Together Programme, [Online]. Available: https://www. tracetogether.gov.sg.
  2. The European Parliament and the Council of the European Union: Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/ec (General Data Protection Regulation). Official Journal of the European Union, L119.1, 4.5.2016.
  3. Corona-Warn-App Consortium, [Online]. Available: https://www.coronawarn.app/en/.
  4. C. Troncoso et. al, “Decentralized Privacy-Preserving Proximity Tracing,” [Online]. Available: https://github.com/DP-3T/documents/ blob/master/DP3T%20White%20Paper.pdf.
  5. Apple & Google, “Exposure Notification Cryptography Specification,” [Online]. Available: https://covid19-static.cdn-apple.com/ applications/covid19/current/static/contact-tracing/pdf/ExposureNotification-CryptographySpecificationv1.2.pdf?1.
  6. D. Shumow and N. Ferguson, “On the Possibility of a Back Door in the NIST SP800-90 Dual Ec Prng,” [Online]. Available: http:// rump2007.cr.yp.to/15-shumow.pdf.
  7. V. Goyal, A. O’Neill, and V. Rao, “Correlated-input secure hash functions,” Theory of Cryptography Conference (TCC), 2011, pp. 182‒200.
  8. A.Z. Broder, “On the resemblance and containment of documents,” Proceedings. Compression and Complexity of SEQUENCES 1997, Italy, 1997, pp. 21‒29.
Go to article

Authors and Affiliations

Adam Bobowski
1
Jacek Cichoń
1
ORCID: ORCID
Mirosław Kutyłowski
1
ORCID: ORCID

  1. Wrocław University of Science and Technology, Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wrocław, Poland
Download PDF Download RIS Download Bibtex

Abstract

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.
Go to article

Bibliography

  1.  L.D. Maxim, R. Niebo, and M.J. Utell, “Screening tests: a review with examples”, Inhal. Toxicol., vol. 26, no. 13, pp. 811–828, 2014.
  2.  D. Ardila et al., “End-to-end lung cancer screening with threedimensional deep learning on low-dose chest computed tomography”, Nat. Med., vol. 25, no. 6, pp. 954–961, 2019.
  3.  R. Landy, F. Pesola, A. Castañón, and P. Sasieni, “Impact of cervical screening on cervical cancer mortality: estimation using stage-specific results from a nested case–control study”, Br. J. Cancer, vol. 115, no. 9, pp. 1140–1146, 2016.
  4.  E.F. Conant et al., “Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the prospr consortium”, Breast Cancer Res. Treat., vol. 156, no. 1, pp. 109–116, 2016.
  5.  K. Gostic, A.C. Gomez, R.O. Mummah, A.J. Kucharski, and J.O. Lloyd-Smith, “Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19”, eLife 9, vol. 9, p. e55570, 2020.
  6.  L. Wynants et al., “Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal”, BMJ, vol. 369, p. m1328, 2020.
  7.  A. Callahan et al., “Estimating the efficacy of symptom-based screening for COVID-19”, NPJ Digit. Med., vol. 3, no. 1, pp. 1–3, 2020.
  8.  R. Trevethan, “Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice”, Front. Public Health, vol. 5, p. 307, 2017.
  9.  J. Henzel et al., “Classification supporting COVID-19 diagnostics based on patient survey data”, arXiv:2011.12247, 2020.
  10.  H. Swapnarekha, H.S. Behera, J. Nayak, and B. Naik, “Role of intelligent computing in COVID-19 prognosis: A state-of-theart review”, Chaos Solitons Fractals, vol. 138, p. 109947, 2020.
  11.  S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for COVID-19 (SARS- CoV-2) pandemic: A review”, Chaos Solitons Fractals, vol. 139, p. 110059, 2020.
  12.  Y. Mohamadou, A. Halidou, and P.T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19”, Appl. Intell., vol. 50, no. 11, pp. 3913–3925, 2020.
  13.  A. Banerjee et al., “Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population”, Int. Immunopharmacol., vol. 86, p. 106705, 2020.
  14.  T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with x-ray images”, Comput. Biol. Med., vol. 121, p. 103792, 2020.
  15.  R.M. Pereira, D. Bertolini, L.O. Teixeira, C.N. Silla, and Y.M. Costa, “COVID-19 identification in chest x-ray images on flat and hierarchical classification scenarios”, Comput. Meth. Programs Biomed., vol. 194, p. 105532, 2020.
  16.  D. Singh, V. Kumar, V. Yadav, and M. Kaur, “Deep neural network-based screening model for COVID-19-infected patients using chest x-ray images”, Int. J. Pattern Recogn. Artif. Intell., vol. 25, no. 3, p. 2151004, 2021.
  17.  A.G. Wintjens et al., “Applying the electronic nose for preoperative SARS-CoV-2 screening”, Surg. Endosc., 2020, doi: 10.1007/s00464- 020-08169-0.
  18.  J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 artificial intelligence diagnosis using only cough recordings”, IEEE Eng. Med. Biol. Mag., vol. 1, pp. 275‒281, 2020.
  19.  P. Bagad et al., “Cough against covid: Evidence of covid-19 signature in cough sounds”, arXiv:2009.08790 (2020).
  20.  C. Feng et al., “A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics”, medRxiv 2020.03.19.20039099.
  21.  D. Brinati, A. Campagner, D. Ferrari, M. Locatelli, G. Banfi, and F. Cabitza, “Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study”, J. Med. Syst., vol. 44, p. 135, 2020.
  22.  “Suspected COVID-19 pneumonia diagnosis aid system”, [Online]. Available: https://intensivecare.shinyapps.io/COVID19/, (Accessed on 28/12/2020).
  23.  “ML-based COVID-19 test from routine blood test”, [Online]. Available: https://covid19-blood-ml.herokuapp.com/, (Accessed on 28/12/2020).
  24.  Symptomate, “Symptomate COVID-19 risk assessment tool”, [Online]. Available: https://symptomate.com/covid19/checkup, (Accessed on 12/28/2020).
  25.  CDC, “Testing for COVID-19”, [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html, (Accessed on 28/12/2020).
  26.  Apple Inc., “Apple covid-19”, [Online]. Available: https://covid19.apple.com/screening, (Accessed on 28/12/2020).
  27.  “COVID-19 Risk Assessment”, [Online]. Available: https://covid.preflet.com/en, (Accessed on 28/12/2020).
  28.  M. DataLab, M. Groups, “COVID-19 risk calculator”. [Online]. Available: https://crs19.pl/ (Accessed on 28/12/2020).
  29.  P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed. Chapman & Hall, 1989.
  30.  T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
  31.  “Open source machine learning platform”. [Online]. Available: https://www.h2o.ai/, (Accessed on 20/12/2020).
  32.  “Data drivEn COVID19 DEtection with machine learning”). [Online]. Available: https://decode.polsl.pl (Accessed on 30/12/2020).
Go to article

Authors and Affiliations

Michał Kozielski
1
ORCID: ORCID
Joanna Henzel
1
ORCID: ORCID
Joanna Tobiasz
2
ORCID: ORCID
Aleksandra Gruca
1
Paweł Foszner
3
ORCID: ORCID
Joanna Zyla
2
ORCID: ORCID
Małgorzata Bach
4
Aleksandra Werner
4
ORCID: ORCID
Jerzy Jaroszewicz
5
Joanna Polańska
2
ORCID: ORCID
Marek Sikora
1
ORCID: ORCID

  1. Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  2. Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
  3. Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
  4. Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
  5. Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland
Download PDF Download RIS Download Bibtex

Abstract

The ongoing period of the pandemic makes everybody focused on the matters related to fighting this immense problem posed to the societies worldwide. The governments deal with the threat by publishing regulations which should allow to mitigate the pandemic, walking on thin ice as the decision makers do not always know how to properly respond to the threat in order to save people. Computer-based simulations of e.g. parts of the city or rural area should provide significant help, however, there are some requirements to fulfill. The simulation should be verifiable, supported by the urban research and it should be possible to run it in appropriate scale. Thus in this paper we present an interdisciplinary work of urban researchers and computer scientists, proposing a scalable, HPC-grade model of simulation, which was tested in a real scenario and may be further used to extend our knowledge about epidemic spread and the results of its counteracting methods. The paper shows the relevant state of the art, discusses the micro-scale simulation model, sketches out the elements of its implementation and provides tangible results gathered for a part of the city of Krakow, Poland.
Go to article

Bibliography

  1.  I. Mironowicz, Modele transformacji miast. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2016.
  2.  A. Matusik, K. Racoń-Leja, M. Gyurkovich, and K. Dudzic-Gyurkovich, “Hydrourban spatial development model for a resilient inner-city. the example of gdańsk,” Archit. City Environ., vol. 15, no. 43, pp. 1–2, 2020.
  3.  J.L. Kriken, P. Enquist, and R. Rapaport, City building: nine planning principles for the twenty-first century. Princeton Architectural Press, 2011.
  4.  W. Kosiński, Paradigm of the City of the 21st Century. Between the Past of the Polis and the Future of the Metropolis, J. Gyurkovich, Ed. Kraków: Wydaw. PK, 2016.
  5.  J.F.P. Rose, The well-tempered city: what modern science, ancient civilizations, and human nature teach us about the future of urban life. Harper Wave, 2017.
  6.  E. Rewers, Post-Polis. Wstęp do filozofii ponowoczesnego miasta. Kraków: Universitas, 2005, [in Polish].
  7.  M. Dymnicka, Przestrzeń publiczna, a przemiany miasta. Warszawa: Wydawnictwo Naukowe Scholar, 2013, [in Polish].
  8.  M. Gyurkovich et al., Hybrid Urban Structures, M. Gyurkovich, Ed. Kraków: Wydaw. PK, 2016.
  9.  S. Kostof, The City Shaped.Urban Patterns and Meanings through History. London – New York: Thames & Hudson, 1999.
  10.  A.A. Kantarek, Tkanka urbanistyczna.Wybrane zagadnienia, J. Gyurkovich, Ed. Kraków: Wydaw. PK, 2019, [in Polish].
  11.  A. Noworól, “Functional urban area as the city of the future,” Tech. Trans., vol. 111, no. 1-A, 2014.
  12.  K. Racoń-Leja, Miasto i wojna: wpływ II wojny światowej na przekształcenia struktury przestrzennej i współczesną kondycję urbanistyczną wybranych miast europejskich, J. Gyurkovich, Ed. Kraków: Wydaw. PK, 2019, [in Polish].
  13.  J. Teller, “Urban density and covid-19: towards an adaptive approach,” Build. Cities, vol. 2, no. 1, pp. 150–165, 2021.
  14.  C. at Johns Hopkins University, “Covid-19 dashboard by the center for systems science and engineering,” 2021, [Online] Available: https:// coronavirus.jhu.edu/map.html.
  15.  M. Castells, “Communication, power and counter-power in the network society,” Int. J. Commun., vol. 1, no. 1, p. 29, 2007.
  16.  R. Sennet, “How should we live? density in postpandemic cities,” Domus, no. 1046, 2020, [Online]. Available: https://www.domusweb. it/en/architecture/2020/05/09/how-should-we-live-density-in-post-pandemic-cities.html.
  17.  M. Kowicki, Rozproszenie zabudowy na obszarach Małopolski, a kryzys kreatywności opracowań planistyczno-przestrzennych. Kraków: Wydaw. PK, 2014, [in Polish].
  18.  G. Korzeniak et al., Małe i średnie miasta w policentrycznym rozwoju Polski. Kraków: Instytut Rozwoju Miast, 2014, [in Polish].
  19.  GUS, “Demographic Yearbook of Poland,” 2019.
  20.  N.A. Salingaros, “Eight city types and their interactions: the “eight-fold” model,” Techn. Trans., vol. 2, pp. 5–70, 2017.
  21.  J. Busquets and M. Corominas, Cerda and the Barcelona of the future: reality versus project. Centre de Cultura Contemporania de Barcelona, 2009.
  22.  A.A. Kantarek, K. Kwiatkowski, and I. Samuels, “From rural plots to urban superblocks,” Urban Morphology: journal of the International Seminar on Urban Form, vol. 22, no. 2, pp. 155–157, 2018.
  23.  M. Gyurkovich and A. Sotoca, “Towards the Cracow Metropolis – a dream or a reality? A selected issues,” Tech. Trans., vol. 115, no. 2, pp. 5–25, 2018.
  24.  P. Lorens, Równoważenie rozwoju przestrzennego miast polskich. Gdańsk: Wydaw. PG, 2013, [in Polish].
  25. Back to the Sense of the City: 11th VCT International monograph book, Year 2016, July, Krakow. Barcelona: Centre of Land Policy and Valuations (CPSV), 2016.
  26.  A. Zwoliński, “Geometrical structure of public spaces in virtual city models. exploring urban morphology by hierarchy of open spaces,” Space Form, vol. 2019, no. 37, pp. 235–243, 2019.
  27.  K. Lynch, Good city form. MIT Press, 2001.
  28.  D.C. Duives, W. Daamen, and S.P. Hoogendoorn, “State-ofthe-art crowd motion simulation models,” Transp. Res. Part C Emerging Technol., vol. 37, pp. 193–209, 2013.
  29.  E.D. Kuligowski, “Computer evacuation models for buildings,” in SFPE Handbook of Fire Protection Engineering. Springer, 2016, pp. 2152–2180.
  30.  B. Zhan, D.N. Monekosso, P. Remagnino, S.A. Velastin, and L.-Q.Xu, “Crowd analysis: a survey,” Mach. Vision Appl., vol. 19, no. 5‒6, pp. 345–357, 2008.
  31.  K. Teknomo, Y. Takeyama, and H. Inamura, “Review on microscopic pedestrian simulation model,” CoRR, vol. abs/1609.01808, 2016. [Online]. Available: http://arxiv.org/abs/1609.01808.
  32.  M. Paciorek, A. Bogacz, and W. Turek, “Scalable signal-based simulation of autonomous beings in complex environments,” in Computational Science – ICCS 2020. Cham: Springer International Publishing, 2020, pp. 144–157.
  33.  J. Wąs and R. Lubaś, “Towards realistic and effective agentbased models of crowd dynamics,” Neurocomputing, vol. 146, pp. 199–209, 2014.
  34.  P. Wittek and X. Rubio-Campillo, “Scalable agent-based modelling with cloud hpc resources for social simulations,” in 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings. IEEE, 2012, pp. 355–362.
  35.  J. Bujas, D. Dworak, W. Turek, and A. Byrski, “Highperformance computing framework with desynchronized information propagation for large-scale simulations,” J. Comput. Sci, vol. 32, pp. 70–86, 2019.
  36.  Y. Mohamadou, A. Halidou, and P.T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of covid-19,” Appl. Intell, vol. 50, no. 11, pp. 3913–3925, 2020.
  37.  M. Fuentes and M. Kuperman, “Cellular automata and epidemiological models with spatial dependence,” Physica A, vol. 267, no. 3, pp. 471‒486, 1999.
  38.  I. Tiwari, P. Sarin, and P. Parmananda, “Predictive modeling of disease propagation in a mobile, connected community using cellular automata,” Chaos: Interdiscip. J. Nonlinear Sci., vol. 30, no. 8, p. 081103, 2020.
  39.  M. Dascalu, M. Malita, A. Barbilian, E. Franti, and G.M. Stefan, “Enhanced cellular automata with autonomous agents for covid-19 pandemic modeling,” Rom. J. Inf. Sci. Technol, vol. 23, pp. S15–S27, 2020.
  40.  Y. Xiao, M. Yang, Z. Zhu, H. Yang, L. Zhang, and S. Ghader, “Modeling indoor-level non-pharmaceutical interventions during the covid-19 pandemic: a pedestrian dynamics-based microscopic simulation approach,” Transp. Policy, vol. 109, pp. 12–23, 2021.
  41.  T. Kapecki, “Elements of sustainable development in the context of the environmental and financial crisis and the covid-19 pandemic,” Sustainability, vol. 12, no. 15, pp. 1–12, 2020.
  42.  A. Jasiński, “Public space or safe space–remarks during the covid-19 pandemic,” Tech. Trans., vol. 117, no. 1, 2020.
  43.  S. Gzell, “Urban design and the sense of the city,” Tech. Trans., vol. 113, no. 2-A, pp. 15–19, 2016.
  44.  M. Hanzl, “Urban forms and green infrastructure–the implications for public health during the covid-19 pandemic,” Cities Health, pp. 1–5, 2020, doi: 10.1080/23748834.2020.1791441.
  45.  M.D. Pinheiro and N.C. Luís, “Covid-19 could leverage a sustainable built environment,” Sustainability, vol. 12, no. 14, p. 5863, 2020.
  46.  M.R. Fatmi, “Covid-19 impact on urban mobility,” J. Urban Manage., vol. 9, no. 3, pp. 270–275, 2020.
  47.  A. Porębska, P. Rizzi, S. Otsuki, and M. Shirotsuki, “Walkability and resilience: A qualitative approach to design for risk reduction,” Sustainability, vol. 11, no. 10, p. 2878, 2019.
  48.  F. Vergara Perucich, J. Correa Parra, and C. Aguirre-Nuñez, Atlas de indicadores espaciales de vulnerabilidad ante el covid-19 en Chile, F. Vergara, Ed. Centro Producción del Espacio, 2020.
  49.  W.H. Whyte et al., The social life of small urban spaces. Conservation Foundation Washington, DC, 1980.
  50.  A. Białkiewicz, B. Stelmach, and M.J. Żychowska, “Dobra kultury współczesnej. zarys problemu ochrony,” Wiadomości Konserwatorskie – J. Heritage Conserv., no. 63, pp. 152–162, 2020, [in Polish].
  51.  E. Szczerek, Rewitalizacja osiedli wielkopłytowych a ciągłośc´ i komplementarność przestrzeni publicznej miasta, A. Franta, Ed. Kraków: Wydaw. PK, 2018, [in Polish].
  52.  B. Malinowska-Petelenz, Sacrum in civitas: wybrane zagadnienia, A.A. Kantarek, Ed. Kraków: Wydaw. PK, 2018, [in Polish].
  53.  J. Gehl and B. Svarre, How to study public life. Washington, DC: Island press, 2013.
Go to article

Authors and Affiliations

Mateusz Paciorek
1
ORCID: ORCID
Damian Poklewski-Koziełł
2
ORCID: ORCID
Kinga Racoń-Leja
2
ORCID: ORCID
Aleksander Byrski
1
ORCID: ORCID
Mateusz Gyurkovich
2
ORCID: ORCID
Wojciech Turek
1
ORCID: ORCID

  1. AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Krakow, Poland
  2. Cracow University of Technology, ul. Warszawska 24, 31-155 Krakow, Poland
Download PDF Download RIS Download Bibtex

Abstract

In times of the COVID-19, reliable tools to simulate the airborne pathogens causing the infection are extremely important to enable the testing of various preventive methods. Advection-diffusion simulations can model the propagation of pathogens in the air. We can represent the concentration of pathogens in the air by “contamination” propagating from the source, by the mechanisms of advection (representing air movement) and diffusion (representing the spontaneous propagation of pathogen particles in the air). The three-dimensional time-dependent advection-diffusion equation is difficult to simulate due to the high computational cost and instabilities of the numerical methods. In this paper, we present alternating directions implicit isogeometric analysis simulations of the three-dimensional advection-diffusion equations. We introduce three intermediate time steps, where in the differential operator, we separate the derivatives concerning particular spatial directions. We provide a mathematical analysis of the numerical stability of the method. We show well-posedness of each time step formulation, under the assumption of a particular time step size. We utilize the tensor products of one-dimensional B-spline basis functions over the three-dimensional cube shape domain for the spatial discretization. The alternating direction solver is implemented in C++ and parallelized using the GALOIS framework for multi-core processors. We run the simulations within 120 minutes on a laptop equipped with i7 6700 Q processor 2.6 GHz (8 cores with HT) and 16 GB of RAM.
Go to article

Bibliography

  1.  “Coronavirus disease (COVID-19): How is it transmitted?”. [Online] Available: https://www.who.int/emergencies/diseases/novel- coronavirus-2019/question-and-answers-hub/q-a-detail/q-a-how-is-covid-19-transmitted.
  2.  D.W. Peaceman and H.H. Rachford Jr., “The numerical solution of parabolic and elliptic differential equations’’, J. Soc. Ind. Appl. Math., vol. 3, no. 1, pp. 28‒41, 1955.
  3.  J. Douglasand and H. Rachford, “On the numerical solution of heat conduction problems in two and three space variables’’, Trans. Am. Math. Soc., vol. 82, no. 2, pp. 421‒439, 1956.
  4.  E.L. Wachspress and G. Habetler, “An alternating-direction-implicit iteration technique’’, J. Soc. Ind. Appl. Math., vol. 8, no. 2, pp. 403‒423, 1960.
  5.  G. Birkhoff, R.S. Varga, and D. Young, “Alternating direction implicit methods’’, Adv. Comput., vol. 3, pp. 189‒273, 1962.
  6.  J.L. Guermond and P. Minev, “A new class of fractional step techniques for the incompressible Navier-Stokes equations using direction splitting’’, C.R. Math., vol. 348, pp. 581‒585, 2010.
  7.  J.L. Guermond, P. Minev, and J. Shen, “An overview of projection methods for incompressible flows’’, Comput. Methods Appl. Mech. Eng., vol. 195, pp. 6011‒6054, 2006.
  8.  J.A. Cottrell, T. J. R. Hughes, and Y. Bazilevs, Isogeometric Analysis: Toward Unification of CAD and FEA, John Wiley and Sons, 2009.
  9.  M.-C. Hsu, I. Akkerman, and Y. Bazilevs, “High-performance computing of wind turbine aerodynamics using isogeometric analysis’’, Comput. Fluids, vol. 49, pp. 93‒100, 2011.
  10.  K. Chang, T.J.R. Hughes, and V.M. Calo, “Isogeometric variational multiscale large-eddy simulation of fully-developed turbulent flow over a wavy wall’’, Comput. Fluids, vol. 68, pp. 94‒104, 2012.
  11.  L. Dedè, T.J.R. Hughes, S. Lipton, and V.M. Calo, “Structural topology optimization with isogeometric analysis in a phase field approach’’, USNCTAM2010, 16th US National Congree of Theoretical and Applied Mechanics, 2010.
  12.  L. Dedè, M.J. Borden, and T.J.R. Hughes, “Isogeometric analysis for topology optimization with a phase field model’’, Arch. Comput. Methods Eng., vol. 19, pp. 427‒465, 2012.
  13.  H. Gómez, V.M. Calo, Y. Bazilevs, and T.J.R. Hughes, “Isogeometric analysis of the {Cahn-Hilliard} phase-field model’’, Comput. Methods Appl. Mech. Eng., vol. 197, pp. 4333‒4352, 2008.
  14.  H. Gómez, T.J.R. Hughes, X. Nogueira, and V.M. Calo, “Isogeometric analysis of the isothermal Navier-Stokes-Korteweg equations’’, Comput. Methods Appl. Mech. Eng., vol. 199, pp. 1828‒1840, 2010.
  15.  R. Duddu, L. Lavier, T.J.R. Hughes, and V.M. Calo, “A finite strain Eulerian formulation for compressible and nearly incompressible hyper-elasticity using high-order NURBS elements’’, Int. J. Numer. Methods Eng., vol. 89, pp. 762‒785, 2012.
  16.  S. Hossain, S.F.A. Hossainy, Y. Bazilevs, V.M. Calo, and T.J.R. Hughes, “Mathematical modeling of coupled drug and drug-encapsulated nanoparticle transport in patient-specific coronary artery walls’’, Comput. Mech., vol. 49, pp. 213‒242, 2012.
  17.  Y. Bazilevs, V.M. Calo, Y. Zhang, and T.J.R. Hughes, “Isogeometric fluid-structure interaction analysis with applications to arterial blood flow’’, Comput. Mech., vol. 38, pp. 310‒322, 2006.
  18.  Y. Bazilevs, V.M. Calo, J.A. Cottrell, T.J.R. Hughes, A. Reali, and G. Scovazzi, “Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows’’, Comput. Methods Appl. Mech. Eng., vol. 197, pp. 173‒201, 2007.
  19.  V.M. Calo, N. Brasher, Y. Bazilevs, and T.J.R. Hughes, “Multiphysics Model for Blood Flow and Drug Transport with Application to Patient-Specific Coronary Artery Flow’’, Comput. Mech., vol. 43, pp. 161‒177, 2008.
  20.  M. Łoś, M. Paszyński, A. Kłusek, and W. Dzwinel, “Application of fast isogeometric L2 projection solver for tumor growth simulations’’, Comput. Methods Appl. Mech. Eng., vol. 316, pp. 1257‒1269, 2017.
  21.  M. Łoś, A. Kłusek, M. Amber Hassam, K. Pingali, W. Dzwinel, and M. Paszyński, “Parallel fast isogeometric L2 projection solver with GALOIS system for 3D tumor growth simulations’’, Comput. Methods Appl. Mech. Eng., vol. 343, pp. 1‒22, 2019.
  22.  A. Paszyńska, K. Jopek. M. Woźniak, and M. Paszyński, “Heuristic algorithm to predict the location of C0 separators for efficient isogeometric analysis simulations with direct solvers’’, Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 6, pp. 907‒917, 2018.
  23.  L. Gao and V.M. Calo, “Fast Isogeometric Solvers for Explicit Dynamics’’, Comput. Methods Appl. Mech. Eng., vol. 274, pp. 19‒41, 2014.
  24.  L. Gao and V.M. Calo, “Preconditioners based on the alternating-direction-implicit algorithm for the 2D steady-state diffusion equation with orthotropic heterogeneous coefficients’’, J. Comput. Appl. Math., vol. 273, pp. 274‒295, 2015.
  25.  L. Gao, “Kronecker Products on Preconditioning’’, PhD. Thesis, King Abdullah University of Science and Technology, 2013.
  26.  M. Łoś, M. Woźniak, M. Paszyński, L. Dalcin, and V.M. Calo, “Dynamics with Matrices Possessing Kronecker Product Structure’’, Procedia Comput. Sci., vol. 51, pp. 286‒295, 2015.
  27.  M. Woźniak, M. Łoś, M. Paszyński, L. Dalcin, and V. Calo, “Parallel fast isogeometric solvers for explicit dynamics’’, Comput. Inform., vol. 36, no. 2, pp. 423‒448, 2017.
  28.  M. Łoś, M. Woźniak, M. Paszyński, A. Lenharth, and K. Pingali, “IGA-ADS : Isogeometric Analysis FEM using ADS solver’’, Comput. Phys. Commun., vol. 217, pp. 99‒116, 2017.
  29.  G. Gurgul, M. Woźniak, M. Łoś, D. Szeliga, and M. Paszyński, “Open source JAVA implementation of the parallel multi-thread alternating direction isogeometric L2 projections solver for material science simulations’’ Comput. Methods Mater. Sci., vol. 17, no.1, pp. 1‒11, 2017.
  30.  M. Łoś, J. Munoz-Matute, K. Podsiadło, M. Paszyński, and K. Pingali, “Parallel shared-memory isogeometric residual minimization (iGRM) for three-dimensional advection-diffusion problems’’, Lect. Notes Comput. Sci., vol. 12143, pp. 133‒148, 2020.
  31.  A. Alonso, R. Loredana Trotta, and A. Valli, “Coercive domain decomposition algorithms for advection-diffusion equations and systems’’, J. Comput. Appl. Math., vol. 96, no. 1, pp. 51‒76, 1998.
  32.  K. Pingali, D. Nguyen, M. Kulkarni, M. Burtscher, M.A. Hassaan, R. Kaleem, T.-H. Lee, A. Lenharth, R. Manevich, M. Mendez-Lojo, D. Prountzos, and X. Sui, “The tao of parallelism in algorithms’’, SIGPLAN, vol. 46, 2011, doi: 10.1145/1993316. 1993501.
  33.  A. Takhirov, R. Frolov, and P. Minev, “Direction splitting scheme for Navier-Stokes-Boussinesq system in spherical shell geometries’’, arXiv:1905.02300, 2019.
Go to article

Authors and Affiliations

Marcin Łoś
1
ORCID: ORCID
Maciej Woźniak
1
ORCID: ORCID
Ignacio Muga
2
ORCID: ORCID
Maciej Paszynski
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, al. Mickiewicza 30, 30-059 Krakow, Poland
  2. Instituto de Matemáticas, Pontificia Universidad Católica de Valparaíso, Chile
Download PDF Download RIS Download Bibtex

Abstract

Hybridization of meta-heuristic algorithms plays a major role in the optimization problem. In this paper, a new hybrid meta-heuristic algorithm called hybrid pathfinder algorithm (HPFA) is proposed to solve the optimal reactive power dispatch (ORPD) problem. The superiority of the Differential Evolution (DE) algorithm is the fast convergence speed, a mutation operator in the DE algorithm incorporates into the pathfinder algorithm (PFA). The main objective of this research is to minimize the real power losses and subject to equality and inequality constraints. The HPFA is used to find optimal control variables such as generator voltage magnitude, transformer tap settings and capacitor banks. The proposed HPFA is implemented through several simulation cases on the IEEE 118-bus system and IEEE 300-bus power system. Results show the superiority of the proposed algorithm with good quality of optimal solutions over existing optimization techniques, and hence confirm its potential to solve the ORPD problem.
Go to article

Bibliography

  1.  M. Gwozd and L. Ciepliński, “Power supply with parallel reactive and distortion power compensation and tunable inductive filter-part 1”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, pp. 401–408, 2020, doi: 10.24425/BPASTS.2020.133383.
  2.  M.N. Acosta, D. Topic, and M.A. Andrade, “Optimal Microgrid–Interactive Reactive Power Management for Day–Ahead Operation”, Energies, vol. 14, no. 5, p. 1275, 2021, doi: 10.3390/en14051275.
  3.  A.M. Tudose, I.I. Picioroaga, D.O. Sidea, and Co. Bulac, “Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm”, Energies, vol. 14, no. 5, p. 1222, 2021, doi: 10.3390/en14051222.
  4.  E. Canelas, T. Pinto-Varela, and B. Sawik, “Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study”, Energies, vol. 13, no. 9, p. 2249, 2020, doi: 10.3390/en13092249.
  5.  V. Suresh and S.S. Kumar, “Optimal reactive power dispatch for minimization of real power loss using SBDE and DE-strategy algorithm”, J. Ambient Intell. Hum. Comput., 2020, doi: 10.1007/s12652-020-02673-w.
  6.  H. Yapici and N. Cetinkaya, “A new meta-heuristic optimizer: pathfinder algorithm”, Appl. Soft Comput., vol. 78, pp. 545–568, 2019, doi: 10.1016/j.asoc.2019.03.012.
  7.  R. Storn and K. Price, “Differential evolution – A simple and efficient adaptive scheme for global optimization over continuous spaces,” J. Global Optim., vol. 11, pp. 341– 359, 1997, doi: 10.1023/A:1008202821328.
  8.  R.P. Singha and S.P. Ghoshal, “Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers”, Appl. Soft Comput., vol. 29, pp. 298–309, 2015, doi: 10.1016/j.asoc.2015.01.006.
  9.  M. Ghasemi et. al, “A new hybrid algorithm for optimal reactive power dispatch problem with discrete and continuous control variables,” Appl. Soft Comput., vol. 22, pp. 126–140, 2014, doi: 10.1016/j.asoc.2014.05.006.
  10.  M. Ghasemi and M. Ghanbarian, “Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study”, Inf. Sci., vol. 278, pp. 231–249, 2014, doi: 10.1016/j.ins.2014.03.050.
  11.  B. Mandal and P.K. Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization”, Electr. Power Energy Syst., vol. 53, pp. 123–134, 2013, doi: 10.1016/j.ijepes.2013.04.011.
  12.  S Mouassa and A. Salhi, “Ant lion optimizer for solving optimal reactive power dispatch problem in power systems”, Eng. Sci. Technol., vol. 20, pp 885–895, 2017, doi: 10.1016/j.jestch.2017.03.006.
  13.  S. Mugemanyi et. al., “Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm”, IEEE Access, vol. 8, pp. 65830–65867, 2020, doi: 10.1109/ACCESS.2020.2982988.
  14.  W.M. Villa-Acevedo and J.M. Lopez-Lezama, “A novel constraint handling approach for the optimal reactive power dispatch problem”, Energies, vol. 11, p. 2352, 2018, doi: 10.3390/en11092352.
  15.  R. Zimmerman, C.E. Murillo-Sanchez, and D. Gan, “MATPOWER 6.0, power systems engineering research center (PSERC)”, 2005, [Online]. Available: https://matpower.org/docs/MATPOWER-manual-6.0.pdf.
Go to article

Authors and Affiliations

V. Suresh
1
S. Senthil Kumar
1

  1. Department of Electrical and Electronics Engineering, Government College of Engineering, Salem-11, India
Download PDF Download RIS Download Bibtex

Abstract

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.
Go to article

Bibliography

  1.  M.M. Saha, J. Izykowski, and E. Rosolowski, Fault location on power networks. London: Springer, 2010.
  2.  M.B. Chatterjee and S. Debnath, “Cross correlation aided fuzzy based relaying scheme for fault classification in transmission lines,” Eng. Sci. Technol. Int J., vol. 23, no. 3, pp. 534–543, 2020, doi: 10.1016/j.jestch.2019.07.002.
  3.  A. Mukherjee, P.K. Kundu, and A. Das, “Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features,” Electr. Eng., 2021, doi: 10.1007/s00202-021-01285-7.
  4.  R. Godse and S. Bhat, “Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line,” IEEE Access, vol. 8, pp. 38459–38471, 2020, doi: 10.1109/access.2020.2975431.
  5.  Y. Liu, Y. Zhu, and K.Wu, “CNN-Based Fault Phase Identification Method of Double Circuit Transmission Lines,” Electr. Power Compon. Syst., vol. 48, no. 8, pp. 833–843, 2020, doi: 10.1080/15325008.2020.1821836.
  6.  M. Paul and S. Debnath, “Fault Detection and Classification Scheme for Transmission Lines Connecting Windfarm Using Single end Impedance,” IETE J. Res., pp. 1–13, 2021, doi: 10.1080/03772063.2021.1886601.
  7.  Y. Aslan and Y. E. Yağan, “Artificial neural-networkbased fault location for power distribution lines using the frequency spectra of fault data,” Electr. Eng., vol. 99, no. 1, pp. 301–311, 2016, doi: 10.1007/s00202-016-0428-8.
  8.  S. Ekici, “Support Vector Machines for classification and locating faults on transmission lines,” Appl. Soft Comput., vol. 12, no. 6,pp. 1650– 1658, 2012, doi: 10.1016/j.asoc.2012.02.011.
  9.  S.R. Samantaray, “A systematic fuzzy rule based approach for fault classification in transmission lines,” Appl. Soft Comput., vol. 13, no. 2, pp. 928–938, 2013, doi: 10.1016/j.asoc.2012.09.010.
  10.  S.R. Samantaray, P.K. Dash, and G. Panda, “Fault classification and location using HS-transform and radial basis function neural network,” Electr. Power Syst. Res., vol. 76, no. 9‒10, pp. 897–905, 2006, doi: 10.1016/j.epsr.2005.11.003.
  11.  A.A. Girgis and E.B. Makram, “Application of adaptive Kalman filtering in fault classification, distance protection, and fault location using microprocessors,” IEEE Trans. Power Syst., vol. 3, no. 1, pp. 301–309, 1988, doi: 10.1109/59.43215.
  12.  N. Ramesh Babu and B. Jagan Mohan, “Fault classification in power systems using EMD and SVM,” Ain Shams Eng. J., vol. 8, no. 2, pp. 103–111, 2017, doi: 10.1016/j.asej.2015.08.005.
  13.  F. Martin and J.A. Aguado, “Wavelet-based ann approach for transmission line protection,” IEEE Trans. Power Deliv., vol. 18, no. 4, pp. 1572–1574, 2003, doi: 10.1109/tpwrd.2003.817523.
  14.  O.A.S. Youssef, “Combined Fuzzy-Logic Wavelet-Based Fault Classification Technique for Power System Relaying,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 582–589, 2004, doi: 10.1109/tpwrd.2004.826386.
  15.  A. Yadav and Y. Dash, “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination,” Adv. Artif. Neural Syst., vol. 2014, pp. 1–20, 2014, doi: 10.1155/2014/230382.
  16.  M. Fikri and M.A.H. El-Sayed, “New algorithm for distance protection of high voltage transmission lines,” IEE Proc. C Gener. Transm. Distrib., vol. 135, no. 5, p. 436, 1988, doi: 10.1049/ip-c.1988.0056.
  17.  S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” Fundamental Papers in Wavelet Theory, 2009, pp. 494–513, doi: 10.1109/34.192463.
  18.  R. Salat and S. Osowski, “Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 979–986, 2004, doi: 10.1109/tpwrs.2004.825883.
  19.  P.K. Dash, S.R. Samantaray, and G. Panda, “Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine,” IEEE Trans. Power Deliv., vol. 22, no. 1, pp. 67–73, 2007, doi: 10.1109/tpwrd.2006. 876695.
  20.  V. Vapnik, “The Support Vector Method of Function Estimation,” Nonlinear Modeling, 1998, pp. 55–85, doi: 10.1007/978-1-4615-5703- 6_3.
  21.  Chih-Wei Hsu and Chih-Jen Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425, 2002, doi: 10.1109/72.991427.
  22.  S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: a stepwise procedure for building and training a neural network,” Neurocomputing, pp. 41–50, 1990, doi: 10.1007/978-3-642-76153-9_5.
  23.  J. Manit and P. Youngkong, Neighborhood components analysis in sEMG signal dimensionality reduction for gait phase pattern recognition. 7th Int. Conf. on Broadband Communications and Biomedical Applications, 2011, doi: 10.1109/IB2Com.2011.6217897.
  24.  M. Akdag and S. Rustemli, “Transmission line fault location: Simulation of real faults using wavelet transform based travelling wave methods,” Bitlis Eren Univ. J. Sci. Technol., vol. 9, no. 2, pp. 88–98, 2019, doi: 10.17678/beuscitech.653273.
  25.  N. Perera and A.D. Rajapakse, “Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier,” IEEE Trans. Power Deliv., vol. 26, no. 1, pp. 410–419, 2011, doi: 10.1109/tpwrd.2010.2060214.
  26.  D. Gogolewski and W. Makiela, “Problems of Selecting the Wavelet Transform Parameters in the Aspect of Surface Texture Analysis,” TEH VJESN, vol. 28, no. 1, 2021, doi: 10.17559/tv-20190312141348.
  27.  J. Ypsilantis et al., “Adaptive, rule based fault diagnostician for power distribution networks,” IEE Proc. C Gener. Transm. Distrib., vol. 139, no. 6, p. 461, 1992, doi: 10.1049/ip-c.1992.0064.
  28.  F.B. Costa, “Fault-Induced Transient Detection Based on Real-Time Analysis of the Wavelet Coefficient Energy,” IEEE Trans. Power Deliv., vol. 29, no. 1, pp. 140–153, 2014, doi: 10.1109/tpwrd.2013.2278272.
  29.  P. Kapler, “An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis,” Bull. Pol. Acad Sci. Tech. Sci., vol. 69, no. 1, 2021, doi: 10.24425/bpasts.2020.136216.
  30.  Y.Q. Chen, O. Fink, and G. Sansavini, “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 561–569, 2018, doi: 10.1109/TIE.2017.2721922.
  31.  G. Revati and B. Sunil, “Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults,” Turk. J. Electr. Eng. Comput. Sci., vol. 28, no. 5, pp. 2768–2788, 2020, doi: 10.3906/elk-1912-7.
  32.  B.Y. Vyas, R.P. Maheshwari, and B. Das, “Versatile relaying algorithm for detection and classification of fault on transmission line,” Electr. Power Syst. Res., vol. 192, p. 106913, 2021, doi: 10.1016/j.epsr.2020.106913.
Go to article

Authors and Affiliations

Melih Coban
1 2
ORCID: ORCID
Suleyman S. Tezcan
2
ORCID: ORCID

  1. Bolu Abant Izzet Baysal University, Bolu, Turkey
  2. Gazi University, Ankara, Turkey
Download PDF Download RIS Download Bibtex

Abstract

The paper is the second part of the work, devoted to a DC power supply with a power factor correction function. The power supply is equipped additionally with a shunt active power filter function, which enables the compensation of reactive and distortion power, generated by loads, connected to the same power grid node. A tunable inductive filter, included at the input of the power electronics current source – the main block of the power supply – allows for an improvement of the quality of the system control, compared to the device with a fixed inductive filter. This improvement was possible by extending the current source “frequency response”, which facilitated increasing the dynamics of current changes at the power supply input. The second part of the work briefly reminds the reader of the principle of operation and the structures of both the power supply control system and its power stage. The main purpose of this paper is to present the selected test results of the laboratory model of the electric system with the power supply.
Go to article

Bibliography

  1.  M. Gwóźdź, Ł. Ciepliński, and M. Krystkowiak, “Power supply with parallel reactive and distortion power compensation and tunable inductive filter – Part 1,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 3, pp. 401–408, 2020.
  2.  Y. Ma, F. Hong, X. Zhou, and Z. Gao, “An overview on harmonic suppression,” 2018 Chinese Control and Decision Conference (CCDC), Shenyang, 2018, pp. 4943– 4948, doi: 10.1109/CCDC.2018.8407987.
  3.  M. Pasko, D. Buła, K. Dębowski, D. Grabowski, and M. Maciążek, “Selected methods for improving operating conditions of three- phase systems working in the presence of current and voltage deformation – part I,” Archives of Electrical Engineering, vol. 67, no. 3, pp. 591–602, 2018.
  4.  M. Siwczyński and M. Jaraczewski, “Reactive compensator synthesis in time-domain,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 60, no. 1, pp. 119–124, 2012.
  5.  D. Buła and M. Pasko, “Stability analysis of hybrid active power filter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 62, no. 2, pp. 279–286, 2014.
  6.  S. Fryze, “Active, reactive, and apparent power in circuits with nonsinusoidal voltage and current”, Przegląd Elektrotechniczny, vol. 13, pp. 193–203, 1931.
  7.  M.H. Rashid, Power Electronics Handbook. Oxford, Elsevier, 2018.
  8.  M. Krystkowiak, “Modified model of wideband power electronics controlled current source with output current modulation,” Elektronika, vol. 57, no. 11, pp. 65–70, 2016 [in Polish].
  9.  Mitsubishi Electric, Intelligent Power Modules. [Online]. Available: http://www.mitsubishielectric.com/semiconductors/products/powermod/ intelligentpmod/index.html [Accessed: 05 Feb. 2021].
  10.  Magnetics, [Online]. Available: https://www.mag-inc.com/home [Accessed: 05 Feb. 2021].
  11.  S. Saeed, R. Georgious, and J. Garcia, “Modeling of magnetic elements including losses application to variable inductor”, Energies, vol. 13, p. 1865, 2020, doi: 10.3390/en13081865.
  12.  E. Chong, and S. Zak. An Introduction to Optimization. 4th ed. Wiley Publishing, 2013.
  13.  Alfine-Tim [Online]. Available: http://analog.alfine.pl/oferta/produkty-alfine/systemy-uruchomieniowe [Accessed: 05 Feb. 2021].
  14.  M. Gwóźdź, Ł. Ciepliński, and A. Gąsiorek. “Real-time identification of the selected parameters of periodic signals,” Progress in Applied Electrical Engineering, PAEE, (on-line Conference), Kościelisko, Poland, 2020.
  15.  Standard EN 50160 (2010) – Voltage characteristics of electricity supplied by public distribution networks.
  16.  W. Kester, The Data Conversion Handbook. Newnes, Analog Devices Inc, 2005.
  17.  S. Pop, D. Pitica, and I. Ciascai, “Adaptive algorithm for error correction from sensor measurement,” 2008 31st International Spring Seminar on Electronics Technology, Budapest, 2008, pp. 373–378, doi: 10.1109/ISSE.2008.5276632.
  18.  J.C. Doyle, B.A. Francis, and A.R. Tannenbaum, Feedback Control Theory. Dover Publications, 2013.
  19.  T. Żabiński and L. Trybus, “Tuning P-PI and PI-PI controllers for electrical servos,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, no. 1, pp. 51–58, 2010.
  20.  M. Naouar et al., “FPGA-based speed control of synchronous machine using a P-PI controller,” 2006 IEEE International Symposium on Industrial Electronics, Montreal, QC, Canada, 2006, pp. 1527–1532, doi: 10.1109/ISIE.2006.295698.
  21.  R. Porada and N. Mielczarek, “Modeling of chaotic systems in the ChaoPhS Program,” in Modelling Dynamics in Processes and Systems, W. Mitkowski, J. Kacprzyk, (Eds). Studies in Computational Intelligence, vol. 180, Springer, Berlin, Heidelberg, 2009, doi: 10.1007/978- 3-540-92203-2_1.
  22.  Analog Devices, [Online]. Available: https://www.analog.com/en/products/adsp-21369.html#product-documentation [Accessed: 05 Feb. 2021].
Go to article

Authors and Affiliations

Michał Gwóźdź
1
ORCID: ORCID
Rafał Wojciechowski
1
ORCID: ORCID
Łukasz Ciepliński
1
ORCID: ORCID

  1. Poznan University of Technology, Faculty of Control, Robotics and Electrical Engineering, Piotrowo 3A, 60-965 Poznan, Poland
Download PDF Download RIS Download Bibtex

Abstract

Elements of the lightning protection system (LPS) often perform additional functions in the facility. Correct and economical design of these elements is possible with the fulfillment of specific requirements, close coordination and inter-branch cooperation. The article draws attention to important aspects of LPS design and highlights the ambiguities that may arise during this process. Firstly, the history of changes in national standardization in the field of lightning protection is approximated. Secondly, the individual components of external LPS are presented. Subsequently, the normative material requirements for earthing are compiled, depending on their function (for lightning protection and protection against electric shock in MV and LV installations). The last part of the paper is devoted to the comparison of the protective angle method and the rolling sphere method. The analysis was made on the example of a simple object for which LPS class I is required. It has been shown that despite the possibility of using both methods, they may result in different solutions. Depending on the choice of method, the difference in the arrangement of the air-termination system is indicated. Examples of generally available LPS solutions are also given, taking account of various materials and assembly technologies.
Go to article

Bibliography

  1.  Ministry of Investment and Development. “Regulation of Minister of Infrastructure of 12 April 2002 regarding technical conditions that shall be met by buildings and their location,” Consolidated text JoL 2019 item 1065, as amended, Apr. 8, 2019.
  2.  Technical Committee No. 55 (PKN/KT 55). “Protection against lightning,” Series of standards PN-EN 62305 – Parts 1–4, 2011/2012.
  3.  E. Musiał, “Foundation and ring earth electrode,” SEP INPE, vol. 143, pp. 3–33, 2011.
  4.  K. Aniserowicz, “Analytical calculations of surges caused by direct lightning strike to underground intrusion detection system,” Bull. Polish Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 263–269, 2019, doi: 10.24425/bpas.2019.128118.
  5.  M. Zielenkiewicz, T. Maksimowicz, and R. Marciniak, “Grounding installations – standards recommendations,” SEP INPE, vol. 184–185, pp. 67–87, 2015.
  6.  CBM Technology. “Earth electrodes.” [Online]. Available: https://cbm-technology.com.pl/wp-content/uploads/2021/05/CBM-KATALOG. pdf [Accessed: 22. May. 2020].
  7.  OBO Bettermann. “Earthing systems.” [Online]. Available: https://obo.pl/media/Leitfaden_Erdungs-Systeme_pl.pdf [Accessed: 22. May. 2020].
  8.  Technical Committee No. 173 (PKN/KT 173). “Telecommunications bonding networks for buildings and other structures,” Standard PN- EN 50310, Sep. 27, 2016.
  9.  Technical Committee No. 73 (PKN/KT 73). “Earthing of power installations exceeding 1 kV a.c.,” Standard PN-EN 50522, Apr. 29, 2011.
  10.  Working Committee NA 005-09-85 AA. “Foundation earth electrode – Planning, execution and documentation,” Standard DIN 18014, Mar. 1, 2014.
  11.  M. Mokhtari, Z. Abdul-Malek, and G.B. Gharehpetian, “A critical review on soil ionisation modelling for grounding electrodes,” Archives of Electrical Engineering, vol. 65, no. 3, pp. 449–461, 2016, doi: 10.1515/aee-2016-0033.
  12.  E. Musiał, “Measurement of earth resistance,” SEP INPE, vol. 45, pp. 53–56, 2002.
  13.  Dehn Polska. “Foundation earth electrodes.” [Online]. Available: https://szkolenia.dehn.pl/pliki/publikacje/ds162_uziomy_fundamentowe_ pl.pdf [Accessed: 22. May. 2020].
  14.  A. Dąda, P. Błaut, and K. Sidor, “The role of equipotential bondings as a measure of protection against electric shock by the example of special installations,” in Proc. of SPIE 11176, 2019, pp. 83–93, doi: 10.1117/12.2536726.
  15.  Technical Committee No. 55 (PKN/KT 55). “Low-voltage electrical installations – Part 5–54: Selection and erection of electrical equipment – Earthing arrangements and protective conductors,” Standard PN-HD 60364-5-54, Aug. 26, 2011.
  16.  Elko–Bis Systemy Odgromowe. “Lightning masts on a foundation.” [Online]. Available: https://tinyurl.com/38xnn6xj [Accessed: 22. May. 2020].
  17.  Alumast. “Composite lightning masts.” [Online]. Available: https://tinyurl.com/2t832n34 [Accessed: 22. May. 2020].
Go to article

Authors and Affiliations

Anna Dąda
1
Paweł Błaut
1
Piotr Miller
2

  1. AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, al. Mickiewicza 30, 30-059 Krakow, Poland
  2. Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
Download PDF Download RIS Download Bibtex

Abstract

The continuing efforts for reduction of the torque and flux ripples using Finite Set Model Predictive Direct Torque Control methods (FS-MPDTC) have been currently drowning a great attention from the academic communities and industrial applications in the field of electrical drives. The major problem of high torque and flux ripples refers to the consideration of just one active voltage vector at the whole control period. Implementation of two or more voltage vectors at each sampling time has recently been adopted as one of the practical techniques to reduce both the torque and flux ripples. Apart from the calculating challenge of the effort control, the parameter dependency and complexity of the duty ratio relationships lead to reduction of the system robustness. those are two outstanding drawbacks of these methods. In this paper, a finite set of the voltage vectors with a finite set of duty cycles are employed to implement the FS-MPDTC of induction motor. Based on so-called Discrete Duty Cycle- based FS-MPDTC (DDC-FS-MPDTC), a base duty ratio is firstly determined based on the equivalent reference voltage. This duty ratio is certainly calculated using the command values of the control system, while the motor parameters are not used in this algorithm. Then, two sets of duty ratios with limit members are constructed for two adjacent active voltage vectors supposed to apply at each control period. Finally, the prediction and the cost function evaluation are performed for all of the preselected voltage vectors and duty ratios. However, the prediction and the optimization operations are performed for only 12 states of inverter. Meanwhile, time consuming calculations related to SVM has been eliminated. So, the robustness and complexity of the control system have been respectively decreased and increased, and both the flux and torque ripples are reduced in all speed ranges. The simulation results have verified the damping performance of the proposed method to reduce the ripples of both the torque and flux, and accordingly the experimental results have strongly validated the aforementioned statement.
Go to article

Bibliography

  1.  J.P. Wach, “Maximum Torque Control of 3-phase induction motor drives,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 433–445, 2018.
  2.  A. Sikorski, K. Kulikowski, and M. Korzeniewski, “Modern Direct Torque and Flux Control methods of an induction machine supplied by three-level inverter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 61, no. 4, pp. 771–778, 2013.
  3.  D. Stando and M.P. Kazmierkowski, “Constant switching frequency predictive control scheme for three-level inverter-fed sensorless induction motor drive,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1057–1068, 2020.
  4.  V. Talavat, S. Galvani, and M. Hajibeigy, “Direct predictive control of asynchronous machine torque using matrix converter,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 773–788, 2018.
  5.  I. Takahashi and T. Noguchi, “A new quick response and high efficiency control strategy of an induction motor,” IEEE Trans. Power App., vol. IA-22, no. 5, pp. 820–827, Sept. 1986, doi: 10.1109/TIA.1986.4504799.
  6.  M. Depenbrock, “Direct self-control (DSC) of a inverter fed induction machine,” IEEE Trans. Power Electron., vol. 3, no. 4, pp. 420–429, Oct. 1988.
  7.  Y.-S. Lai and J.-H. Chen, “A new approach to direct torque control of induction motor drives for constant inverter switching frequency and torque ripple reduction,” IEEE Trans. Energy. Convers., vol., 16, no. 3, pp. 220–227, Sep. 2001.
  8.  C. Lascu, I. Boldea, and F. Blaabjerb, “A modified direct torque control for induction motor sensorless drive,” IEEE Trans. Ind. Appl., vol. 36, no. 1, pp. 122–130, Jan/Feb. 2000.
  9.  L. Tang, L. Zhong, M. Rahman, and Y. Hu, “A novel direct torque controlled interior permanent magnet synchronous machine drive with low ripple in flux and torque and fixed switching frequency,” IEEE Trans. Ind. Appl., vol. 19, no. 2, pp. 346–354, Mar. 2004.
  10.  R. Narayan and D.B. Subudhi, “Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 20, no.3, pp. 363–376, 2010.
  11.  I. Bakhti, S. Chaouch, and A. Maakouf, “High performance backstepping control of induction motor with adaptive sliding mode observer,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 21, no.3, pp. 331–344, 2011.
  12.  B. Kenny and R. Lorenz, “Stator- and rotor-flux-based deadbeat direct torque control of induction machines,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1093–1101, Jul/Aug. 2003.
  13.  J. Rodriguez, M.P. Kazmierkowski, J. Espinoza, P. Zanchetta, H. Abu-Rub, H. Young, and C.A. Rojas, “State of the art of finite control set model predictive control in power electronics,” IEEE Trans. Ind. Inform., vol. 9, no. 2, pp. 1003–1016, May. 2013.
  14.  Y. Zhang, Y. Bai, H. Yang and B. Zhang “Low switching frequency model predictive control of three-level inverter-fed im drives with speed-sensorless and field weakening operations,” IEEE Trans. Ind. Electron., vol. 66, no. 6, pp. 4262–4272, 2019, doi: 10.1109/ TIE.2018.2868014.
  15.  S.A. Davari, D.A. Khaburi, and R. Kennel, “An improved FCS-MPC algorithm for an induction motor with an imposed optimized weighting factor,” IEEE Trans. Power Electron., vol. 27, no. 3, pp. 1540–1551, 2012.
  16.  L. Yan, M. Dou, H. Zhang, and Z. Hua, “Speed sensorless dual reference frame predictive torque control for induction machines,” IEEE Trans. Power. Electron., vol. 34, no. 12, pp. 12285–12295, 2019, doi: 10.1109/TPEL.2019.2904542.
  17.  C.S. Vazquez, J. Rodriguez, M. Rivera, L.G. Franquelo, and M. Norambuena, “Model predictive control for power converters and drives: advanced and trends,” IEEE Trans. Ind. Electron., vol. 64, no. 2, pp. 935–947, 2017.
  18.  W. Xie et al., “Finite control set-model predictive torque control with a deadbeat solution for pmsm drives,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5402–5410, Sept. 2015, doi: 10.1109/TIE.2015.2410767.
  19.  Y. Zhang, B. Yang, H. Yang, and M. Nurambuena, “Generalized sequential model predictive control of im drives with field-weakening ability,” IEEE Trans. Power Elecron., vol. 34, no. 9, pp. 8944–8955, 2019, doi: 10.1109/TPEL.2018.2886206.
  20.  M. Norambuena, J. Rodrigez, Z. Zhang, F. Wang, C. Garcia, R. Kenel, and G.-D. Andreescu, “A very simple strategy for high-quality performance of AC machines using model predictive control,” IEEE Trans. Power Electron., vol. 34, no. 1, pp. 794–800, Jan. 2019.
  21.  J. Rodriguez, R.M. Kennel, J.R. Espinoza, M. Trincado, C.A. Silva, and C.A. Rojas, “High performance control strategies for electrical drives: An experimental assessment,” IEEE Trans. Ind. Electron., vol.29, no. 2, pp. 812– 820, Jan/Feb. 2012.
  22.  T. Geyer, “Tuning guidelines for model predictive torque and flux control,” IEEE Trans. Ind. Appl., vol. 54, no. 5, pp. 4464–4475, Oct. 2018.
  23.  F. Wang, G. Lin, and Y. He, “Passivity-based model predictive control of three-level inverter-fed induction motor,” IEEE Trans. Power. Electron., vol. 36, no. 2, pp. 1984–1993, Feb. 2021, doi: 10.1109/TPEL.2020.3008915.
  24.  M. Pacas and J. Weber, “Predictive direct torque control for the PM synchronous machine,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1350–1356, Oct. 2005.
  25.  F. Niu, F. Niu, K. Li, and Y. Wang, “Direct torque control for permanent-magnet synchronous machines based on duty ratio modulation,” IEEE Trans. Ind Electron., vol. 62, no. 10, pp. 6160–6170, Oct. 2015.
  26.  Y. Zhang and J. Zhu, “Direct torque control of permanent magnet synchronous motor with a reduced torque ripple and commutation frequency,” IEEE Trans. Power Electron., vol. 26, no. 1, pp. 235–248, Jan. 2011.
  27.  J.-K. Kang and S.-K. Sul, “New direct torque control of induction motor for minimum torque ripple and constant switching frequency,” IEEE Trans. Ind. Appl., vol. 35, no. 5, pp. 1076–1082, Sep/Oct. 1999.
  28.  K.K. Shyu, J.K. Lin, V.T. Pham, M.J. Yang, and T.W. Wang, “Global minimum torque ripple design for direct torque control of induction motor drives,” IEEE Trans. Ind Electron., vol. 57, no. 9, pp. 3148–3156, Sep. 2010.
  29.  Y. Ren, Z.Q. Zhu, and J. Liu, “Direct torque control of permanent-magnet synchronous machine drives with a simple duty ratio regulator,” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5249–5259, Oct. 2014.
  30.  Q. Liu and K. Hameyer, “Torque ripple minimization for direct torque control of pmsm with modified FSMPC,” IEEE Trans. Ind. Electron., vol. 52, no. 6, pp. 4855–4864, Aug. 2016.
  31.  Y. Zhang and H. Yang, “Torque ripple reduction of model predictive torque control of induction motor drives,” in Proc. Energy Convers. Congr. Expo., 2013, pp. 1176–1183.
  32.  Y. Zhang, H. Yang, and B. Xia, “Model predictive torque control of induction motor drives with reduced torque ripple,” IET Electr. Power Appl., vol. 9, no. 9, pp. 595–604, 2015.
  33.  Y. Zhang and H. Yang, “Model predictive torque control of induction motor drives with optimal duty cycle control,” IEEE Trans. Power Elecron., vol. 29, no. 12, pp. 6593–6603, Dec. 2014.
  34.  Y. Zhang and H. Yang, “Generalized two-vector-based Model-predictive torque control of induction motor drives,” IEEE Trans. Power Elecron., vol. 30, no. 7, pp. 6593–6603, Jul. 2015.
  35.  Y. Zhang, J. Zhu, and B. Xia, “A novel duty cycle control strategy to reduce both the torque and stator flux ripples for DTC of permanent- magnet synchronous motor drives with switching frequency reduction,” IEEE Trans. Power Electron., vol. 31, no. 5, pp. 3738–3753, May 2016.
  36.  C. Lascu and G.-D. Andreescu, “Sliding mode observer and improved integrator with dc-offset compensation for flux estimation in sensorless controlled induction motors,” IEEE Trans. Ind. Electron., vol. 53, no. 3, pp. 785–794, Jun. 2006.
  37.  P.H. Cortes, S. Kouro, B. La Rocca, R. Vargas, J. Rodrigues, J. Leon, S. Vazquez, and L. Franquelo, “Guidelines for weighting factors design in model predictive control of power converters and drives,” in Proc. IEEE ICIT, 2009, pp. 1–7.
Go to article

Authors and Affiliations

Babak Kiani
1
Babak Mozafari
1
Soodabeh Soleymani
1
Hosein Mohammadnezhad Shourkaei
1

  1. Department of Electrical Engineering, Science and research Branch, Islamic Azad University, Tehran, IRAN
Download PDF Download RIS Download Bibtex

Abstract

This paper aims to discuss the behavior of the proprietary real-time simulator (RTS) during testing the coordination of distance relay protections in power engineering. During the construction process of the simulator, the mapping of various dynamic phenomena occurring in the modeled part of the power system was considered. The main advantage to the solution is a lower cost of construction while maintaining high values of essential parameters, based on the generally available software environment (MATLAB/Simulink). The obtained results are discussed in detail. This paper is important from the point of view of the cost-effectiveness of design procedures, especially in power systems exploitation and when avoiding faults that result from the selection of protection relay devices, electrical devices, system operations, and optimization of operating conditions. The manuscript thoroughly discusses the hardware configuration and sample results, so that the presented real-time simulator can be reproduced by another researcher.
Go to article

Bibliography

  1.  M. Faruque, T. Strasser, and G. Lauss, “Real-Time Simulation Technologies for Power Systems Design, Testing and Analysis”, IEEE Power Energy Technol. Syst. J., vol. 2, no. 2, pp. 63‒73, 2015.
  2.  P.G. McLaren, R. Kuffel, R. Wierckx, J. Giesbrecht, and L. Arendt, “A real time digital simulator for testing relays”, IEEE Trans. Power Deliv., vol. 7, no. 1, pp. 207–213, 1992.
  3.  C. Dufour and J. Belanger, “A PC-based real-time parallel simulator of electric systems and drives”, Parallel Comput. Electr. Eng., vol. 7, no. 1, pp. 105–113, 2004.
  4.  D. Majstorovic, I. Celanovic, N.D. Teslic, N. Celanovic, and V.A. Katic, “Ultralow-latency hardware-in-the-loop platform for rapid validation of power electronics designs”, IEEE Trans. Ind.. Electron., vol. 58, no. 10, pp. 4708–4716, 2011.
  5.  R. Razzaghi, M. Mitjans, F. Rachidi, and M. Paolone, “An automated FPGA real-time simulator for power electronics and power systems electromagnetic transient applications”, Electr. Power Syst. Res. vol. 141, pp. 147–156, 2016.
  6.  F.R. Blánquez, E. Rebollo, F. Blázquez, and C.A. Platero, “Real Time Power Plant Simulation Platform for Training on Electrical Protections and Automatic Voltage Regulators”, 12th International Conference on Environment and Electrical Engineering, Wroclaw, Poland, 2013, pp.18‒22.
  7.  L.A. Montoya and D. Montenegro, “Adaptive Protection Testbed Using Real time and Hardware-in-the-Loop Simulation”, IEEE International Conference PowerTech., 2013, Grenoble, France, 2013, pp. 20‒24.
  8.  M. Krakowski and Ł. Nogal, “Testing power system protections utilizing hardware-in-the-loop simulations on real-time Linux”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1099‒1105, 2020.
  9.  X. Guillaud et al., “Applications of Real-Time Simulation Technologies in Power and Energy Systems”, IEEE Power Energy Technol. Syst. J., vol. 2, no. 3, pp. 103–115, 2015.
  10.  M.D. Omar Faruque et al., “Real-Time Simulation Technologies for Power Systems Design, Testing, and Analysis”, IEEE Power Energy Technol. Syst. J., vol. 2, no. 2, pp. 63–73, 2015.
  11.  R. Kuffel, D. Ouellete, and P. Forsyth, “Real time simulation and testing using IEC 61850”, Modern Electric Power Systems, (MEPS) International Symposium, 2010, pp. 1‒8.
  12.  D. Gurusinghe, S. Kariyawasam, and D. Ouellette, “Testing of IEC 61850 sampled values based digital substation automation systems”, J. Eng., vol. 15, 2018, pp. 807–811.
  13.  M. Krakowski, K. Kurek, and Ł. Nogal, “Comparative analysis of the DAQ cards-based and the IEC 61850-based real time simulations in the matlab/simulink environment for power system protections”, Electr. Power Syst. Res., vol. 192, pp. 1‒6, 2021.
  14.  RTDS Technologies Inc., Real Time Digital Simulators, [Online] Available: https://www.rtds.com, (accesed: 10.01.2019).
  15.  OPAL-RT Technologies, [Online] Available: https://www.opal-rt.com, (accesed: 10.05.2019).
  16.  Z. Yang, Y. Wang, L. Xing, B. Yin, and J.Tao, “Relay Protection Simulation and Testing of Online Setting Value Modification Based on RTDS”, IEEE Access, vol. 8, pp. 4693‒4699, 2019.
  17. Simulink desktop realtime toolbox, [Online] Available: https://www.mathworks.com/products/simulink-desktop-real-time.html, (accesed: 10.02.2019).
  18.  F. Coffele, C. Booth, and A. Dysko, “An adaptive overcurrent protection scheme for distribution networks”, IEEE Trans. Power Deliv., vol 30, no. 1, pp. 561–568, 2015.
  19.  D. Dantas, “Energy and reactive power differential protectionhardware-in-the-loop validation for transformer application”, J. Eng., vol. 15, pp. 1160–1164, 2018.
  20.  Z. Xu, Z. Su, J. Zhang, A. Wen, and Q. Yang, “An interphase distance relaying algorithm for series-compensated transmission lines”, IEEE Trans. Power Deliv., vol. 29, no. 2, pp. 834–841, 2014.
  21.  R. Kuffel, P. Forsyth, and C. Peters, “The Role and Importance of Real Time Digital Simulation in the Development and Testing of Power System Control and Protection Equipment”, IFAC PapersOnLine, vol. 49‒27, pp. 178–182, 2016.
  22.  V. Papaspiliotopoulos, G. Korres, V. Kleftakis, and N. Hatziargyriou, “Hardware-in-theloop design and optimal setting of adaptive protection schemes for distribution systems with distributed generation”, IEEE Trans. Power Deliv., vol. 32, no. 1, pp. 393–400, 2015.
  23.  A. Smolarczyk, E. Bartosiewicz, R. Kowalik, and D.D. Rasolomampionona, „A Simple Real-Time Simulator for Protection Devices Test”, EnergyCon 2014, IEEE International Energy Conference, Dubrovnik, Croatia, 2014, pp. 837 – 843.
  24.  GE Digital Energy, D60 Line Distance Protection System. UR Series Instruction Manual, (accessed: 12.06.2018).
  25.  Advantech, [Online] Available: https://www.www.advantech.com, (accessed: 15.07.2019).
  26.  OMICRON electronics, CMS 156 Reference Manual,Version CMS156.AE.9, (accessed: 14.07.2020).
  27.  P. Opała, “Extension of a real time simulator for testing of protection relays”, M.Sc. thesis, Warsaw University of Technology, Electrical Power Engineering Institute, Warsaw, 2018.
Go to article

Authors and Affiliations

Adam Smolarczyk
1
ORCID: ORCID
Sebastian Łapczyński
1
ORCID: ORCID
Michał Szulborski
1
ORCID: ORCID
Łukasz Kolimas
1
ORCID: ORCID
Łukasz Kozarek
2
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Electrical Engineering, Electrical Power Engineering Institute, 00-662 Warsaw, Poland
  2. ILF Consulting Engineers Polska Sp. z o.o., ul. Osmańska 12, 02-823 Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

The cyclicity of the state matrices of positive linear electrical circuits with the chain structure is considered. Two classes of positive linear electrical circuits with the chain structure and cyclic Metzler state matrices are analyzed. Some new properties of these classes of positive electrical circuits are established. The results are extended to fractional linear electrical circuits.
Go to article

Bibliography

  1.  A. Berman and R.J. Plemmons, Nonnegative Matrices in the Mathematical Sciences. Philadelphia: SIAM, 1994.
  2.  L. Farina and S. Rinaldi, Positive Linear Systems; Theory and Applications. New York: J. Wiley, 2000.
  3.  T. Kaczorek, Positive 1D and 2D Systems. London: Springer-Verlag, 2002.
  4.  T. Kaczorek, “Positive linear systems with different fractional orders,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, no. 3, pp. 453–458, 2010.
  5.  T. Kaczorek, “Normal fractional positive linear systems and electrical circuits,” in Proc. Conf. Automation 2019, Warsaw, 2020, pp. 13–26.
  6.  T. Kaczorek, Selected Problems of Fractional Systems Theory. Berlin: Springer, 2011.
  7.  T. Kaczorek and K. Rogowski, Fractional Linear Systems and Electrical Circuits. Cham: Springer, 2015.
  8.  W. Mitkowski, “Dynamical properties of metzler systems,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 54, no. 4, pp. 309–312, 2008.
  9.  W. Mitkowski, Outline of Control Theory. Kraków: Publishing House AGH, 2019.
  10.  P. Ostalczyk, Discrete Fractional Calculus. River Edge, NJ: World Scientific, 2016.
  11.  I. Podlubny, Fractional Differential Equations. San Diego: Academic Press, 1999.
  12.  T. Kaczorek, “Reachability and observability of positive discrete-time linear systems with integer positive and negative powers of the state frobenius matrices,” Arch. Control Sci., vol. 28, no. 1, pp. 5–20, 2018.
  13.  M.D. Ortigueira and J. A. Tenreiro Machado, “New discrete-time fractional derivatives based on the bilinear transformation: definitions and properties,” J. Adv. Res., vol. 25, pp. 1–10, 2020.
  14.  A. Ruszewski, “Stability of discrete-time fractional linear systems with delays,” Arch. Control Sci., vol. 29, no. 3, pp. 549–567, 2019.
  15.  L. Sajewski, “Stabilization of positive descriptor fractional discrete-time linear systems with two different fractional orders by decentralized controller,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 5, pp. 709–714, 2017.
  16.  R. Stanisławski, K. Latawiec, and M. Łukaniszyn, “A comparative analysis of laguerre-based approximatiors to the grunwald-letnikov fractional-order difference,” Math. Probl. Eng., vol. 2015, 2015.
  17.  F.G. Gantmacher, The Theory of Matrices. London: Chelsea Pub. Comp., 1959.
  18.  T. Kaczorek and K. Borawski, “Stability of continuoustime and discrete-time linear systems with inverse state matrices,” Meas. Autom. Monit., vol. 62, no. 4, pp. 132–135, 2016.
  19.  T. Kaczorek, Polynomial and Rational Matrices. London: Springer, 2007.
Go to article

Authors and Affiliations

Tadeusz Kaczorek
1
ORCID: ORCID

  1. Bialystok University of Technology, Faculty of Electrical Engineering, Wiejska 45D, 15-351 Białystok, Poland
Download PDF Download RIS Download Bibtex

Abstract

When patterns to be recognised are described by features of continuous type, discretisation becomes either an optional or necessary step in the initial data pre-processing stage. Characteristics of data, distribution of data points in the input space, can significantly influence the process of transformation from real-valued into nominal attributes, and the resulting performance of classification systems employing them. If data include several separate sets, their discretisation becomes more complex, as varying numbers of intervals and different ranges can be constructed for the same variables. The paper presents research on irregularities in data distribution, observed in the context of discretisation processes. Selected discretisation methods were used and their effect on the performance of decision algorithms, induced in classical rough set approach, was investigated. The studied input space was defined by measurable style-markers, which, exploited as characteristic features, facilitate treating a task of stylometric authorship attribution as classification
Go to article

Bibliography

  1.  G. Franzini, M. Kestemont, G. Rotari, M. Jander, J. Ochab, E. Franzini, J. Byszuk, and J. Rybicki, “Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm,” Front. Digital Humanit., vol. 5, p. 4, 2018, doi: 10.3389/fdigh.2018.00004.
  2.  A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, “Data level preprocessing methods,” in Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018, pp. 79–121, doi: 10.1007/978-3-319-98074-4_5.
  3.  S. Garcia, J. Luengo, J. Saez, V. Lopez, and F. Herrera, “A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 734–750, 2013, doi: 10.1109/TKDE.2012.35.
  4.  S. Das, S. Datta, and B.B. Chaudhuri, “Handling data irregularities in classification: Foundations, trends, and future challenges,” Pattern Recognit., vol. 81, pp. 674–693, 2018, doi: 10.1016/j.patcog.2018.03.008.
  5.  U. Stańczyk, “Evaluating importance for numbers of bins in discretised learning and test sets,” in Intelligent Decision Technologies 2017: Proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017) – Part II, ser. Smart Innovation, Systems and Technologies, I. Czarnowski, J.R. Howlett, and C.L. Jain, Eds. Springer International Publishing, 2018, vol. 72, pp. 159–169, doi: 10.1007/978-3-319-59421-7_15.
  6.  G. Baron, “On approaches to discretization of datasets used for evaluation of decision systems,” in Intelligent Decision Technologies 2016, ser. Smart Innovation, Systems and Technologies, I. Czarnowski, A. Caballero, R. Howlett, and L. Jain, Eds. Springer, 2016, vol. 56, pp. 149–159, doi: 10.1007/978-3-319-39627-9_14.
  7.  U. Stańczyk and B. Zielosko, “On approaches to discretisation of stylometric data and conflict resolution in decision making,” in Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 23rd International Conference KES-2019, Budapest, Hungary, 4‒6 September 2019, ser. Procedia Computer Science, I. J. Rudas, J. Csirik, C. Toro, J. Botzheim, R.J. Howlett, and L.C. Jain, Eds. Elsevier, 2019, vol. 159, pp. 1811– 1820, doi: 10.1016/j.procs.2019.09.353.
  8.  J. Bazan, H. Nguyen, S. Nguyen, P. Synak, and J. Wróblewski, “Rough set algorithms in classification problem,” in Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, L. Polkowski, S. Tsumoto, and T. Lin, Eds. Heidelberg: Physica-Verlag HD, 2000, pp. 49–88, doi: 10.1007/978-3-7908-1840-6_3.
  9.  J. Bazan and M. Szczuka, “The rough set exploration system,” in Transactions on Rough Sets III, ser. Lecture Notes in Computer Science, J. F. Peters and A. Skowron, Eds. Berlin, Heidelberg: Springer, 2005, vol. 3400, pp. 37–56, doi: 10.1007/11427834_2.
  10.  I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H. Nguyen, A. Skowron, and B. Zielosko, Three Approaches to Data Analysis – Test Theory, Rough Sets and Logical Analysis of Data, ser. Intelligent Systems Reference Library. Berlin, Heidelberg: Springer, 2013, vol. 41, doi: 10.1007/978-3-642-28667-4.
  11.  Z. Pawlak and A. Skowron, “Rudiments of rough sets,” Inf. Sci., vol. 177, no. 1, pp. 3–27, 2007, doi: 10.1016/j.ins.2006.06.003.
  12.  J. Rybicki, M. Eder, and D. Hoover, “Computational stylistics and text analysis,” in Doing Digital Humanities: Practice, Training, Research, 1st ed., C. Crompton, R. Lane, and R. Siemens, Eds. Routledge, 2016, pp. 123–144, doi: 10.4324/9781315707860.
  13.  M. Eder, “Style-markers in authorship attribution a crosslanguage study of the authorial fingerprint,” Stud. Pol. Ling., vol. 6, no. 1, pp. 99–114, 2011.
  14.  H. Craig, “Stylistic analysis and authorship studies,” in A companion to digital humanities, S. Schreibman, R. Siemens, and J. Unsworth, Eds. Oxford: Blackwell, 2004, doi: 10.1002/9780470999875.ch20.
  15.  G. Baron, “Comparison of cross-validation and test sets approaches to evaluation of classifiers in authorship attribution domain,” in Proceedings of the 31st International Symposium on Computer and Inf. Sci., ser. Communications in Computer and Information Science, T. Czachórski, E. Gelenbe, K. Grochla, and R. Lent, Eds. Cracow: Springer, 2016, vol. 659, pp. 81–89, doi: 10.1007/978-3-319-47217- 1_9.
  16.  S.S. Mullick, S. Datta, S.G. Dhekane, and S. Das, “Appropriateness of performance indices for imbalanced data classification: An analysis,” Pattern Recognit., vol. 102, pp. 107–197, 2020, doi: 10.1016/j.patcog.2020.107197.
  17.  J.M. Johnson and T.M. Khoshgoftaar, “Survey on deep learning with class imbalance,” J. Big Data, vol. 6, no. 27, pp. 1–54, 2019, doi: 10.1186/s40537-019-0192-5.
  18.  N. Basurto, C. Cambra, and Á. Herrero, “Improving the detection of robot anomalies by handling data irregularities,” Neurocomputing, 2020, doi: 10.1016/j.neucom.2020.05.101, in press.
  19.  G. Shi, C. Feng,W. Xu, L. Liao, and H. Huang, “Penalized multiple distribution selection method for imbalanced data classification,” Knowledge-Based Syst., vol. 196, p. 105833, 2020, doi: 10.1016/j.knosys.2020.105833.
  20.  S. Au, R. Duan, S.G. Hesar, and W. Jiang, “A framework of irregularity enlightenment for data pre-processing in data mining,” Ann. Oper. Res., vol. 174, no. 1, pp. 47–66, 2010, doi: 10.1007/s10479-008-0494-z.
  21.  M. Koziarski, M. Wozniak, and B. Krawczyk, “Combined cleaning and resampling algorithm for multi-class imbalanced data with label noise,” Knowledge-Based Syst., vol. 204, p. 106223, 2020, doi: 10.1016/j.knosys.2020.106223.
  22.  N. Basurto, Á. Arroyo, C. Cambra, and Á. Herrero, “Imputation of missing values affecting the software performance of component-based robots,” Comput. Electr. Eng., vol. 87, p. 106766, 2020, doi: 10.1016/j.compeleceng.2020.106766.
  23.  S. Argamon, K. Burns, and S. Dubnov, Eds., The structure of style: Algorithmic approaches to understanding manner and meaning. Berlin: Springer, 2010, doi: 10.1007/978-3-642-12337-5.
  24.  S. Sbalchiero and M. Eder, “Topic modeling, long texts and the best number of topics. some problems and solutions,” Qual. Quant., vol. 54, pp. 1095–1108, 2020, doi: 10.1007/s11135-020-00976-w.
  25.  R. Peng and H. Hengartner, “Quantitative analysis of literary styles,” Am. Statistician, vol. 56, no. 3, pp. 15–38, 2002, doi: 10.1198/000313002100.
  26.  E. Stamatatos, “A survey of modern authorship attribution methods,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 3, pp. 538–556, 2009, doi: 10.1002/asi.21001.
  27.  D. Khmelev and F. Tweedie, “Using Markov chains for identification of writers,” Lit. Linguist. Comput., vol. 16, no. 4, pp. 299–307, 2001, doi: 10.1093/llc/16.3.299.
  28.  M. Koppel, J. Schler, and S. Argamon, “Computational methods in authorship attribution,” J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 1, pp. 9–26, 2009, doi: 10.1002/asi.20961.
  29.  M. Jockers and D. Witten, “A comparative study of machine learning methods for authorship attribution,” Lit. Linguist. Comput., vol. 25, no. 2, pp. 215–223, 2010, doi: 10.1093/llc/fqq001.
  30.  M. Eder and J. Rybicki, “Do birds of a feather really flock together, or how to choose training samples for authorship attribution,” Lit. Linguist. Comput., vol. 28, pp. 229–236, 8 2013, doi: 10.1093/llc/fqs036.
  31.  M. Eder, “Does size matter? Authorship attribution, small samples, big problem,” Digital Scholarsh. Humanit., vol. 30, pp. 167–182, 06 2015, doi: 10.1093/llc/fqt066.
  32.  K. Kalaivani and S. Kuppuswami, “Exploring the use of syntactic dependency features for document-level sentiment classification,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 339–347, 2019, doi: 10.24425/bpas.2019.128608.
  33.  G. Rotari, M. Jander, and J. Rybicki, “The Grimm brothers: A stylometric network analysis,” Digital Scholarsh. Humanit., 02 2020, doi: 10.1093/llc/fqz088.
  34.  C. Jankowski, D. Reda, M. Mańkowski, and G. Borowik, “Discretization of data using Boolean transformations and information theory based evaluation criteria,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 63, no. 4, pp. 923–932, 2015, doi: 10.1515/bpasts-2015-0105.
  35.  U. Fayyad and K. Irani, “Multi-interval discretization of continuous valued attributes for classification learning,” in Proceedings of the 13th International Joint Conference on Artificial Intelligence, vol. 2. Morgan Kaufmann Publishers, 1993, pp. 1022–1027.
  36.  I. Kononenko, “On biases in estimating multi-valued attributes,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence IJCAI’95, vol. 2. Morgan Kaufmann Publishers Inc., 1995, pp. 1034–1040.
  37.  J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, no. 5, pp. 465–471, 1978, doi: 10.1016/0005-1098(78)90005-5.
  38.  S. Kotsiantis and D. Kanellopoulos, “Discretization techniques: A recent survey,” GESTS Int. Trans. Comput. Sci. Eng., vol. 32, no. 1, pp. 47–58, 2006.
  39.  B. Zielosko, “Application of dynamic programming approach to optimization of association rules relative to coverage and length,” Fundamenta Informaticae, vol. 148, no. 1-2, pp. 87–105, 2016, doi: 10.3233/FI-2016-1424.
  40.  S.G. Weidman and J. O’Sullivan, “The limits of distinctive words: Re-evaluating literature’s gender marker debate,” Digital Scholarsh. Humanit., vol. 33, pp. 374–390, 2018, doi: 10.1093/llc/fqx017.
Go to article

Authors and Affiliations

Urszula Stańczyk
1
Beata Zielosko
2

  1. Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland
  2. University of Silesia in Katowice, ul. Będzińska 39, 41-200 Sosnowiec, Poland
Download PDF Download RIS Download Bibtex

Abstract

Time invariant linear operators are the building blocks of signal processing. Weighted circular convolution and signal processing framework in a generalized Fourier domain are introduced by Jorge Martinez. In this paper, we prove that under this new signal processing framework, weighted circular convolution also has a generalized time invariant property. We also give an application of this property to algorithm of continuous wavelet transform (CWT). Specifically, we have previously studied the algorithm of CWT based on generalized Fourier transform with parameter 1. In this paper, we prove that the parameter can take any complex number. Numerical experiments are presented to further demonstrate our analyses.
Go to article

Bibliography

  1.  N. Holighaus, G. Koliander, Z. Průša, and L.D. Abreu, “Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm,” IEEE Trans. Signal Process., vol. 67, no. 15, pp. 3894–3908, 2019.
  2.  M. Rayeezuddin, B. Krishna Reddy, and D. Sudheer Reddy, “Performance of reconstruction factors for a class of new complex continuous wavelets,” Int. J. Wavelets Multiresolution Inf. Process., vol. 19, no. 02, p. 2050067, 2021, doi: 10.1142/S0219691320500678.
  3.  Y. Guo, B.-Z. Li, and L.-D. Yang, “Novel fractional wavelet transform: Principles, MRA and application,” Digital Signal Process., vol. 110, p. 102937, 2021. [Online]. Available: doi: 10.1016/j.dsp.2020.102937.
  4.  V.K. Patel, S. Singh, and V.K. Singh, “Numerical wavelets scheme to complex partial differential equation arising from Morlet continuous wavelet transform,” Numer. Methods Partial Differ. Equations, vol. 37, no. 2, pp. 1163–1199, mar 2021.
  5.  C.K. Chui, Q. Jiang, L. Li, and J. Lu, “Signal separation based on adaptive continuous wavelet-like transform and analysis,” Appl. Comput. Harmon. Anal., vol. 53, pp. 151‒179, 2021.
  6.  O. Erkaymaz, I.S. Yapici, and R.U. Arslan, “Effects of obesity on time-frequency components of electroretinogram signal using continuous wavelet transform,” Biomed. Signal Process. Control, vol. 66, p. 102398, 2021.
  7.  Z. Yan, P. Chao, J. Ma, D. Cheng, and C. Liu, “Discrete convolution wavelet transform of signal and its application on BEV accident data analysis,” Mech. Syst. Signal Process., vol. 159, 2021.
  8.  R. Bardenet and A. Hardy, “Time-frequency transforms of white noises and Gaussian analytic functions,” Appl. Comput. Harmon. Anal., vol. 50, pp. 73–104, 2021, doi: 10.1016/j.acha.2019.07.003.
  9.  M.X. Cohen, “A better way to define and describe Morlet wavelets for time-frequency analysis,” NeuroImage, vol. 199, pp. 81–86, 2019. doi: 10.1016/j.neuroimage.2019.05.048.
  10.  H. Yi and H. Shu, “The improvement of the Morlet wavelet for multi-period analysis of climate data,” C.R. Geosci., vol. 344, no. 10, pp. 483–497, 2012.
  11.  S.G. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, 2009.
  12.  H. Yi, P. Ouyang, T. Yu, and T. Zhang, “An algorithm for Morlet wavelet transform based on generalized discrete Fourier transform,” Int. J. Wavelets Multiresolution Inf. Process., vol. 17, no. 05, p. 1950030, 2019, doi: 10.1142/S0219691319500309.
  13.  R. Tolimieri, M. An, and C. Lu, Algorithms for Discrete Fourier Transform and Convolution. Springer, 1997.
  14.  J.-M. Attendu and A. Ross, “Method for finding optimal exponential decay coefficient in numerical Laplace transform for application to linear convolution,” Signal Process., vol. 130, pp. 47–56, 2017.
  15.  W. Li and A.M. Peterson, “FIR Filtering by the Modified Fermat Number Transform,” IEEE Trans. Acoust. Speech Signal Process., vol. 38, no. 9, pp. 1641–1645, 1990.
  16.  M.J. Narasimha, “Linear Convolution Using Skew-Cyclic Convolutions,” Signal Process. Lett., vol. 14, no. 3, pp. 173–176, 2007.
  17.  J. Martinez, R. Heusdens, and R.C. Hendriks, “A Generalized Poisson Summation Formula and its Application to Fast Linear Convolution,” IEEE Signal Process Lett., vol. 18, no. 9, pp. 501–504, 2011.
  18.  R.C. Guido, F. Pedroso, A. Furlan, R.C. Contreras, L.G. Caobianco, and J.S. Neto, “CWT×DWT×DTWT×SDTWT: Clarifying terminologies and roles of different types of wavelet transforms,” Int. J. Wavelets Multiresolution Inf. Process., vol. 18, no. 06, p. 2030001, 2020, doi: 10.1142/S0219691320300017.
  19.  P. Kapler, “An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e136216, 2021, doi: 10.24425/bpasts.2020.136216.
  20.  J. Martinez, R. Heusdens, and R.C. Hendriks, “A generalized Fourier domain: Signal processing framework and applications,” Signal Process., vol. 93, no. 5, pp. 1259‒1267, 2013.
  21.  S. Hui and S.H. Żak, “Discrete Fourier transform and permutations,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 6, pp. 995–1005, 2019.
  22.  Z. Babic and D.P. Mandic, “A fast algorithm for linear convolution of discrete time signals,” in 5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedings of Papers (Cat. No.01EX517), vol. 2, 2001, pp. 595–598.
  23.  H. Yi, S. Y. Xin, and J. F. Yin, “A Class of Algorithms for ContinuousWavelet Transform Based on the Circulant Matrix,” Algorithms, vol. 11, no. 3, p. 24, 2018.
  24.  D. Spałek, “Two relations for generalized discrete Fourier transform coefficients,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 3, pp. 275– 281, 2018, doi: 10.24425/123433.
Go to article

Authors and Affiliations

Hua Yi
1
ORCID: ORCID
Yu-Le Ru
1
Yin-Yun Dai
1

  1. School of Mathematics and Physics, Jinggangshan University, Ji’an, 343009, P.R. China
Download PDF Download RIS Download Bibtex

Abstract

Workflow Scheduling is the major problem in Cloud Computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).
Go to article

Bibliography

  1. A.M. Manasrah and H. Ba Ali, “Workflow scheduling using hybrid GA-PSO algorithm in cloud computing,” Wireless Commun. Mob. Comput., vol. 15, no. 3, pp. 1–16, 2018, doi: 10.1155/2018/1934784.
  2.  S.G. Ahmad, C.S. Liew, E.U. Munir, A.T. Fong, and S.U. Khan, “A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems,” J. Parallel Distrib. Comput., vol. 87, no. 2, pp. 80–90, 2016, doi: 10.1016/j. jpdc.2015.10.001.
  3.  H. Alaskar, A. Hussain, N. Al-Aseem, P. Liatsis, and D. Al-Jumeily, “Application of convolutional neural networks for automated ulcer detection in wireless capsule endoscopy images,” Sensors., vol. 19, no. 6, pp. 1265–1281, 2019, doi: 10.3390/s19061265.
  4.  L. Teylo, L. Arantes, P. Sens, and L.M.A. Drummond, “A dynamic task scheduler tolerant to multiple hibernations in cloud environments,” J. Cluster Comput., vol. 21, no. 5, pp. 1–23, 2020, doi: 10.1007/s10586-020-03175-2.
  5.  M. Sardaraz and M. Tahir, “A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing,” Int. J. Distri. Sensor Net., vol. 16, no. 8, pp. 1–10, 2020, doi: 10.1177/1550147720949142.
  6.  M. Hosseinzadeh, M.Y. Ghafour, and H.K. Hama, “Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review,” J. Grid Comput., vol. 18, no. 3, pp. 327–356, 2020, doi: 10.1007/s10723-020-09533-z.
  7.  C.L. Chen, M.L. Chiang, and C.B. Lin, “The high performance of a task scheduling algorithm using reference queues for cloud- computing data centers,” Electronics, . vol. 9, no. 1, pp. 371–379, 2020, doi: 10.3390/electronics9020371.
  8.  Y. Hu, H. Wang, and W. Ma, “Intelligent Cloud workflow management and scheduling method for big data applications,” J. Cloud Comp, vol. 9, no. 1, pp. 1–13, 2020, doi: 10.1186/s13677-020-00177-8.
  9.  M. Grochowski, A. Kwasigroch, and A. Mikołajczyk,” Selected technical issues of deep neural networks for image classification purposes,” Bull. Polish Acad. Sci. Tech. Sci., vol. 67, no. 2, pp. 363–376, 2019, doi: 10.24425/bpas.2019.128485.
  10.  A.A. Nasr, N.A. El-Bahnasawy, and G. Attiya, “Cost-effective algorithm for workflow scheduling in cloud computing under deadline constraint,” Arab. J. Sci. Eng., vol.44, no. 4, pp. 3765–3780, 2019, doi: 10.1007/s13369-018-3664-6.
  11.  Z. Swiderska-Chadaj, T. Markiewicz, J. Gallego, G. Bueno, B. Grala, and M. Lorent, “Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model,” Bull. Polish Acad. Sci. Tech. Sci., vol. 66, no. 6, pp. 849–856, 2018, doi: 10.24425/bpas.2018.125932.
  12.  Y. Cui and Z. Xiaoqing, “Workflow tasks scheduling optimization based on genetic algorithm in Clouds,” in IEEE 3rd Int. Conf. on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, 2018, pp. 6–10, doi: 10.1109/ICCCBDA.2018.8386458.
  13.  T. Wang, Z. Liu, Y. Chen, Y. Xu, and X. Dai, “Load balancing task scheduling based on Genetic algorithm in Cloud Computing,” in IEEE 12th Int. Conf. on Dependable, Autonomic and Secure Computing, Dalian, 2014, pp. 146–152, doi: 10.1109/DASC.2014.35.
  14.  X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan, “A deep learning model integrating FCNNs and CRFs for brain tumor segmentation,” Med. Imag. Anal., vol. 43, no. 4, pp.  98–111, 2018, doi: 10.1016/j.media.2017.10.002.
  15.  C. Liu, R. Zhao, W. Xie, and M. Pang, “Pathological lung segmentation based on random forest combined with deep model and multi- scale superpixels,” Neu. Proc, Let., vol. 52, no. 2, pp. 1631–1649, 2020, doi: 10.1007/s11063-020-10330-8.
  16.  C. Zhang, J. Zhao, Ji. Niu, and D. Li, “New convolutional neural network model for screening and diagnosis of mammograms,” PLoS One., vol. 15, no. 8, pp. 67–80, 2020, doi: 10.1371/journal.pone.0237674.
  17.  L.D. Nicanor, H.R. Orozco Aguirre, and V.M. Landassuri Moreno, “An assessment model to establish the use of services resources in a cloud computing scenario,” J. High Perf. Vis. Int., vol. 12, no. 5, pp. 83–100, 2020, doi: 10.1007/978-981-15-6844-2_7.
  18.  V. Magudeeswaran and J. Fenshia Singh, “Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images,” Int. J. Imag. Sys. and Tech., vol. 27, no. 1, pp. 98–103, 2017, doi: 10.1002/ima.22214.
  19.  S.P. Cleary and J.S. Prell, “Liberating native mass spectrometry from dependence on volatile salt buffers by use of Gábor transform,” Int. J. Imag. Syst. Tech., vol. 20, no. 4, pp. 519–523, 2019, doi: 10.1002/cphc.201900022.
  20.  V. Srivastava, R.K. Purwar, and A. Jain, “A dynamic threshold‐based local mesh ternary pattern technique for biomedical image retrieval,” Int. J. Imag. Sysy. Tech., vol.29, no. 2, pp. 168–179, 2019, doi: 10.1002/ima.22296.
  21.  J.H. Johnpeter and T. Ponnuchamy, “Computer aided automated detection and classification of brain tumors using CANFIS classification method,” Int. J. Imag. Sysy. Tech., vol.29, no. 4, pp. 431–438, 2019, doi: 10.1002/ima.22318.
  22.  N. Kumar and D. Kumar, “Classification using artificial neural network optimized with bat algorithm”, Int. J. Innovative Tech. Exploring Eng. (IJITEE), vol. 9, no. 3, pp.  696–700, 2020, doi: 10.35940/ijitee.C8378.019320.
  23.  A.S. Mahboob and S.H. Zahiri, “Automatic and heuristic complete design for ANFIS classifier, network: computation in neural systems,” J. Net. Comput. Neu. Syst., vol. 30, no. 4, pp. 31–57, 2019, doi: 10.1080/0954898X.2019.1637953.
  24.  C. Shorten and T.M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big. Data., vol.  60, no. 6, pp. 1–48, 2019, doi: 10.1186/s40537-019-0197-0.
Go to article

Authors and Affiliations

P. Tharani
1
A.M. Kalpana
1

  1. Department of Computer Science and Engineering, Government College of Engineering, Salem-636011, Tamil Nadu, India
Download PDF Download RIS Download Bibtex

Abstract

In the paper the new paradigm for structural optimization without volume constraint is presented. Since the problem of stiffest design (compliance minimization) has no solution without additional assumptions, usually the volume of the material in the design domain is limited. The biomimetic approach, based on trabecular bone remodeling phenomenon is used to eliminate the volume constraint from the topology optimization procedure. Instead of the volume constraint, the Lagrange multiplier is assumed to have a constant value during the whole optimization procedure. Well known MATLAB topology based optimization code, developed by Ole Sigmund, was used as a tool for the new approach testing. The code was modified and the comparison of the original and the modified optimization algorithm is also presented. With the use of the new optimization paradigm, it is possible to minimize the compliance by obtaining different topologies for different materials. It is also possible to obtain different topologies for different load magnitudes. Both features of the presented approach are crucial for the design of lightweight structures, allowing the actual weight of the structure to be minimized. The final volume is not assumed at the beginning of the optimization process (no material volume constraint), but depends on the material’s properties and the forces acting upon the structure. The cantilever beam example, the classical problem in topology optimization is used to illustrate the presented approach.
Go to article

Bibliography

  1.  W. Wang et al., “Space-time topology optimization for additive manufacturing”, Struct. Multidiscip. Optim., vol. 61, no. 1, pp. 1‒18, 2020, doi: 10.1007/s00158-019-02420-6.
  2.  Y. Saadlaoui, et al., “Topology optimization and additive manufacturing: Comparison of conception methods using industrial codes”, J. Manuf. Syst., vol. 43, pp. 178‒286, 2017, doi: 10.1016/j.jmsy.2017.03.006.
  3.  J. Zhu, et al., “A review of topology optimization for additive manufacturing: Status and challenges”, Chin. J. Aeronaut., vol. 34, no. 1, pp. 9‒110, 2021, doi: 10.1016/j.cja.2020.09.020.
  4.  O. Sigmund, “A 99 line topology optimization code written in Matlab”, Struct. Multidiscip. Optim., vol. 21, no. 2, pp. 120‒127, 2001, doi: 10.1007/s001580050176.
  5.  M. Bendsoe and O. Sigmund, Topology optimization. Theory, methods and applications, Berlin Heidelberg New York, Springer, 2003, doi: 10.1007/978-3-662-05086-6.
  6.  M. Bendsoe and N. Kikuchi, “Generating optimal topologies in structural design using a homogenization method”, Comput. Methods Appl. Mech. Eng., vol. 71, pp. 197‒224, 1988.
  7.  O. Sigmund and K. Maute, “Topology optimization approaches”, Struct. Multidiscip. Optim., vol. 48, pp. 1031‒1055, 2013, doi: 10.1007/ s00158‒013‒0978‒6.
  8.  Z. Ming and R. Fleury, “Fail-safe topology optimization”, Struct. Multidiscip. Optim., vol. 54, no. 5, pp. 1225‒1243, 2016, doi: 10.1007/ s00158-016-1507-1.
  9.  L. Krog et al., “Topology optimization of aircraft wing box ribs”, AIAA Paper, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, 2004, doi: 10.2514/6.2004-4481.
  10.  Z. Luo et al., “A new procedure for aerodynamic missile designs using topological optimization approach of continuum structures”, Aerosp. Sci. Technol., vol. 10, pp. 364‒373, 2006, doi: 10.1016/j.ast.2005.12.006.
  11.  M. Zhou et al., “Industrial application of topology optimization for combined conductive and convective heat transfer problems”, Struct. Multidiscip. Optim., vol. 54, no 4, pp. 1045‒1060, 2016, doi: 10.1007/s00158-016-1433-2.
  12.  G. Allaire et al., “The homogenization method for topology optimization of structures: old and new”, Interdiscip. Inf. Sci., vol.25/2, pp. 75‒146, 2019, doi: 10.4036/iis.2019.B.01.
  13.  G. Allaire and R.V. Kohn, “Topology Optimization and Optimal Shape Design Using Homogenization”, Topology Design of Structures. NATO ASI Series – Series E: Applied Sciences, M. Bendsoe, C. Soares – eds., vol. 227, pp. 207‒218, 1993, doi: 10.1007/978-94-011- 1804-0_14.
  14.  G. Allaire et al., ”Shape optimization by the homogenization method”, Numer. Math., vol. 76, no. 1, pp. 27‒68, 1997, doi: 10.1007/ s002110050253.
  15.  G. Allaire, Shape Optimization by the Homogenization Method, Springer, 2002, doi: 10.1007/978-1-4684-9286-6.
  16.  J. Wolff, “The Classic: On the Inner Architecture of Bones and its Importance for Bone Growth”, Clin. Orthop. Rel. Res., vol. 468, no. 4, pp. 1056‒1065, 2010, doi: 10.1007/s11999-010-1239-2.
  17.  H. M. Frost, The Laws of Bone Structure, C.C. Thomas, Springfield, 1964.
  18.  R. Huiskes et al., ”Adaptive bone-remodeling theory applied to prosthetic-design analysis”, J. Biomech., vol. 20, pp. 1135‒1150, 1987.
  19.  R. Huiskes, “If bone is the answer, then what is the question?”, J. Anat., vol. 197, no. 2, pp. 145‒156, 2000.
  20.  D.R. Carter, “Mechanical loading histories and cortical bone remodeling”, Calcif. Tissue Int., vol. 36, no. Suppl. 1, pp. 19‒24, 1984, doi: 10.1007/BF02406129.
  21.  R.F.M. van Oers, R. Ruimerman, E. Tanck, P.A.J. Hilbers, R. Huiskes, “A unified theory for osteonal and hemi-osteonal remodeling”, Bone, vol. 42, no. 2, pp. 250‒259, 2008, doi: 10.1016/j.bone.2007.10.009.
  22.  M. Nowak, J. Sokołowski, and A. Żochowski, “Justification of a certain algorithm for shape optimization in 3D elasticity”, Struct. Multidiscip. Optim., vol. 57, no. 2, pp. 721‒734, 2018, doi: 10.1007/s00158-017-1780-7.
  23.  M. Nowak, J. Sokołowski, and A. Żochowski, “Biomimetic approach to compliance optimization and multiple load cases”, J. Optim. Theory Appl., vol. 184, no. 1, pp. 210‒225, 2020, doi: 10.1007/s10957-019-01502-1.
  24.  J. Sokołowski and J-P. Zolesio, Introduction to Shape Optimization. Shape Sensitivity Analysis, Springer-Verlag, 1992, doi: 10.1007/978- 3-642-58106-9.
  25.  D. Gaweł et al., “New biomimetic approach to the aircraft wing structural design based on aeroelastic analysis”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 5, pp. 741‒750, 2017, doi: 10.1515/bpasts-2017-0080.
Go to article

Authors and Affiliations

Michał Nowak
1
ORCID: ORCID
Aron Boguszewski
1

  1. Poznan University of Technology, Division of Virtual Engineering, ul. Jana Pawła II 24, 60-965 Poznań, Poland
Download PDF Download RIS Download Bibtex

Abstract

Appropriate modeling of unsteady aerodynamic characteristics is required for the study of aircraft dynamics and stability analysis, especially at higher angles of attack. The article presents an example of using artificial neural networks to model such characteristics. The effectiveness of this approach was demonstrated on the example of a strake-wing micro aerial vehicle. The neural model of unsteady aerodynamic characteristics was identified from the dynamic test cycles conducted in a water tunnel. The aerodynamic coefficients were modeled as a function of the flow parameters. The article presents neural models of longitudinal aerodynamic coefficients: lift and pitching moment as functions of angles of attack and reduced frequency. The modeled and trained aerodynamic coefficients show good consistency. This method manifests great potential in the construction of aerodynamic models for flight simulation purposes
Go to article

Bibliography

  1.  C. Galiński and R. Żbikowski, “Some problems of micro air vehicles development,” Bull. Polish Acad. Sci. Tech. Sci., vol. 55, no. 1, pp. 91–98, 2007.
  2.  K. Sibilski, M. Nowakowski, D. Rykaczewski, P. Szczepaniak, A. Żyluk, A. Sibilska-Mroziewicz, M. Garbowski, and W. Wróblewski, “Identification of fixed-wing micro aerial vehicle aerodynamic derivatives from dynamic water tunnel tests,” Aerospace, vol. 7, no. 8, p. 116, 2020, doi: 10.3390/aerospace7080116.
  3.  K. Sibilski, M. Lasek, A. Sibilska-Mroziewicz, and M. Garbowski, Dynamcs of Flight of Fixed Wings Micro Aerial Vehicles, Publishing House of the Warsaw University of Technology, Warsaw, 2020.
  4.  M. Abdulrahim, S. Watkins, R. Segal, M. Marino, and J. Sheridan, “Dynamic sensitivity to atmospheric turbulence of fixed-wing UAV with varying configuration,” J. Aircaft, vol. 47, no. 6, pp. 1873–1883, 2010, doi: 10.2514/1.46860.
  5.  A.N. Kolmogorov, “On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition,” Dokl. Akad. Nauk SSSR, vol. 114, no. 5, pp. 953–956, 1957, [in Russian].
  6.  W.E. Faller, S.J. Schreck, and H.E. Helin, “Real-time model of three dimensional dynamic reattachment using neural networks,” J. Aircraft, vol. 32, no. 6, pp. 1177–1182, 1995, doi: 10.2514/3.46861.
  7.  W.F. Faller and S.J. Schreck, “Unsteady fluid mechanics applications of neural networks,” J. Aircraft, vol. 34, no. 1, pp. 48–55, 1997, doi: 10.2514/2.2134.
  8.  M. Kerho and B. Kramer, Research water tunnels – specification, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2003.
  9.  M. Kerho and B. Kramer, Five-component balance and computer-controlled model support system for water tunnel applications, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2009.
  10.  M. Kerho and B. Kramer, Ultrasonic flowmeter and temperature probe, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2010.
  11.  P.H. Reisenthel, “Development of nonlinear indicial model using response functions generated by a neural network,” in Proceedings of the 35th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 6–9 January 1997, p. AIAA 97‒0337, doi: 10.2514/6.1997-337.
  12.  S. Hitzel and D. Zimper, “Wind tunnel simulation and ‘Real’ flight of advanced combat aircraft: industrial perspective,” J. Aircraft, vol. 55, no. 2, pp. 587–602, 2018, doi: 10.2514/1.C033696.
  13.  D. Rohlf, S. Schmidt, and J. Irving, “Stability and control analysis for an unmanned aircraft configuration using system-identification techniques,” J. Aircraft, vol. 49, no. 6, pp. 1597–1609, 2012, doi: 10.2514/1.C031392.
  14.  D.J. Ignatyev and A.N. Khrabrov, “Neural network modelling of unsteady aerodynamic characteristics at high angles of attack,” Aerospace Sci. Technol., vol. 41, pp. 106–115, 2015, doi: 10.1016/j.ast.2014.12.017.
  15.  D. Ignatyev and A. Khrabrov, “Experimental study and neural network modeling of aerodynamic characteristics of canard aircraft at high angles of attack,” Aerospace, vol. 5, no. 1, p. 26, 2018, doi: 10.3390/aerospace5010026.
  16.  P.C. Murphy, V. Klein, and N.T. Frink, “Nonlinear unsteady aerodynamic modeling using wind-tunnel and computational data,” J. Aircraft, vol. 54, no. 2, pp. 659–683, 2017, doi: 10.2514/1.C033881.
  17.  P. Murphy, V. Klein, and N. Szyba, “Progressive aerodynamic model identification from dynamic water tunnel test of the F-16XL aircraft,” in Proceedings of the AIAA Atmospheric Flight Mechanics Conference and Exhibit, Guidance, Navigation, and Control and Co-located Conferences, 2004, Providence, RI, USA, p. AIAA 2004–5727, doi: 10.2514/6.2004-5277.
  18.  B. Paprocki, A. Pregowska, and J. Szczepański, “Optimizing information processing in brain-inspired neural networks,” Bull. Polish Acad. Sci. Tech. Sci., vol. 8, no. 2, pp. 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  19.  W.S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull. Math. Biophys., vol. 5, no. 4, pp. 115– 133, 1943, doi: 10.1007/BF02478259.
  20.  J. Hertz, A. Krogh, and R. Palmer, Introduction to the theory of neural computation, CRC Press, Taylor & Francis Inc., London – N-York, 1991.
  21.  R.A. Kosiński, Artificial neural networks. non-linear dynamics and chaos, PWN, Warszawa, 2017.
  22.  S. Osowski, Neural networks for information processing, 4th Edition, Publishing House of the Warsaw University of Technology, Warsaw, 2020.
  23.  R. Tadeusiewicz, Neural networks, Akademic Publisching House, Warsaw, 1993.
  24.  K. Diamantaras and S. Kung, Principal component neural networks, theory and application, J. Wiley, New York, 1996.
  25.  R. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Mag., vol. 4, no. 2, pp. 4–22, 1987, doi: 10.1109/ MASSP.1987.1165576.
  26.  K.S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural network”, IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4–27, 1990, doi: 10.1109/72.80202.
  27.  A. Cichocki and R. Unbehauen, “Neural networks for solving systems of linear equations and related problems,” IEEE Trans. Circuits Syst. I: Fundam. Theory Appl., vol. 39, no. 2, pp. 124–138, 1992, doi: 10.1109/81.167018.
  28.  J. Denoeux and R. Lengalle, “Initialising back propagation networks with prototypes,” Neural Networks, vol. 6, no. 3, pp. 351–363, 1993, doi: 10.1016/0893-6080(93)90003-F.
  29.  E. Karnin, “A simple procedure for pruning backpropagation trained neural networks,” IEEE Trans. Neural Networks, vol. 1, no. 2, pp. 239–242, 1990, doi: 10.1109/72.80236.
  30.  J. Manerowski and D. Rykaczewski, “Modelling of UAV flight dynamics using perceptron artificial neural networks,” J. Theor. App. Mech., vol. 43, no. 2, pp. 297–307, 2005.
  31.  R. Barron, “Approximation and estimation bounds for artificial neural networks,” Machine Learning, vol. 14, pp. 115–133, 1994, doi: 10.1007/BF00993164.
  32.  J.F. Horn, A.J. Calise, and J.V.R. Prasad, “Flight Envelope Cueing on a Tilt-Rotor Aircraft Using Neural Network Limit Prediction,” J. Amer. Helic. Soc., vol. 46, no. 1, pp. 23–31, 2001, doi: 10.4050/JAHS.46.23.
  33.  T. Cepowski and T. Szelangiewicz, “Application of Artificial Neural Networks to investigations of ship seakeeping ability,” Pol. Marit. Res., vol. 8, no. 3, pp. 11–15, 2001.
  34.  T. Mueller, “Aerodynamic Measurements at Low Reynolds Number for Fixed Wing Micro-Air Vehicles,” in AVT/VKI Special Course on Development and Operation of UAVs for Military and Civil Applications, NATO/VKI, Brussel, Belgium, 1999.
  35.  Dong Sun, Huaiyu Wu, Rong Zhu, and Ling Che Hung, “Development of Micro Air Vehicle Based on Aerodynamic Modeling Analysis in Tunnel Tests,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, pp. 2235–2240, doi: 10.1109/ROBOT.2005.1570445.
  36.  R. Randall, S. Shkarayev, G. Abate, and J. Babcock, “Longitudinal aerodynamics of rapidly pitching fixed-wing Micro Air Vehicles,” J. Aircraft, vol. 49, no. 2, pp. 453–471, 2012, doi: 10.2514/1.C031378.
  37.  C. Tongchitpakdee, W. Hlusriyakul, C. Pattanathummasid, and C. Thipyopas, “Aerodynamic investigation and analysis of wingtip thickness’s effect on low aspect ratio wing,” in Proc. International Micro Air Vehicle Conference and Flight Competition (IMAV2013), Toulouse, France, 2013.
  38.  J-M. Moschetta, “The aerodynamics of micro air vehicles: technical challenges and scientific issues,” Int. J. Eng. Sys. Model. Sim., vol. 6, no. 3/4, pp. 134–148, 2014, doi: 10.1504/IJESMS.2014.063122.
  39.  D. Viieru, J. Tang, Y. Lian, H. Liu, and W. Shy, “Flapping and flexible wing aerodynamics of low Reynolds number flight vehicles,” in Proc. 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 2006, p. AIAA 2006–503, doi: 10.2514/6.2006-503.
  40.  D. Gyllhem, K. Mohseni, and D. Lawrence, “Numerical simulation of flow around the Colorado Micro Aerial Vehicle,” in Proceedings 35th AIAA Fluid Dynamics Conference and Exhibit, Toronto, Canada, 2005, p. AIAA 2005–4757, doi: 10.2514/6.2005-4757.
  41.  V.V. Golubev and M,R. Visbal, “Modeling MAV response in gusty urban environment,” Int. J. Micro Air Veh., vol. 4, no. 1, pp. 79–92, 2012, doi: 10.1260/1756-8293.4.1.79.
  42.  R. Cory and R. Tedrake, “Experiments in fixed-wing UAV perching,” in Proceedings AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hi, USA, 2008, p. AIAA 2008–7256, doi: 10.2514/6.2008-7256.
  43.  D.V. Uhlig and M.S. Selig, “Stability characteristics of Micro Air Vehicles from experimental measurements,” in Proc. 29th AIAA Applied Aerodynamics Conference, Honolulu, HI, USA, 2011, p. AIAA 2011–3659. doi: 10.2514/6.2011-3659.
Go to article

Authors and Affiliations

Dariusz Rykaczewski
ORCID: ORCID
Mirosław Nowakowski
ORCID: ORCID
Krzysztof Sibilski
ORCID: ORCID
Wiesław Wróblewski
ORCID: ORCID
Michał Garbowski
Download PDF Download RIS Download Bibtex

Abstract

The objective of the research was to investigate the efficiency of selected methods of data fusion from visual sensors used on-board satellites for attitude measurements. Data from a sun sensor, an earth sensor, and a star tracker were fused, and selected methods were applied to calculate satellite attitude. First, a direct numerical solution, a numerical and analytical solution of the Wahba problem, and the TRIAD method for attitude calculation were compared used for integrating data produced by a sun sensor and an earth sensor. Next, attitude data from the star tracker and earth/sun sensors were integrated using two methods: weighted average and Kalman filter. All algorithms were coded in the MATLAB environment and tested using simulation models of visual sensors. The results of simulations may be used as an indication for the best data fusion in real satellite systems. The algorithms developed may be extended to incorporate other attitude sensors like inertial and/or GNSS to form a complete satellite attitude system.
Go to article

Bibliography

  1.  E. Babcock, “CubeSat Attitude Determination via Kalman Filtering of Magnetometer and Solar Cell Data,” in 25th AIAA/USU Conference on Small Satellites, 2011, [Online]. Available: https://digitalcommons.usu.edu/smallsat/2011/all2011/56/.
  2.  M. Fakhari Mehrjardi, H. Sanusi, Mohd.A.Mohd. Ali, and M.A. Taher, “Three-Axis Attitude Estimation Of Satellite Through Only Two- Axis Magnetometer Observations Using LKF Algorithm,” Metrol. Meas. Syst., vol. 22, no. 4, pp. 577–590, 2015, [Online]. Available: https://journals.pan.pl/dlibra/publication/104365/edition/90368.
  3.  T. Nguyen, K. Cahoy, and A. Marinan, “Attitude Determination for Small Satellites with Infrared Earth Horizon Sensors,” J. Spacecr. Rockets, vol. 55, no. 6, pp. 1466– 1475, 2018, doi: 10.2514/1.A34010.
  4.  Y.T. Chiang, F.R. Chang, L.S. Wang, Y.W. Jan, and L.H. Ting, “Data fusion of three attitude sensors,” in SICE 2001. Proceedings of the 40th SICE Annual Conference. International Session Papers (IEEE Cat. No.01TH8603), 2002, pp. 234–239, doi: 10.1109/SICE.2001.977839.
  5.  H. Kim, J. Hong, W. Park, and C. Ryoo, “Satellite celestial navigation using star-tracker and earth sensor,” in 2015 15th International Conference on Control, Automation and Systems (ICCAS), Oct. 2015, pp. 461–465, doi: 10.1109/ICCAS.2015.7364961.
  6.  L. Yuqing, Y. Tianshe, L. Jian, F. Na, and W. Guan, “A fault diagnosis method by multi sensor fusion for spacecraft control system sensors,” in 2016 IEEE International Conference on Mechatronics and Automation, Aug. 2016, pp. 748–753, doi: 10.1109/ICMA.2016.7558656.
  7.  F.L. Markley, “Attitude Determination Using Two Vector Measurements,” 1998. [Online]. Available: https://ntrs.nasa.gov/search. jsp?R=19990052720.
  8.  J.J. Moré, “The Levenberg-Marquardt algorithm: Implementation and theory,” in Numer. Anal., vol. 630, 1978, pp. 105–116.
  9.  A. Forsgren, P.E. Gill, and M.H. Wright, “Interior Methods for Nonlinear Optimization,” SIAM Rev., vol. 44, no. 4, pp. 525–597, Jan. 2002, doi: 10.1137/S0036144502414942.
  10.  E.B. Dam, M. Koch, and M. Lillholm, “Quaternions, Interpolation and Animation,” Copenhagen, 1998. [Online]. Available: https://web. mit.edu/2.998/www/QuaternionReport1.pdf.
Go to article

Authors and Affiliations

Janusz Narkiewicz
1
Mateusz Sochacki
1
Adam Rodacki
1
Damian Grabowski
1

  1. Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, Institute of Aeronautics and Applied Mechanics, ul. Nowowiejska 24, 00-665 Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

In this investigation, high specific strength precipitation hardenable alloy AA7068-T6 was joined using friction stir welding. Experiments were carried out using the three factor-three level central composite face-centered design of response surface methodology. Regression models were developed to assess the influence of tool rotational speed, welding speed, and axial force on ultimate tensile strength and elongation of the fabricated joints. The validity of the developed models was tested using the analysis of variance (ANOVA), actual and adjusted values of the regression coefficients, and experimental trials. The analysis of the developed models together with microstructural studies of typical cases showed that the tool rotational speed and welding speed have a significant interaction effect on the tensile strength and elongation of the joints. However, the axial force has a relatively low interaction effect with tool rotational speed and welding speed on the strength and elongation of the joints. The process variables were optimized using the desirability function analysis. The optimized values of joint tensile strength and elongation – 516 MPa and 21.57%, respectively were obtained at a tool rotational speed of 1218 rpm, welding speed of 47 mm/ min, and an axial force of 5.3 kN.
Go to article

Bibliography

  1.  A.M. Khalil, I.S. Loginova, A.V. Pozdniakov, A.O. Mosleh, and A.N. Solonin, “Evaluation of the Microstructure and Mechanical Properties of a New Modified Cast and Laser-Melted AA7075 Alloy,” Materials, vol. 12, no. 20, 2019. [Online]. Available: https://www.mdpi. com/1996-1944/12/20/3430.
  2.  M. Minnicino, D. Gray, and P. Moy, “Aluminum alloy 7068 mechanical characterization,” Army Research Lab Aberdeen Proving Ground MD Weapons and Materials Research, Tech. Rep., 2009.
  3.  R.S. Mishra and Z. Ma, “Friction stir welding and processing,” Mater. Sci. Eng., R, vol. 50, no. 1‒2, pp. 1–78, 2005.
  4.  M. Mohammadi-pour, A. Khodabandeh, S. Mohammadipour, and M. Paidar, “Microstructure and mechanical properties of joints welded by friction-stir welding in aluminum alloy 7075-T6 plates for aerospace application,” Rare Met., pp. 1–9, 2016.
  5.  P. Goel, A.N. Siddiquee, N.Z. Khan, M.A. Hussain, Z.A. Khan, M.H. Abidi, and A. Al-Ahmari, “Investigation on the effect of tool pin profiles on mechanical and microstructural properties of friction stir butt and scarf welded aluminium alloy 6063,” Metals, vol. 8, no. 1, p. 74, 2018.
  6.  N. Martinez, N. Kumar, R. Mishra, and K. Doherty, “Effect of tool dimensions and parameters on the microstructure of friction stir welded aluminum 7449 alloy of various thicknesses,” Mater. Sci. Eng. A, vol. 684, pp. 470–479, 2017.
  7.  W. Xu, H. Wang, Y. Luo, W. Li, and M. Fu, “Mechanical behavior of 7085-T7452 aluminum alloy thick plate joint produced by double- sided friction stir welding: Effect of welding parameters and strain rates,” J. Manuf. Processes, vol. 35, pp. 261–270, 2018.
  8.  M. Mehta, A. Arora, A. De, and T. DebRoy, “Tool geometry for friction stir welding – optimum shoulder diameter,” Metall. Mater. Trans. A, vol. 42, no. 9, pp. 2716–2722, 2011.
  9.  M. Jayaraman, R. Sivasubramanian, V. Balasubramanian, and A. Lakshminarayanan, “Application of RSM and ANN to predict the tensile strength of Friction StirWelded A319 cast aluminium alloy,” Int. J. Manuf. Res., vol. 4, no. 3, pp. 306–323, 2009.
  10.  S. Jannet, P. Mathews, and R. Raja, “Comparative investigation of friction stir welding and fusion welding of 6061 T6-5083 O aluminum alloy based on mechanical properties and microstructure,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 62, no. 4, 2014.
  11.  K. Deepandurai and R. Parameshwaran, “Multiresponse optimization of FSW parameters for cast AA7075/SiCp composite,” Mater. Manuf. Processes, vol. 31, no. 10, pp. 1333–1341, 2016.
  12.  M.M. Krishnan, J. Maniraj, R. Deepak, and K. Anganan, “Prediction of optimum welding parameters for FSW of aluminium alloys AA6063 and A319 using RSM and ANN,” Mater. Today: Proc., vol. 5, no. 1, pp. 716–723, 2018.
  13.  M. Vahdati, M. Moradi, and M. Shamsborhan, “Modeling and Optimization of the Yield Strength and Tensile Strength of Al7075 Butt Joint Produced by FSW and SFSW Using RSM and Desirability Function Method,” Trans. Indian Inst. Met., vol. 73, no. 10, pp. 2587–2600, 2020.
  14.  G. Derringer and R. Suich, “Simultaneous optimization of several response variables,” J. Qual. Technol., vol. 12, no. 4, pp. 214–219, 1980.
  15.  G. Kumar, R. Kumar, and R. Kumar, “Optimization of process parameters of friction stir welded AA5082- AA7075 butt joints using resonance fatigue properties,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 1, 2020, doi: 10.24425/bpasts.2020.131830.
  16.  A.R. Rose, K. Manisekar, and V. Balasubramanian, “Effect of axial force on microstructure and tensile properties of friction stir welded AZ61A magnesium alloy,” Trans. Nonferrous Met. Soc. China, vol. 21, no. 5, pp. 974–984, 2011.
  17.  K. Jata, K. Sankaran, and J. Ruschau, “Friction-stir welding effects on microstructure and fatigue of aluminum alloy 7050-T7451,” Metall. Mater. Trans. A, vol. 31, no. 9, pp. 2181–2192, 2000.
  18.  F. Viana, A. Pinto, H. Santos, and A. Lopes, “Retrogression and re-ageing of 7075 aluminium alloy: microstructural characterization,” J. Mater. Process. Technol., vol. 92, pp. 54–59, 1999.
  19.  D. Godard, P. Archambault, E. Aeby-Gautier, and G. Lapasset, “Precipitation sequences during quenching of the AA 7010 alloy,” Acta Mater., vol. 50, no. 9, pp. 2319– 2329, 2002.
  20.  A.P. Reynolds, W. Tang, Z. Khandkar, J.A. Khan, and K. Lindner, “Relationships between weld parameters, hardness distribution and temperature history in alloy 7050 friction stir welds,” Sci. Technol. Weld. Joining, vol. 10, no. 2, pp. 190–199, 2005.
  21.  V.S. Gadakh and K. Adepu, “Heat generation model for taper cylindrical pin profile in fsw,” J. Mater. Res. Technol., vol. 2, no. 4, pp. 370–375, 2013.
  22.  K.K. Ramachandran, N. Murugan, and S.S. Kumar, “Performance analysis of dissimilar friction stir welded aluminium alloy AA5052 and HSLA steel butt joints using response surface method,” Int. J. Adv. Manuf. Technol, vol. 86, no. 9, pp. 2373–2392, 2016.
Go to article

Authors and Affiliations

M.D. Bindu
1
P.S. Tide
1
A.B. Bhasi
1
K.K. Ramachandran
2

  1. Division of Mechanical Engineering, Cochin University of Science and Technology, Kerala, India
  2. Department of Mechanical Engineering, Government Engineering College, Trissur, Kerala, India
Download PDF Download RIS Download Bibtex

Abstract

The article deals with the technological principles regarding the final drying process of the porous ammonium nitrate (PAN) granules in multistage gravitational shelf dryers. The data on the dryer’s optimal technological operating modes are obtained. PAN samples are studied; the regularity of the porous structure change in the granule depending on the dryer’s hydrodynamic and thermodynamic conditions is established. Experimental data obtained during the research will be used to create a methodology for the engineering calculation of gravitational shelf dryers. Moreover, the data on the optimal operating conditions of the drying machines at the final drying stage will be used to improve the technology to form porous granules from agricultural ammonium nitrate.
Go to article

Bibliography

  1.  T.J. Janssen, Explosive materials: classification, composition and properties, Nova Science Publishers, Inc., New York, 2011.
  2.  Patent No. 5540793 US: Porous prilled ammonium nitrate, 1996.
  3.  Patent No. 2118074, CA: Porous prilled ammonium nitrate, 2002.
  4.  Patent No. 2093727, CA: Hardened porous ammonium nitrate, 2004.
  5.  Patent No. 2004‒256365, JP: Method of manufacturing porous granular ammonium nitrate, 2004.
  6.  Patent No. 2005‒350276, JP: Method for producing porous granular ammonium nitrate, 2005.
  7.  Patent No. 2221717, CA: Procedure and installation for the manufacture of porous ammonium nitrate, 2005.
  8.  Patent No. 102093146, CN: Microporous granular ammonium nitrate and preparation methods thereof, 2011.
  9.  Patent No. 102173968, CN: Production method of porous granular ammonium nitrate, 2011.
  10.  Patent No. 2452719, RU: Device for production of porous granulated ammonium nitrate and method for production of porous granulated ammonium nitrate, 2012.
  11.  Patent No. 391973, PL: Method for producing granulated porous ammonium nitrate, 2012.
  12.  Patent No. 103896695, CN: Microporous pelletal ammonium nitrate and preparation method thereof, 2014.
  13.  Patent No. 204384319, CN: Device for producing porous ammonium nitrate and industrial ammonium nitrate, 2015.
  14.  Patent No. 204237724, CN: Recycling device for caked ammonium nitrate during production of porous ammonium nitrate, 2015.
  15.  Patent No. 104311372, CN: Porous ammonium nitrate production caking ammonium nitrate recycling apparatus and method of use, 2016.
  16.  Patent No. 106316727 CN: Porous and granular ANFO (ammonium nitrate fuel oil) and preparation method thereof, 2017.
  17.  Patent No. 2599170, RU: Method of producing porous granulated ammonium nitrate, 2016.
  18.  Patent No. 2600061, RU: Method of porous granulated ammonium nitrate producing and device for its implementation, 2016.
  19.  Patent No. 112294 UA: Device for granulation in the suspended layer, 2016.
  20.  Patent No. 112393 UA: Vortex granulator with utilization of waste gases, 2016.
  21.  Patent No. 112394 UA: Vortex granulator, 2016.
  22.  Patent No. 112622 UA: Vortex granulator, 2016.
  23.  Patent No. 113141 UA: Vortex granulator, 2017.
  24.  G. Martin and W. Barbour, Industrial nitrogen compounds and explosives, Chemical Manufacture and Analysis, Watchmaker Publishing, Seaside, 2003.
  25.  N. Kubota, Propellants and explosives: thermochemical aspects of combustion. 3rd ed., Wiley-VCH Verlag & Co.,Weinheim, 2015.
  26.  D. Buczkowski and B. Zygmunt, “Detonation Properties of Mixtures of Ammonium Nitrate Based Fertilizers and Fuels”, Cent. Eur. J. Energetic Mater., vol. 8, no. 2, pp. 99–106, 2011.
  27.  A.E. Artyukhov and V.I. Sklabinskyi, “Experimental and industrial implementation of porous ammonium nitrate producing process in vortex granulators”, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, vol. 6, pp. 42–48, 2013.
  28.  A.E. Artyukhov and N.A. Artyukhova, “Utilization of dust and ammonia from exhaust gases: new solutions for dryers with different types of fluidized bed”, J. Environ. Health Sci. Eng., vol. 16, no. 2, pp. 193–204, 2018.
  29.  A.E. Artyukhov and V.I. Sklabinskyi, “Investigation of the temperature field of coolant in the installations for obtaining 3D nanostructured porous surface layer on the granules of ammonium nitrate”, J. Nano- and Electron. Phys., vol. 9, no. 1, pp. 01015-1–01015-4, 2017.
  30.  N.A. Artyukhova, “Multistage finish drying of the N4HNO3 porous granules as a factor for nanoporous structure quality improvement”, J. Nano- and Electron. Phys., vol. 10, no. 3, pp. 03030-1–03030-5, 2018.
  31.  J. Hahm and A. Beskok, “Numerical simulation of multiple species detection using hydrodynamic/electrokinetic focusing”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 53, no. 4, pp. 325–334, 2005.
  32.  А.E. Artyukhov, V.K. Obodiak, P.G. Boiko, and P.C. Rossi, Computer modeling of hydrodynamic and heat-mass transfer processes in the vortex type granulation devices, in CEUR Workshop Proceedings, vol. 1844, pp. 33–47, 2017.
  33.  A.E. Artyukhov, N.O. Artyukhova, and A.V. Ivaniia, “Creation of software for constructive calculation of devices with active hydrodynamics”, in Proceedings of the 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET 2018), 2018, pp. 139–142.
  34.  A.E. Artyukhov, N.A. Artyukhova, A.V. Ivaniia, and J. Gabrusenoks, “Multilayer modified NH4NO3 granules with 3D nanoporous structure: effect of the heat treatment regime on the structure of macro- and mezopores”, in Proc IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017), 2017, pp. 315–318.
  35.  A.E. Artyukhov and J. Gabrusenoks, “Phase composition and nanoporous structure of core and surface in the modified granules of NH4NO3”, Springer Proc. Phys., vol. 210, pp. 301–309, 2018.
  36.  N.O. Artyukhova and J. Krmela, “Nanoporous structure of the ammonium nitrate granules at the final drying: The effect of the dryer operation mode”, J. Nano- Electron. Phys., vol. 11, no. 4, pp. 04006-1–04006-4, 2019.
  37.  V.K. Obodiak, N.O. Artyukhova, and A.E. Artyukhov, “Calculation of the residence time of dispersed phase in sectioned devices: Theoretical basics and software implementation” Lect. Notes Mech. Eng., pp. 813‒820, 2020.
  38.  B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  39.  W. Jefimowski A. Nikitenko Z. Drążek, and M. Wieczorek, “Stationary supercapacitor energy storage operation algorithm based on neural network learning system”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 4, pp. 733–738, 2020, doi: 10.24425/bpasts.2020.134176.
Go to article

Authors and Affiliations

Nadiia Artyukhova
1
Jan Krmela
2
ORCID: ORCID
Artem Artyukhov
1
ORCID: ORCID
Vladimíra Krmelová
3
Mária Gavendová
3
Alžbeta Bakošová
2

  1. Sumy State University, Oleg Balatskyi Academic and Research Institute of Finance, Economics and Management, Department of Marketing, Rymskogo-Korsakova st. 2, 40007, Sumy, Ukraine
  2. Alexander Dubček University of Trenčín, Faculty of Industrial Technologies in Púchov, Department of Numerical Methods and Computational Modeling, Ivana Krasku 491/30, 020 01 Púchov, Slovakia
  3. Alexander Dubček University of Trenčín, Faculty of Industrial Technologies in Púchov, Department of Material Technologies and Environment, Ivana Krasku 491/30, 020 01 Púchov, Slovakia
Download PDF Download RIS Download Bibtex

Abstract

In the current work the calculations of the reaction cross-section of total fusion σ fus, the fusion barrier distribution D fus, and the probability P fus were achieved for systems ⁶He+⁶⁴Zn, ⁸B+⁵⁸Ni and ⁸He+¹⁹⁷Au which involve halo nuclei by using a semiclassical approach. The semiclassical and quantum mechanics treatments comprise the approximation of WKB for describing the relative motion among projectile nuclei and target nuclei, and the method of CDCC (Continuum Discretized Coupled Channel) for describing the intrinsic motion for the projectile and target nuclei. Our semiclassical calculations yielded findings that were compared to obtainable experimental data as well as quantum mechanics calculations. For fusion cross-sections σ fus below and above the Coulomb barrier Vb, the quantum mechanics coupled channels are very similar, according to the experimental results.
Go to article

Bibliography

  1.  J. Badziak, “Laser nuclear fusion: current status, challenges and prospect,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 60, no. 4, pp. 729–738, 2012, doi: 10.2478/v10175-012-0084-8.
  2.  K. Hagino and N. Takigawa, “Subbarrier fusion reactions and many-particle quantum tunneling,” Prog. Theor. Phys., vol. 128, no. 6, pp. 1061–1106, 2012, doi: 10.1143/ptp.128.1061.
  3.  M. Dasgupta, D.J. Hinde, N. Rowley, and A.M. Stefanini, “Measuring barriers to fusion,” Annu. Rev. Nucl. Part. Sci., vol. 48, no. 1, pp. 401–461, 1998, doi: 10.1146/annurev.nucl.48.1.401.
  4.  D.J. Griffiths and D.F. Schroeter, Introduction to quantum mechanics, Cambridge University Press, 2018.
  5.  L.F. Canto, P.R.S. Gomes, R. Donangelo, and M.S. Hussein, “Fusion and breakup of weakly bound nuclei,” Phys. Rep., vol. 424, no. 1–2, pp. 1–111, 2006, doi: 10.1016/j.physrep.2005.10.006.
  6.  A. Diaz-Torres and M. Boselli “Low-energy fusion dynamics of weakly bound nuclei,” EPJ Web of Conferences, vol. 117, p. 08002, 2016, doi: 10.1051/epjconf/201611708002.
  7.  A. Diaz-Torres, I.J. Thompson, and C. Beck, “How does breakup influence the total fusion of 6, 7Li at the Coulomb barrier?,” Phys. Rev. C, vol. 68, no. 4, pp. 44607, 2003, doi: 10.1103/physrevc.68.044607.
  8.  L.R. Gasques, D.J. Hinde, M. Dasgupta, A. Mukherjee, and R.G. Thomas, “Suppression of complete fusion due to breakup in the reactions B10,  11 + Bi209,” Phys. Rev. C, vol.79, no.3, pp. 34605, 2009, doi: 10.1103/physrevc.79.034605.
  9.  B. Wang, W.J. Zhao, A. Diaz-Torres, E.G. Zhao, and S.G. Zhou, “Systematic study of suppression of complete fusion in reactions involving weakly bound nuclei at energies above the Coulomb barrier,” Phys. Rev. C, vol. 93, no. 1, pp. 14615, 2016, doi: 10.1103/ physrevc.93.014615.
  10.  M.E. Brandan and G.R. Satchler, “The interaction between light heavy-ions and what it tells us,” Phys. Rep., vol. 285, no. 4–5, pp. 143–243, 1997, doi: 10.1016/s0370-1573(96)00048-8.
  11.  P.R. Silveira Gomes, J.L. Rios, J.R. Borges, and D.R. Otomar, “Fusion, breakup and scattering of weakly bound nuclei at near barrier energies,” Open Nucl. Part. Phys. J., vol. 6, no. 1, 2013, doi: 10.2174/1874415X01306010010.
  12.  P.R.S. Gomes et al., “Break-up and scattering of weakly bound nuclei,” Revista Mexicana De Física, vol. 52, pp. 23–29, 2006 [Online]. Available: https://www.researchgate.net/publication/242365995_Fusion_break-up_and_scattering_of_weakly_bound_nuclei.
  13.  F.A. Majeed and Y.A. Abdul-Hussien, “Semiclassical treatment of fusion and breakup processes of 6, 8He halo nuclei,” J. Theor. Appl. Phys., vol. 10, no. 2, pp. 107–112, 2016, doi: 10.1007/s40094-016-0207-y.
  14.  F.A. Majeed, “The role of the breakup channel on the fusion reaction of light and weakly bound nuclei,” Int. J. Nucl. Energ. Sci. Tech., vol. 11, no. 3, pp. 218–228, 2017, doi: 10.1504/ijnest.2017.088068.
  15.  F.A. Majeed, R.Sh. Hamodi, and F.M. Hussian, “Effect of coupled channels on semiclassical and quantum mechanical calculations for heavy ion fusion reactions,” J. Comput.Theor. Nanosci., vol. 14, no. 5, pp. 2242–2247, 2017, doi: 10.1166/jctn.2017.6816.
  16.  F.A. Majeed, K.H.H. AlAteah and M.S. Mehemed, “Coupled channel calculations using semi-classical and quantum mechanical approaches for light and medium mass systems,” Int. J. Energ. Sci. Tech, vol. 11, no. 7, pp. 291–308, 2018, doi: 10.1504/IJNEST.2017.090652.
  17.  F.A. Majeed and F.A. Mahdi, “Quantum Mechanical Calculations of a Fusion Reaction for Some Selected Halo Systems,” Ukr. J. Phys., vol. 64, no. 1, pp. 11, 2019, doi: 10.15407/ujpe64.1.11.
  18.  F.A. Majeed, Y.A. Abdul-Hussien, and F.M. Hussian. “Fusion Reaction of Weakly Bound Nuclei,” in Nuclear Fusion-One Noble Goal and a Variety of Scientific and Technological Challenges, IntechOpen, 2019.
  19.  A.J. Najim, F.A. Majeed, and Kh.H. Al-Attiyah, “Coupled-Channel Calculations for Fusion Cross Section and Fusion Barrier Distribution of 32S+144, 150, 152, 154Sm,” In IOP Conf. Ser.: Mater. Sci. Eng., vol. 571, pp. 012124, 2019, doi: 10.36478/jeasci.2019.10406.10412.
  20.  A.J. Najim, F.A. Majeed and K.H.A. Al-Attiyah, “Description of coupled-channel in Semiclassical treatment of heavy ion fusion reactions,” J. Eng. Appl. Sci., vol. 14, pp. 10406–10412, 2019, doi: 10.1088/1757-899X/571/1/012113.
  21.  H.J. Musa, F.A. Majeed, and A.T. Mohi, “Coupled Channels Calculations of Fusion Reactions for 46Ti+64Ni, 40Ca+194Pt and 40Ar+148Sm Systems,” Iraqi J. Phys., vol. 18, no. 47, pp. 84–90, 2020, doi: 10.30723/ijp.v18i47.604.
  22.  H.J. Musa, F.A. Majeed, and A.T. Mohi. “Improved WKB Approximation for Nuclear Fusion Reactions,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 871, no. 1, pp. 012063, 2020, doi: 10.1088/1757-899x/871/1/012063.
  23.  M.S. Mehemed, S.M. Obaid, and F.A. Majeed, “Coupled channels calculation of fusion reaction for selected medium systems,” Int. J. Nucl. Energy Sci. Technol., vol. 14, no. 2, pp. 165–180, 2020, doi: 10.1504/IJNEST.2020.112162.
  24.  N. Austern, Direct Nuclear Reaction Theory, Wiley, New York. 1970.
  25.  G.R. Satchler, Direct Nuclear Reactions, Oxford University Press, Oxford. 1983.
  26.  W.H.Z. Cardenas et al., “Approximations in fusion and breakup reactions induced by radioactive beams,” Nucl. Phys. A, vol. 703, no. 3–4, pp. 633–648, 2002. doi: 10.1016/s0375-9474(01)01672-4.
  27.  M.S. Hussein, “Theory of the heavy-ion fusion cross section,” Phys. Rev. C, vol. 30, no. 6, pp. 1962, 1984, doi: 10.1103/physrevc.30.1962.
  28.  G.R. Satchler, “Absorption cross sections and the use of complex potentials in coupled-channels models,” Phys. Rev. C, vol. 32, no. 6, pp. 2203, 1985, doi: 10.1103/PhysRevC.32.2203.
  29.  N. Rowley, G.R. Satchler, and P.H. Stelson, “On the “distribution of barriers” interpretation of heavy-ion fusion,” Phys. Lett. B, vol. 254, no. 1–2, pp. 25–29, 1991, doi: 10.1016/0370-2693(91)90389-8.
  30.  H. Timmers, D. Ackermann, S. Beghini, L.Corradi, J.H. He, G. Montagnoli, F. Scarlassara, A.M. Stefanini, N. Rowley, “A case study of collectivity, transfer and fusion enhancement,” Nucl. Phys. A, vol. 633, no. 3, pp. 421–445, 1998, doi: 10.1016/s0375-9474(98)00121-3.
  31.  V. Scuderi et al., “Fusion and direct reactions for the system 6He+64Zn at and below the coulomb barrier,” Phys. Rev. C, vol. 84, no. 6, pp. 064604, 2011, doi: 10.1103/physrevc.84.064604.
  32.  P. Moller, J.R. Nix, W.D. Myers, and W.J. Swiatecki, “Nuclear properties for astrophysical and radioactive-ionbeam applications,” At. Data Nucl. Data Tables, vol. 59, pp. 131–343, 1995, doi: 10.1006/adnd.1997.0746.
  33.  K. Hagino, A. Vitturi, C.H. Dasso, and S.M. Lenzi, “Role of breakup processes in fusion enhancement of drip-line nuclei at energies below the Coulomb barrier,” Phys. Rev. C, vol. 61, no. 3, pp. 0376, 2000, doi: 10.1103/PhysRevC.61.037602.
Go to article

Authors and Affiliations

Maryam H. Abd Madhi
1
Fouad A. Majeed
1
ORCID: ORCID

  1. Department of Physics, College of Education for Pure Sciences, University of Babylon, Babylon, Iraq
Download PDF Download RIS Download Bibtex

Abstract

This paper presents the results of diagnostic examinations conducted on the coils of super-heaters made of 10CrMo9‒10 steel that were operated in industrial conditions at 480°C for 130 thousand hours. The tube was exposed in a coal-fired boiler. The chemical and phase composition of the oxide/deposit layers formed on both sides of the tube walls (outside – flue-gas side and inside – steam side) and their sequence was examined using optical microscopy, scanning electron microscopy with electron backscatter diffraction and energy-dispersive X-ray spectroscopy, and X-ray diffraction. The changes in the mechanical properties caused by corrosion and aging processes were concluded from the hardness measurements. In addition, the nature of cracks in the oxide layers caused by pressing a Vickers indenter was determined. The results of these examinations have shown a high degradation of steel on the flue-gas inflow side and identified the main corrosion products and mechanisms.
Go to article

Bibliography

  1.  S. Frangini, A. Masci, and F. Zaza, “Molten salt synthesis of perovskite conversion coatings: A novel approach for corrosion protection of stainless steels in molten carbonate fuel cells,” Corros. Sci. vol. 53, no. 8, pp. 2539–2548, 2011, doi: 10.1016/j.corsci.2011.04.011.
  2.  M. Gwoździk, “Analysis of crystallite size changes in an oxide layer formed on steel used in the power industry”, Acta Phys. Pol. A. vol. 130, no. 4, pp. 935–938, 2016, doi: 10.12693/APhysPolA.130.935.
  3.  M. Gwoździk and Z. Nitkiewicz, “Texturing of magnetite forming during long-term operation of a pipeline of 10CrMo9‒10 steel,” Solid State Phenomena, vol. 203‒204, pp. 121–124, 2013, doi: 10.4028/www.scientific.net/SSP.203-204.121.
  4.  J. Priss, H. Rojacz, I. Klevtsov, A. Dedov, H. Winkelmann, and E. Badisch, “High temperature corrosion of boiler steels in hydrochloric atmosphere under oil shale ashes,” Corros. Sci. vol. 82, pp. 36–44, 2014, doi: 10.1016/j.corsci.2013.12.016.
  5.  J. Lehmusto, P. Yrjas, and L. Hupa, “Pre-oxidation as a means to increase corrosion resistance of commercial superheater steels,” Oxid Met, vol. 91, pp. 311–326, 2019, doi: 10.1007/s11085-019-09898-x.
  6.  X. Montero and M.C. Galetz, “Effect of different vanadate salt composition on oil ash corrosion of boilers,” Oxid Met, vol. 89, pp. 395–414, 2018, doi: 10.1007/s11085-017-9795-4.
  7.  J. Lehmusto, D. Lindberg, P. Yrjas, and L. Hupa, “The effect of temperature on the formation of oxide scales regarding commercial superheater steels. Oxid Met, vol. 89, pp. 251–278, 2018, doi: 10.1007/s11085-017-9785-6.
  8.  M. Gwoździk and Z. Nitkiewicz, “Studies on the adhesion of oxide layer formed on X10CrMoVNb9‒1 steel,” Arch. Civ. Mech. Eng., vol. 14, pp. 335–341, 2014, doi: 10.1016/j.acme.2013.10.005.
  9.  P. Gawron and S. Danisz, “Dostosowanie zakresu badań diagnostycznych wybranych elementów kotłów pracujących w warunkach współspalania biomasy,” Energetyka, vol. 702, pp. 843–853, 2012 [in Polish].
  10.  F. Klepacki and D. Wywrot, “Trwałość wężownicprzegrzewaczy wtórnych w warunkach niskoemisyjnego spalania,” 12th Informative and Training Symposium: Maintenance of Thermo-Mechanical Power Equipment. Upgrading power equipment to extend its operating period beyond 300 000 hours. Wisła, Poland 2010, pp. 29–35 [in Polish].
  11.  J. Cheng, Y.P. Wu, L.Y. Chen, S. Hong, L. Qiao, and Z. Wei, “Hot corrosion behavior and mechanism of highvelocity arc-sprayed Ni-Cr alloy coatings,” J. Therm. Spray Technol., vol. 28, no. 6, pp. 1263–1274, 2019, doi: 10.1007/s11666-019-00890-0.
  12.  A.K. Pramanick, G. Das, and S.K. Das, “Ghosh Failure investigation of super heater tubes of coalfired power plant,” Case Stud. Eng. Fail. Anal., vol. 9, pp. 17–26, 2017, doi: 10.1016/j.csefa.2017.06.001.
  13.  M. Gwoździk, S. Kulesza, M. Bramowicz, “Application of the fractal geometry methods for analysis of oxide layer”. 26th International Conference on Metallurgy and Materials (METAL 2017), Brno, Czech Republic, 2017, pp. 789- 794.
  14.  P. Monivarman, V.A. Nagarajan, and F.M. Raj, “Mechanical and morphological characterization of discarded fishnet/glass fiber reinforced polyester composite,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1385–1391, 2020, doi: 10.24425/bpasts.2020.134646.
  15.  J. Iwaszko, “Laser surface remelting of powder metallurgy high-speed steel,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1425–1432, 2020, doi: 10.24425/bpasts.2020.135385.
  16.  C. Bhargava, J. Aggarwal, and P.K. Sharma, “Residual life estimation of fabricated humidity sensors using different artificial intelligence techniques,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 1, pp. 147–154, 2019, doi: 10.24425/bpas.2019.127344.
  17.  M. Gwoździk, M. Motylenko, and D. Rafaja, “Microstructure changes responsible for the degradation of the 10CrMo9‒10 and 13CrMo4‒5 steels during long-term operation,” Mater. Res. Express, vol. 7, no. 1, p. 016515, 2020, doi: 10.1088/2053-1591/ab5fc8.
  18.  C. Hao, F.M. Deng, Z.H. Guo, X. Bo, S. Wang, and X. Zhao, “Fractal dimension of decobalt surface on PDC with different acid corrosion reagents at room temperature,” Diam. Relat. Mat., vol. 105, p. 107699, 2020, doi: 10.1016/j.diamond.2020.107699.
  19.  F.M. Mwema, E.T. Akinlabi, and O.P. Oladijo, “Effect of substrate type on the fractal characteristics of AFM images of sputtered aluminium thin films,” Mater. Sci.-Medzg., vol. 26, pp. 49–57, 2020, doi: 10.5755/j01.ms.26.1.22769.
  20.  H. Aminirastabi, H. Xue, V.V. Miti´c, G. Lazovi´c, G. Ji, and D. Peng, “Novel fractal analysis of nanograin growth in BaTiO3 thin film,” Mater Chem Phys, vol. 239, p. 122261, 2020, doi: 10.1016/j.matchemphys.2019.122261.
  21.  W.P. Dong, P.J. Sullivan, and K.J. Stout, “Comprehensive study of parameters for characterizing 3-dimensional surface-topography. 4. Parameters for characterizing spatial and hybrid properties,” Wear, vol. 178, no. 1–2, pp. 45–60, 1994, doi: 10.1016/0043-1648(94)90128- 7.
  22.  T.R. Thomas, B.-G. Rosén, and N. Amini, “Fractal characterisation of the anisotropy of rough surfaces,” Wear, vol. 232, no. 1, pp. 41–50, 1999, doi: 10.1016/S0043-1648(99)00128-3.
  23.  R.X. Fischer et al., “A new mineral from the Bellerberg, Eifel, Germany, intermediate between mullite and sillimanite,” Am. Miner., vol. 100, pp. 1493–1501, 2015, doi: 10.2138/am-2015-4966.
  24.  Z. Liang, M. Yu, and Q. Zhao, “Investigation of fireside corrosion of austenitic heat-resistant steel 10Cr18Ni9Cu3NbN in ultra-supercritical power plants,” Eng. Fail. Anal., vol. 100, pp. 180–191, 2019, doi: 10.1016/j.engfailanal.2019.02.048.
  25.  M.F. Ashby and D.R.H. Jones, Engineering Materials 1 An Introduction to Properties, Applications and Design, Elsevier, 2012.
  26.  J. Fernández, F. González, C. Pesquera, A. Neves Junior, M Mendes Viana and J. Dweck, “Qualitative and quantitative characterization of a coal power plant waste by TG/DSC/MS, XRF and XRD,” J. Therm. Anal. Calorim., vol. 125, no. 2, pp. 703–710, 2016, doi: 10.1007/ s10973-016-5270-8.
  27.  P. Viklund, A. Hjörnhede, P. Henderson, A. Stålenheim, and R. Pettersson, “Corrosion of superheater materials in a waste-to-energy plant,” Fuel Process. Technol., vol. 105, pp. 106–112, 2013, doi: 10.1016/j.fuproc.2011.06.017.
  28.  Y. Wang, J. Jin, D. Liu, H. Yang, and X. Kou, “Understanding ash deposition for Zhundong coal combustion in 330 MW utility boiler: focusing on surface temperature effects,” Fuel, vol. 216, pp. 697–706, 2018, doi: 10.1016/j.fuel.2017.08.112.
  29.  Y. Xie, W. Xie, W-P. Pan, A. Riga, and K. Anderson, “A study of ash deposits on the heat exchange tubes using SDT/MS and XRD techniques,” Thermochim. Acta, vol. 324, pp. 123–133, 1998, doi: 10.1016/S0040-6031(98)00529-2.
  30.  P.J. Ennis and W.J. Quadakkers, “Mechanisms of steam oxidation in high strength martensitic steels,” Int. J. Pressure Vessels Pip., vol. 84, pp. 75–81, 2007, doi: 10.1016/j.ijpvp.2006.09.007.
  31.  R. Abang, A. Findeisen, and H.J. Krautz, “Corrosion behaviour of selected Power plant materials under oxyfuel combustion conditions,” Górnictwo i Geoinżynieria, vol. 35, no. 3/1, pp. 23–42, 2011.
  32.  T. Aleksandrov Fabijanic’, D. Ćorić, M. Šnajdar Musa, and M. Sakoman, “Vickers Indentation Fracture Toughness of Near-Nano and Nanostructured WC-Co Cemented Carbides,” Metals, vol. 7, 143, 2017, doi: 10.3390/met7040143.
  33.  M. Gwoździk and Z. Nitkiewicz, “Scratch resistance characteristic of oxide layer formed on P91 steel,” Inżynieria Materiałowa, vol. 182, no. 4, pp. 435–438, 2011.
Go to article

Authors and Affiliations

Monika Gwoździk
1
Christiane Ullrich
2
Christian Schimpf
2
David Rafaja
2
Sławomir Kulesza
3
Mirosław Bramowicz
3

  1. Czestochowa University of Technology, ul. Dabrowskiego 69, 42-201 Czestochowa, Poland
  2. TU Bergakademie Freiberg, Akademiestraße 6, 09599 Freiberg, Germany
  3. University of Warmia and Mazury in Olsztyn, ul. Michała Oczapowskiego 2, 10-719 Olsztyn, Poland
Download PDF Download RIS Download Bibtex

Abstract

Polyester coatings are among the most commonly used types of powder paints and present a wide range of applications. Apart from its decorative values, polyester coating successfully prevents the substrate from environmental deterioration. This work investigates the cavitation erosion (CE) resistance of three commercial polyester coatings electrostatic spray onto AW-6060 aluminium alloy substrate. Effect of coatings repainting (single- and double-layer deposits) and effect of surface finish (matt, silk gloss and structural) on resistance to cavitation were comparatively studied. The following research methods were used: CE testing using ASTM G32 procedure, 3D profilometry evaluation, light optical microscopy, scanning electron microscopy (SEM), optical profilometry and FTIR spectroscopy. Electrostatic spray coatings present higher CE resistance than aluminium alloy. The matt finish double-layer (M2) and single-layer silk gloss finish (S1) are the most resistant to CE. The structural paint showed the lowest resistance to cavitation wear which derives from the rougher surface finish. The CE mechanism of polyester coatings relies on the material brittle-ductile behaviour, cracks formation, lateral net-cracking growth and removal of chunk coating material and craters’ growth. Repainting does not harm the properties of the coatings. Therefore, it can be utilised to regenerate or smother the polyester coating finish along with improvement of their CE resistance.
Go to article

Bibliography

  1.  A. Kausar, “Review of fundamentals and applications of polyester nanocomposites filled with carbonaceous nanofillers,” J. Plast. Film Sheeting, vol. 35, no. 1, pp. 22–44, Jan. 2019, doi: 10.1177/8756087918783827.
  2.  A. Krzyzak, E. Kosicka, and R. Szczepaniak, “Research into the Effect of Grain and the Content of Alundum on Tribological Properties and Selected Mechanical Properties of Polymer Composites,” Materials, vol. 13, no. 24, Art. no. 5735, Jan. 2020, doi: 10.3390/ma13245735.
  3.  A. Kausar, “High performance epoxy/polyester-based nanocomposite coatings for multipurpose applications: A review,” J. Plast. Film Sheeting, vol. 36, no. 4, pp. 391–408, Oct. 2020, doi: 10.1177/8756087920910481.
  4.  M. Winnicki, T. Piwowarczyk, and A. Małachowska, “General description of cold sprayed coatings formation and of their properties,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 3, pp. 301–310, Jun. 2018.
  5.  L. Łatka, L. Pawłowski, M. Winnicki, P. Sokołowski, A. Małachowska, and S. Kozerski, “Review of Functionally Graded Thermal Sprayed Coatings,” Appl. Sci., vol. 10, no. 15, Art. no. 5153, Jan. 2020, doi: 10.3390/app10155153.
  6.  R. Kosydar et al., “Boron nitride/titanium nitride laminar lubricating coating deposited by pulsed laser ablation on polymer surface,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 56, no. 3, pp. 217–221, 2008.
  7.  T. Burakowski and T. Wierzchon, Surface Engineering of Metals: Principles, Equipment, Technologies. Boca Raton, Fla: CRC Press, 1998.
  8.  T. Hejwowski, Nowoczesne powłoki nakładane cieplnie odporne na zużycie ścierne i erozyjne (Modern wear and erosion resitant thermally deposited coatings). Lublin, Poland: Politechnika Lubelska (Lublin University of Technology), 2013. [Online]. Available: http://bc.pollub. pl/dlibra/docmetadata?id=4059.
  9.  Z.W. Wicks. Jr, F.N. Jones, S.P. Pappas, and D.A. Wicks, Organic Coatings: Science and Technology. John Wiley & Sons, 2007.
  10.  S. Biggs, C.A. Lukey, G.M. Spinks, and S.-T. Yau, “An atomic force microscopy study of weathering of polyester/melamine paint surfaces,” Prog. Org. Coat., vol. 42, no. 1, pp. 49–58, Jun. 2001, doi: 10.1016/S0300-9440(01)00147-3.
  11.  M. Oleksy et al., “Kompozycje modyfikowanych farb proszkowych. Cz. 1. Hybrydowe kompozycje poliestrowych farb proszkowych,” Polimery, vol. 63, no. 11– 12, pp. 762‒771, 2018, doi: 10.14314/polimery.2018.11.4.
  12.  M. Fernández-Álvarez, F. Velasco, and A. Bautista, “Effect on wear resistance of nanoparticles addition to a powder polyester coating through ball milling,” J. Coat. Technol. Res., vol. 15, no. 4, pp. 771–779, Jul. 2018, doi: 10.1007/s11998-018-0106-z.
  13.  M. Zouari, M. Kharrat, and M. Dammak, “Wear and friction analysis of polyester coatings with solid lubricant,” Surf. Coat. Technol., vol. 204, no. 16, pp. 2593–2599, May 2010, doi: 10.1016/j.surfcoat.2010.02.001.
  14.  I. Stojanović, V. Šimunović, V. Alar, and F. Kapor, “Experimental Evaluation of Polyester and Epoxy–Polyester Powder Coatings in Aggressive Media,” Coatings, vol. 8, no. 3, Art. no. 98, Mar. 2018, doi: 10.3390/coatings8030098.
  15.  K.V.S.N. Raju and D.K. Chattopadhyay, “Polyester coatings for corrosion protection,” in High-Performance Organic Coatings, A.S. Khanna, Ed. Woodhead Publishing, 2008, pp. 165–200. doi: 10.1533/9781845694739.2.165.
  16.  M. Szala and E. Kot, “Influence of repainting on the mechanical properties, surface topography and microstructure of polyester powder coatings,” Adv. Sci. Technol. Res. J., vol. 11, no. 2, pp. 159–165, Jun. 2017, doi: 10.12913/22998624/69680.
  17.  M. Walczak, D. Pieniak, and M. Zwierzchowski, “The tribological characteristics of SiC particle reinforced aluminium composites,” Arch. Civ. Mech. Eng., vol. 15, no. 1, pp. 116–123, Jan. 2015, doi: 10.1016/j.acme.2014.05.003.
  18.  M. Szala, L. Łatka, M.Walczak, and M.Winnicki, “Comparative Study on the Cavitation Erosion and Sliding Wear of Cold-Sprayed Al/ Al2O3 and Cu/Al2O3 Coatings, and Stainless Steel, Aluminium Alloy, Copper and Brass,” Metals, vol. 10, no. 7, Art. no. 7, Jul. 2020, doi: 10.3390/met10070856.
  19.  V. Caccese, K.H. Light, and K.A. Berube, “Cavitation erosion resistance of various material systems,” Ships Offshore Struct., vol. 1, no. 4, pp. 309–322, Apr. 2006, doi: 10.1533/saos.2006.0136.
  20.  T. Deplancke, O. Lame, J.-Y. Cavaille, M. Fivel, M. Riondet, and J.-P. Franc, “Outstanding cavitation erosion resistance of Ultra High Molecular Weight Polyethylene (UHMWPE) coatings,” Wear, vol. 328–329, pp. 301–308, Apr. 2015, doi: 10.1016/j.wear.2015.01.077.
  21.  N. Qiu, L. Wang, S. Wu, and D.S. Likhachev, “Research on cavitation erosion and wear resistance performance of coatings,” Eng. Fail. Anal., vol. 55, pp. 208–223, Sep. 2015, doi: 10.1016/j.engfailanal.2015.06.003.
  22.  S. Chi, J. Park, and M. Shon, “Study on cavitation erosion resistance and surface topologies of various coating materials used in shipbuilding industry,” J. Ind. Eng. Chem., vol. 26, pp. 384–389, Jun. 2015, doi: 10.1016/j.jiec.2014.12.013.
  23.  G.L. García et al., “Cavitation resistance of epoxybased multilayer coatings: Surface damage and crack growth kinetics during the incubation stage,” Wear, vol. 316, no. 1–2, pp. 124–132, Aug. 2014, doi: 10.1016/j.wear.2014.04.007.
  24.  M. Hibi, K. Inaba, K. Takahashi, K. Kishimoto, and K. Hayabusa, “Effect of Tensile Stress on Cavitation Erosion and Damage of Polymer,” J. Phys. Conf. Ser., vol. 656, no. 1, p. 012049, Nov. 2015, doi: 10.1088/1742-6596/656/1/012049.
  25.  G. Taillon, S. Saito, K. Miyagawa, and C. Kawakita, “Cavitation erosion resistance of high-strength fiber reinforced composite material,” IOP Conf. Ser. Earth Environ. Sci., vol. 240, no. 6, p. 062056, Mar. 2019, doi: 10.1088/1755-1315/240/6/062056.
  26.  N. Sheppard, “The Historical Development of Experimental Techniques in Vibrational Spectroscopy,” in Handbook of Vibrational Spectroscopy, American Cancer Society, 2006. doi: 10.1002/0470027320.s0101.
  27.  R.M. Silverstein et al., Spectrometric Identification of Organic Compounds, 8th Edition, 8th edition. Wiley, 2014.
  28.  W. Macek et al., “Profile and Areal Surface Parameters for Fatigue Fracture Characterisation,” Materials, vol. 13, no. 17, Art. no. 3691, 2020, doi: 10.3390/ma13173691.
  29.  “ISO 4287:1997. Geometrical Product Specifications (GPS) – Surface texture: Profile method – Terms, definitions and surface texture parameters,” International Organization for Standardization, Geneva, Switzerland, Norma, 1997.
  30.  A. Skoczylas, “Influence of Centrifugal Shot Peening Parameters on the Impact Force and Surface Roughness of EN-AW2024 Aluminum Alloy Elements,” Adv. Sci. Technol. Res. J., vol. 15, no. 1, pp. 71–78, Mar. 2021, doi: 10.12913/22998624/130511.
  31.  “ASTM G32-10: Standard Test Method for Cavitation Erosion Using Vibratory Apparatus,” ASTM International: West Conshohocken, Philadelphia, PA, USA, 2010.
  32.  M. Szala, M. Walczak, L. Łatka, K. Gancarczyk, and D. Özkan, “Cavitation Erosion and Sliding Wear of MCrAlY and NiCrMo Coatings Deposited by HVOF Thermal Spraying,” Adv. Mater. Sci., vol. 20, no. 2, pp. 26–38, Jun. 2020, doi: 10.2478/adms-2020-0008.
  33.  J. Steller, A. Krella, J. Koronowicz, and W. Janicki, “Towards quantitative assessment of material resistance to cavitation erosion,” Wear, vol. 258, no. 1, pp. 604–613, Jan. 2005, doi: 10.1016/j.wear.2004.02.015.
  34.  J. Steller, “International Cavitation Erosion Test and quantitative assessment of material resistance to cavitation,” Wear, vol. 233–235, pp. 51–64, Dec. 1999, doi: 10.1016/S0043-1648(99)00195-7.
  35.  B. Dybowski, M. Szala, T. J. Hejwowski, and A. Kiełbus, “Microstructural phenomena occurring during early stages of cavitation erosion of Al-Si aluminium casting alloys,” Solid State Phenom., vol. 227, pp. 255–258, 2015, doi: 10.4028/www.scientific.net/SSP.227.255.
  36.  J. Zhao, Z. Jiang, J. Zhu, J. Zhang, and Y. Li, “Investigation on Ultrasonic Cavitation Erosion Behaviors of Al and Al-5Ti Alloys in the DistilledWater,” Metals, vol. 10, no. 12, Art. no. 1631, Dec. 2020, doi: 10.3390/met10121631.
  37.  J. Lin, Z. Wang, J. Cheng, M. Kang, X. Fu, and S. Hong, “Effect of Initial Surface Roughness on Cavitation Erosion Resistance of Arc- Sprayed Fe-Based Amorphous/Nanocrystalline Coatings,” Coatings, vol. 7, no. 11, Art. no. 2000, Nov. 2017, doi: 10.3390/coatings7110200.
  38.  M. Szala, L. Łatka, M. Awtoniuk, M. Winnicki, and M. Michalak, “Neural Modelling of APS Thermal Spray Process Parameters for Optimizing the Hardness, Porosity and Cavitation Erosion Resistance of Al2O3‒13 wt% TiO2 Coatings,” Processes, vol. 8, no. 12, Art. no. 1544, Dec. 2020, doi: 10.3390/pr8121544.
  39.  J.C. Lindon, Encyclopedia of Spectroscopy and Spectrometry – 3rd Edition. 2010. [Online]. Available: https://www.elsevier.com/books/ encyclopedia-of-spectro scopy-and-spectrometry/lindon/978-0-12-803224-4 (Accessed: Feb. 24, 2021).
  40.  J.I. Haleem, “A Review of: Handbook of Near-Infrared Analysis,” Instrum. Sci. Technol., vol. 22, no. 3, pp. 283–285, Aug. 1994, doi: 10.1080/10739149408000456.
  41. Infrared Spectroscopy: Fundamentals and Applications. John Wiley & Sons, Ltd, 2004, doi: 10.1002/0470011149.ch3.
Go to article

Authors and Affiliations

Mirosław Szala
1
ORCID: ORCID
Aleksander Świetlicki
2
Weronika Sofińska-Chmiel
3

  1. Department of Materials Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
  2. Students Research Group of Materials Technology, Department of Materials Engineering, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
  3. Analytical Laboratory, Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie-Sklodowska University, pl. Maria Curie-Sklodowska 3, 20-031 Lublin, Poland
Download PDF Download RIS Download Bibtex

Abstract

User authentication is an essential element of any communication system. The paper investigates the vulnerability of the recently published first semiquantum identity authentication protocol (Quantum Information Processing 18: 197, 2019) to the introduced herein multisession attacks. The impersonation of the legitimate parties by a proper combination of phishing techniques is demonstrated. The improved version that closes the identified loophole is also introduced
Go to article

Bibliography

  1.  M.M. Wilde, Quantum Information Theory. Cambridge University Press, 2013, doi: 10.1017/CBO9781139525343.
  2.  S. Wiesner, “Conjugate coding,” SIGACT News, vol. 15, no. 1, pp. 78–88, 1983, doi: 10.1145/1008908.1008920.
  3.  P. Benioff, “The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines,” J. Stat. Phys., vol. 22, no. 5, pp. 563–591, 1980, doi: 10.1007/BF01011339.
  4.  C.H. Bennett and G. Brassard, “Quantum cryptography: Public key distribution and coin tossing,” in Proceedings of International Conference on Computers, Systems and Signal Processing, Bangalore, India, 1984, pp. 175–179.
  5.  C.H. Bennett and G. Brassard, “Quantum cryptography: Public key distribution and coin tossing,” Theor. Comput. Sci., vol. 560, pp. 7–11, 2014, doi: 10.1016/j.tcs.2014.05.025.
  6.  P.W. Shor, “Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer,” SIAM J. Comput., vol. 26, no. 5, pp. 1484–1509, 1997, doi: 10.1137/S0097539795293172.
  7.  A. Shenoy-Hejamadi, A. Pathak, and S. Radhakrishna, “Quantum cryptography: Key distribution and beyond,” Quanta, vol. 6, no. 1, pp. 1–47, 2017, doi: 10.12743/quanta.v6i1.57.
  8.  F. Xu, X. Ma, Q. Zhang, H.-K. Lo, and J.-W. Pan, “Secure quantum key distribution with realistic devices,” Rev. Mod. Phys., vol. 92, p. 025002, 2020, doi: 10.1103/RevModPhys.92.025002.
  9.  D. Pan, K. Li, D. Ruan, S.X. Ng, and L. Hanzo, “Singlephoton- memory two-step quantum secure direct communication relying on Einstein-Podolsky-Rosen pairs,” IEEE Access, vol. 8, pp. 121 146–121 161, 2020, doi: 10.1109/ACCESS.2020.3006136.
  10.  P. Zawadzki, “Advances in quantum secure direct communication,” IET Quant. Comm., vol. 2, no. 2, pp. 54–62, 2021, doi: 10.1049/ qtc2.12009.
  11.  A. Pljonkin and P.K. Singh, “The review of the commercial quantum key distribution system,” in 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), 2018, pp. 795–799, doi: 10.1109/PDGC.2018.8745822.
  12.  R. Qi, Z. Sun, Z. Lin, P. Niu, W. Hao, L. Song, Q. Huang, J. Gao, L. Yin, and G. Long, “Implementation and security analysis of practical quantum secure direct communication,” vol. 8, p. 22, 2019, doi: 10.1038/s41377-019-0132-3.
  13.  X. Li and D. Zhang, “Quantum authentication protocol using entangled states,” in Proceedings of the 5th WSEAS International Conference on Applied Computer Science, Hangzhou, China, 2006, pp. 1004–1009. [Online]. Available: https://www.researchgate.net/ publication/242080451_Quantum_authentication_protocol_using_entangled_states.
  14.  G. Zeng and W. Zhang, “Identity verification in quantum key distribution,” Phys. Rev. A, vol. 61, p. 022303, 2000, doi: 10.1103/ PhysRevA.61.022303.
  15.  Y. Kanamori, S.-M. Yoo, D.A. Gregory, and F.T. Sheldon, “On quantum authentication protocols,” in GLOBECOM ’05. IEEE Global Telecommunications Conference, 2005., vol. 3, 2005, pp. 1650–1654, doi: 10.1109/GLOCOM.2005.1577930.
  16.  P. Zawadzki, “Quantum identity authentication without entanglement,” Quantum Inf. Process., vol. 18, no. 1, p. 7, 2019, doi: 10.1007/ s11128-018-2124-2.
  17.  M. Boyer, D. Kenigsberg, and T. Mor, “Quantum key distribution with classical Bob,” Phys. Rev. Lett., vol. 99, p. 140501, 2007, doi: 10.1103/PhysRevLett.99.140501.
  18.  M. Boyer, R. Gelles, D. Kenigsberg, and T. Mor, “Semiquantum key distribution,” Phys. Rev. A, vol. 79, no. 3, p. 032341, 2009, doi: 10.1103/PhysRevA.79.032341.
  19.  W.O. Krawec, “Security of a semi-quantum protocol where reflections contribute to the secret key,” Quantum Inf. Process., vol. 15, no. 5, pp. 2067–2090, 2016, doi: 10.1007/s11128-016-1266-3.
  20.  Z.-R. Liu and T. Hwang, “Mediated semi-quantum key distribution without invoking quantum measurement,” Ann. Phys., vol. 530, no. 4, p. 1700206, 2018, doi: 10.1002/andp.201700206.
  21.  C.-W. Tsai and C.-W. Yang, “Cryptanalysis and improvement of the semi-quantum key distribution robust against combined collective noise,” Int. J. Theor. Phys., vol. 58, no. 7, pp. 2244–2250, 2019, doi: 10.1007/s10773-019-04116-5.
  22.  W.O. Krawec, “Security proof of a semi-quantum key distribution protocol,” in 2015 IEEE International Symposium on Information Theory (ISIT), 2015, pp. 686–690, doi: 10.1109/ISIT.2015.7282542.
  23.  Y.-P. Luo and T. Hwang, “Authenticated semi-quantum direct communication protocols using Bell states,” Quantum Inf. Process., vol. 15, no. 2, pp. 947–958, 2016, doi: 10.1007/s11128-015-1182-y.
  24.  J. Gu, P.-h. Lin, and T. Hwang, “Double C-NOT attack and counterattack on ‘Three-step semi-quantum secure direct communication protocol’,” Quantum Inf. Process., vol. 17, no. 7, p. 182, 2018, doi: 10.1007/s11128-018-1953-3.
  25.  M.-H. Zhang, H.-F. Li, Z.-Q. Xia, X.-Y. Feng, and J.-Y. Peng, “Semiquantum secure direct communication using EPR pairs,” Quantum Inf. Process., vol. 16, no. 5, p. 117, 2017, doi: 10.1007/s11128-017-1573-3.
  26.  L.-L. Yan, Y.-H. Sun, Y. Chang, S.-B. Zhang, G.-G. Wan, and Z.-W. Sheng, “Semi-quantum protocol for deterministic secure quantum communication using Bell states,” Quantum Inf. Process., vol. 17, no. 11, p. 315, 2018, doi: 10.1007/s11128-018-2086-4.
  27.  C. Xie, L. Li, and D. Qiu, “A novel semi-quantum secret sharing scheme of specific bits,” Int. J. Theor. Phys., vol. 54, no. 10, pp. 3819– 3824, 2015, doi: 10.1007/s10773-015-2622-2.
  28.  A. Yin and F. Fu, “Eavesdropping on semi-quantum secret sharing scheme of specific bits,” Int. J. Theor. Phys., vol. 55, no. 9, pp. 4027– 4035, 2016, doi: 10.1007/s10773-016-3031-x.
  29.  K.-F. Yu, J. Gu, T. Hwang, and P. Gope, “Multi-party semi-quantum key distribution-convertible multi-party semi- quantum secret sharing,” Quantum Inf. Process., vol. 16, no. 8, p. 194, 2017, doi: 10.1007/s11128-017-1631-x.
  30.  X. Gao, S. Zhang, and Y. Chang, “Cryptanalysis and improvement of the semi-quantum secret sharing protocol,” Int. J. Theor. Phys., vol. 56, no. 8, pp. 2512–2520, 2017, doi: 10.1007/s10773-017-3404-9.
  31.  Z. Li, Q. Li, C. Liu, Y. Peng, W. H. Chan, and L. Li, “Limited resource semiquantum secret sharing,” Quantum Inf. Process., vol. 17, no. 10, p. 285, 2018, doi: 10.1007/s11128-018-2058-8.
  32.  K. Sutradhar and H. Om, “Efficient quantum secret sharing without a trusted player,” Quantum Inf. Process., vol. 19, no. 2, p. 73, 2020, doi: 10.1007/s11128-019-2571-4.
  33.  H. Iqbal and W.O. Krawec, “Semi-quantum cryptography,” Quantum Inf. Process., vol. 19, no. 3, p. 97, 2020, doi: 10.1007/s11128-020- 2595-9.
  34.  N.-R. Zhou, K.-N. Zhu, W. Bi, and L.-H. Gong, “Semi-quantum identification,” Quantum Inf. Process., vol. 18, no. 6, p. 197, 2019, doi: 10.1007/s11128-019-2308-4.
  35.  K. Moriarty, B. Kaliski, and A. Rusch, “Pkcs #5: Password-based cryptography specification version 2.1,” Internet Requests for Comments, RFC Editor, RFC 8018, January 2017. [Online]. Available: https://www.rfc-editor.org/rfc/rfc8018.html.
  36.  A. Biryukov, D. Dinu, D. Khovratovich, and S. Josefsson, “The memory-hard Argon2 password hash and proof-of-work function,” Working Draft, IETF Secretariat, Internet-Draft draft-irtf-cfrg-argon2-12, 2020. [Online]. Available: https://tools.ietf.org/id/draft-irtf-cfrg-argon2-03. html.
  37.  P.-H. Lin, T. Hwang, and C.-W. Tsai, “Double CNOT attack on ‘Quantum key distribution with limited classical Bob’,” Int. J. Quantum Inf., vol. 17, no. 02, p. 1975001, 2019, doi: 10.1142/S0219749919750017.
  38.  D. Moody, L. Chen, S. Jordan, Y.-K. Liu, D. Smith, R. Perlner, and R. Peralta, “Nist report on post-quantum cryptography,” National Institute of Standards and Technology, U.S. Department of Commerce, Tech. Rep., 2016, doi: 10.6028/NIST.IR.8105.
  39.  P. Wang, S. Tian, Z. Sun, and N. Xie, “Quantum algorithms for hash preimage attacks,” Quantum Eng., vol. 2, no. 2, p. e36, 2020, doi: 10.1002/que2.36.
Go to article

Authors and Affiliations

Piotr Zawadzki
1
ORCID: ORCID

  1. Department of Telecommunications and Teleinformatics, Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland

This page uses 'cookies'. Learn more