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Number of results: 72
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Abstract

The linear 3D piezoelasticity theory along with active damping control (ADC) strategy are applied for non-stationary vibroacoustic response suppression of a doubly fluid-loaded functionally graded piezolaminated (FGPM) composite hollow cylinder of infinite length under general time-varying excitations. The control gain parameters are identified and tuned using Genetic Algorithm (GA) with a multi-objective performance index that constrains the key elasto-acoustic system parameters and control voltage. The uncontrolled and controlled time response histories due to a pair of equal and opposite impulsive external point loads are calculated by means of Durbin’s numerical inverse Laplace transform algorithm. Numerical simulations demonstrate the superior (good) performance of the GA-optimized distributed active damping control system in effective attenuation of sound pressure transients radiated into the internal (external) acoustic space for two basic control configurations. Also, some interesting features of the transient fluid-structure interaction control problem are illustrated via proper 2D time domain images and animations of the 3D sound field. Limiting cases are considered and accuracy of the formulation is established with the aid of a commercial finite element package as well as comparisons with the current literature.
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Authors and Affiliations

Seyyed M. Hasheminejad
Vahid Rabbani
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Abstract

This paper introduces a compelling new way to think about the education and practice of architecture. “Intelligent architecture” is founded on the basis of how the human mind perceives and interacts with the material world. Perhaps surprisingly, this scientifically-conceived process for architectural design and building leads to a more human architecture, one with a renewed respect for traditional systems of architectural design. Scientific insight into architecture’s origins and manner of conception gives us a profound appreciation of useful solutions embedded in our architectural heritage. This development reverses a century-old practice in industrial-modernist architecture, which advocated erasing the past rather than learning from it. By understanding essential human engagement with the built environment, architects are able to foster greater human wellbeing in the material structures they build.

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Authors and Affiliations

Nikos A. Salingaros
Kenneth G. Masden II
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Abstract

Prof. Edward Nęcka, a cognitive psychologist from the Jagiellonian University and Vice-President of the Polish Academy of Sciences, talks about cognitive misers, memory traps, and confusion in a myriad of new technologies.

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Authors and Affiliations

Edward Nęcka
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Abstract

Rapidly developing artificial intelligence technologies are expected to help us in various sectors of life, but their applications also entail certain risks.
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Authors and Affiliations

Piotr Kaczmarek-Kurczak
1

  1. Centre for Space Studies, Kozminski University– Kozminski ESA Lab in Warsaw
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Abstract

Artificial intelligence technologies are moving forward by leaps and bounds, right before our very eyes. How well prepared are we to treat them not as tools or rivals, but as autonomous partners?
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Authors and Affiliations

Artur Modliński
1
Aleksandra Przegalińska
2

  1. University of Łódź
  2. Kozminski University in Warsaw
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Abstract

When we look at works of art, our brain reacts to what we see in subconscious ways. Certain aspects of our perceptions can be captured using algebraic methods.
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Authors and Affiliations

Marek Kuś
1
Jacek Rogala
2
Joanna Dreszer
3
Beata Bajno
4

  1. PAS Center for Theoretical Physics in Warsaw
  2. Center for Research on Culture, Languageand Mind, University of Warsaw
  3. Institute of Psychology Nicolaus CopernicusUniversity in Toruń
  4. Association of Polish Artists and Designers,Warsaw Section
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Abstract

Modern technologies are now allowing education to seamlessly transfer into the virtual realm, creating a user-friendly environment where students can acquire new skills.
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Authors and Affiliations

Aureliusz Górski
1

  1. Founder & CEO of CampusAI in Warsaw
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Abstract

A Computational Intelligence (CI) approach is one of the main trending and potent data dealing out and processing instruments to unravel and resolve difficult and hard reliability crisis and it takes an important position in intelligent reliability analysis and management of data. Nevertheless, just few little broad reviews have recapitulated the current attempts of Computational Intelligence (CI) in reliability assessment in power systems. There are many methods in reliability assessment with the aim to prolong the life cycles of a system, to maximize profit and predict the life cycle of assets or systems within an organization especially in electric power distribution systems. Sustaining an uninterrupted electrical energy supply is a pointer of affluence and nationwide growth. The general background of reliability assessment in power system distribution using computational intelligence, some computational intelligence techniques, reliability engineering, literature reviews, theoretical or conceptual frameworks, methods of reliability assessment and conclusions was discussed. The anticipated and proposed technique has the aptitude to significantly reduce the needed period for reliability investigation in distribution networks because the distribution network needs an algorithm that can evaluate, assess, measure and update the reliability indices and system performance within a short time. It can also manage outages data on assets and on the entire system for quick and rapid decisions making as well as can prevent catastrophic failures. Those listed above would be taken care of if the proposed method is utilized. This overview or review may be deemed as valuable assistance for anybody doing research.
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Authors and Affiliations

Elijah Adebayo Olajuyin
1
ORCID: ORCID
Paul Kehinde Olulope
2
Emmanuel Taiwo Fasina
2

  1. Bamidele Olumilua University of Education, Science and Technology, Ikere Ekiti, Nigeria
  2. Ekiti State University, Ado Ekiti, Nigeria
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Abstract

This is a modest endeavour written from an engineering perspective by a nonphilosopher to set things straight if somewhat roughly: What does artificial intelligence boil down to? What are its merits and why some dangers may stem from its development in this time of confusion when, to quote Rémi Brague: “From the point of view of technology, man appears as outdated, or at least superfluous”?

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Authors and Affiliations

Jacek Koronacki
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Abstract

The evolution of the economy and the formation of Industry 4.0 lead to an increase in the importance of intangible assets and the digitization of all processes at energy enterprises. This involves the use of technologies such as the Internet of Things, Big Data, predictive analytics, cloud computing, machine learning, artificial intelligence, robotics, 3D printing, augmented reality etc. Of particular interest is the use of artificial intelligence in the energy sector, which opens up such prospects as increased safety in energy generation, increased energy efficiency, and balanced energy-generation processes. The peculiarity of this particular instrument of Industry 4.0 is that it combines the processes of digitalization and intellectualization in the enterprise and forms a new part of the intellectual capital of the enterprise. The implementation of artificial intelligence in the activities of energy companies requires consideration of the features and stages of implementation. For this purpose, a conceptual model of artificial intelligence implementation at energy enterprises has been formed, which contains: the formation of the implementation strategy; the design process; operation and assessment of artificial intelligence. The introduction of artificial intelligence is a large-scale and rather costly project; therefore, it is of interest to assess the effectiveness of using artificial intelligence in the activities of energy companies. Efficiency measurement is proposed in the following areas: assessment of economic, scientific and technical, social, marketing, resource, financial, environmental, regional, ethical and cultural effects as well as assessment of the types of risks associated with the introduction of artificial intelligence.
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Bibliography

Armenakis et al. 1993 – Armenakis, A.A., Harris, S.G. and Mossholder, K.W. 1993. Creating Readiness for Organizational Change. Human Relations 46, pp. 681–703.
Artificial intelligence the next digital frontier? McKinsey Global Institute. July 2017. 80 p. [Online] https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20 artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence- discussion-paper.ashx [Accessed: 2021-07-15].
Bakke, D. 2005. Joy a work: A Revolutionary Approach to Fun on the Job. Seattle: PVG, 314 pp.
Behrens, W. and Hawranek, P.M. 1978. Manual for the preparation of industrial feasibility studies. NY: Unated Nations, 404 pp.
Berger, R. 2013. How to Survive in the VUCA World. Hamburg: Roland Berger, 245 pp.
Blommaert, Т. and Broek, S. 2017. Management in Singularity: From linear to exponential management. Vakmedianet; 1 edition, 172 pp.
Borowski, P.F. 2016. Development strategies for electric utilities. Acta Energetica 4, pp. 16–21.
Borowski, P. 2021. Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.0. Processes 9(2), 381, DOI: 10.3390/pr9020381.
Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 352 pp.
Cheatham et al. 2019 – Cheatham, B., Javanmardian, K. and Samandari, H. 2019. Confronting the risks of artificial intelligence. [Online] https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence [Accessed: 2021-07-15].
Doroshuk, H. 2019. Organizational development: theory, methodology, practice (Організаційний розвиток: теорія, методологія, практика). Odesa: Osvita Ukrainy, 368 pp. (in Ukrainian).
Doroshuk, H. 2020. Reform of the electricity sector in Ukraine – liberalization of the market and corporatization of companies. Polityka Energetyczna – Energy Policy Journal 23(4), pр. 105–122. [Online] https://epj.min-pan.krakow.pl/Reform-of-the-electricity-sector-in-Ukraine-liberalization-of-the-market- and-corporatization,127664,0,2.html/ [Accessed: 2021-07-15].
Edvinsson, L. and Malone, M. 1997. Intellectual Capital: Realizing your Company’s True Value by Finding its Hidden Brainpower. New York, NY: Harper Collins.
Firer, S. and Williams, S.M. 2003. Intellectual capital and traditional measures of corporate performance. Journal of Intellectual Capital 4(3) , pp. 348–360, DOI: 10.1108/14691930310487806.
Hoe, S.L. 2019. The topicality of the learning organization: Is the concept still relevant today? [In:] The Oxford Handbook of the Learning Organization, Oxford University Press: Oxford, UK, pp. 18–32.
Jackson, P.C. Jr. 2019. Introduction to Artificial Intelligence. New York: Dover Publication Inc., 170 pp. Jensen, P.E. 2005. A contextual theory of learning and the learning organization. Knowledge Process Management 12, pp. 53–64, DOI: 10.1002/kpm.217.
Jones, M.T. 2017. A Beginner’s Guide to Artificial Intelligence, Machine Learning and Cognitive Computing. [Online] https://developer.ibm.com/articles/cc-beginner-guide-machine-learning-ai-cognitive/ [Accessed: 2021-07-15].
Kinelski, G. 2020. The main factors of successful project management in the aspect of energy enterprises’ efficiency in the digital economy environment. Polityka Energetyczna – Energy Policy Journal 23(3), pр. 5–20, DOI: 10.33223/epj/126435.
Koistinen, P. 2021. Toward learning organization – Practices in nuclear power plants. [In:] Human Factors in the Nuclear Industry, Elsevier BV: Amsterdam, The Netherlands, pp. 239–247.
Laloux, Fr. 2014. Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness. Brussels: Nelson&Parker, 379 pp.
Levy, F. 2009. A simulated approach to valuing knowledge capital. Washington: The George Washington University, 189 pp.
Nazari, J.A. and Herremans, I.M. 2007. Extending VAIC model: measuring intellectual capital components. Journal of Intellectual Capital 8(4), DOI: 10.1108/14691930710830774.
Oklander et al. 2018 – Oklander, M., Oklander, T., Yashkina, O., Pedko, I. and Chaikovska, M. 2018. Analysis of technological innovations in digital marketing. Eastern-European Journal of Enterprise Technologies 5/3 (95), pp. 80–91, DOI: 10.1088/1755-1315/440/2/022026.
Pan et al. 2020 – Pan, T., Hu, T. and Geng, J. 2020. View learning organization in a situational perspective. IOP Conference Series: Earth and Environmental Science 440 pp.
Piano, S.L. 2020. Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward. Humanities and Social Science Communication 7, DOI: 10.1057/s41599-020-0501-9.
Romer, P.M. 1994. The Origins of Endogenous Growth. The Journal of Economic Perspectives 8(1), pp. 3–22.
Sozontov et al. 2019 – Sozontov, A., Ivanova, M. and Gibadullin, A. 2019. Implementation of artificial intelligence in the electric power industry. [In:] E3S Web of Conferences 114, DOI: 10.1051/e3sconf/201911401009. EDP Sciences.
Toffler, A. 1984. The Third Wave. NY: Bantam, 560 pp.
Tortorella et al. 2020 – Tortorella, G.L., Vergara, A.M.C., Garza-Reyes, J.A. and Sawhney, R. 2020. Organizational learning paths based upon Industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics 219, pp. 284–294, DOI: 10.1016/j.ijpe.2019.06.023.
Von Ketelhod, Wöcke, A. 2008. The impact of electricity crises on the consumption behaviour of small and medium enterprises. Journal of Energy in Southern Africa 19(1), pp. 4–12
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Authors and Affiliations

Hanna Doroshuk
1
ORCID: ORCID

  1. Department of Menegement, Odessa Polytechnic State University, Ukraine
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Abstract

The article deals with the features and characteristics of intelligent systems for modelling business processes. Their classification was made and criteria for comparison were developed. According to the comparative analysis of existing expert systems for intelligent analysis, a reasonable choice of system for modelling business processes of a particular enterprise has been carried out. In general, it was found that the introduction of intelligent systems for modelling business processes of the enterprise and forecasting its activities for future allows management of the company to obtain relevant and necessary information for the adoption of effective management decisions and the development of a strategic plan.
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Bibliography

[1] B. V. Kuzmenko, and O. A. Chaikovska, “The systems of artificial intelligence,” Kyiv, Alterpres, 2006.
[2] H. F. Ivanchenko,“The systems of artificial intelligence,” Kyiv, KNEU, 2011.
[3] D. F. Liuher, “The artificial intelligence: strategies and methods of solving difficult issues,” Moscow, Vyliams (in Russian), 2003, pp. 866.
[4] A. A. Emelianov, E. A. Vlasova, and R. V. Duma, “Simulation modeling of economic systems,” Moscow, Finansy i statistika (in Russian), 2002.
[5] D. Waterman, “Guide to expert systems,” Moscow, Myr, 1989.
[6] A. A. Barsehian, M. S. Kupryianov, V. V. Stepanenko, and Y. Y. Kholod, “Methods and models of data analysis: OLAP and Data Mining,” St-Petersburg, BKhV, 2004.
[7] V. Mashkov and A. Smolarz and V. Lytvynenko, “The problem of system fault-tolerance,” Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska (IAPGOŚ), 4(4), pp. 41-44, 2014.
[8] Z. Omiotek and W. Wójcik, “The use of Hellwig's method for dimension reduction in feature space of thyroid ultrasound images,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 4(3), pp. 14-17, 2014.
[9] P. V. Poliakov, and S. A. Korobov, “Software tools for developing business plans: Project Expert system,” Volhohrad, vol. HU, pp. 48, 2004.
[10] H. S. Prokudin, M. T. Dekhtiaru, “Simulation modeling in informational systems,” Kyiv: NTU., no. 9, pp. 181–189, 2004.
[11] A. P. Rotshtein, and H. B. Rakytyanska, “Diagnosis problem solving using fuzzy relations,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 3, pp. 664-675, 2008.
[12] S. I. Vyatkin, A. N. Romanyuk, and Z. Y. Gotra, “Offsetting, relations, and blending with perturbation functions,” Proc. of SPIE 10445, 2017.
[13] L. I. Timchenko, S. V. Pavlov, N. I. Kokryatskaya, et al. “Bio-inspired approach to multistage image processing,” Proc. of SPIE 10445, 2017.
[14] M. F. Kirichenko, Yu. V. Krak, A. A. Polishchuk, “Pseudo inverse and projective matrices in problems of synthesis of functional transformers,” Kibernetika i Sistemnyj Analiz, vol. 40, no. 3, pp. 116-129, 2004.
[15] K. G. Selivanova, O. G. Avrunin and S. M. Zlepko, “Quality improvement of diagnosis of the electromyography data based on statistical characteristics of the measured signals,” Proc. of SPIE 10031, 2016.
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Authors and Affiliations

Svetlana A. Yaremko
1
Elena M. Kuzmina
1
Nataliia B. Savina
2
Konrad Gromaszek
3
Bakhyt Yeraliyeva
4
Gauhar Borankulova
4

  1. Vinnytsia Institute of Trade and Economics of Kyiv National University of Trade and Economics, Ukraine
  2. National University of Water and Environmental Engineering, Rivne, Ukraine
  3. Lublin University of Technology, Lublin, Poland
  4. Taraz State University after M.Kh.Dulaty, Taraz, Kazakhstan
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Abstract

The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company’s employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production.
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Bibliography

  1.  T. Pereira, J.V. Kennedy, and J. Potgieter, “A comparison of traditional manufacturing vs additive manufacturing, the best method for the job”, Procedia Manuf. 30, 11–18 (2019).
  2.  S. Mirzababaei and S. Pasebani, “A Review on Binder Jet Additive Manufacturing of 316L Stainless Steel”, J. Manuf. Mater. Process 3, 82, 1–36 (2019).
  3.  J-P. Kruth, M.C. Leu, and T. Nakagawa, “Progress in Additive Manufacturing and Rapid Prototyping”, CIRP Ann. 47(2), 525–540 (1998).
  4.  J. Maszybrocka, B. Gapiński, M. Dworak, G. Skrabalak, and A. Stwora, “Modelling, manufacturability and compression properties of the CpTi grade 2 cellular lattice with radial gradient TPMS architecture”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 719–727 (2019).
  5.  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. 68(2), 271–284 (2020).
  6.  I. Rojek, D. Mikołajewski, P. Kotlarz, M. Macko, and J. Kopowski, “Intelligent System Supporting Technological Process Planning for Machining”, in: Machine Modelling and Simulations MMS 2020. Lecture Notes in Mechanical Engineering. Springer, Cham, (to be published).
  7.  W. Grzesik, “Hybrid machining processes. Definitions, generation rules and real industrial importance”, Mechanik 5–6, 338‒342 (2018), [in Polish].
  8.  C.F. Tan, V.K. Kher, and N Ismail, “An expert system carbide cutting tools selection system for CNC lathe machine”, Int. Rev. Mech. Eng. 6(7), 1402–1405 (2012).
  9.  I. Rojek, E. Dostatni, and A. Hamrol, “Ecodesign of Technological Processes with the Use of Decision Trees Method”, in International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO 2017, CISIS 2017, ICEUTE 2017. Advances in Intelligent Systems and Computing, vol. 649, pp. 318–327, eds. H. Pérez García, J. Alfonso-Cendón, L. Sánchez González, H. Quintián and E. Corchado, Springer, Cham, 2018.
  10.  G. Halevi and K. Wang, “Knowledge based manufacturing system (KBMS)”, J. Intell. Manuf. 18(4), 467–474 (2007).
  11.  S. Butdee, Ch. Noomtong, and S. Tichkiewitch, “A Process Planning System with Feature Based Neural Network Search Strategy for Aluminum Extrusion Die Manufacturing”, Asian Int. J. Sci. Technol. Prod. Manuf. Eng. 2(1), 137–157 (2009).
  12.  I. Rojek, “Hybrid neural networks as prediction models”, in Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science ICAISC 2010, vol. 6114, pp. 88–95, eds. L. Rutkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, and J.M. Zurada, Springer, Berlin, Heidelberg, 2010.
  13.  D. Rajeev, D. Dinakaran, and S. Singh. “Artificial neural network based tool wear estimation on dry hard turning processes of aisi4140
  14. steel using coated carbide tool”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4), 553–559 (2017).
  15.  I. Rojek, “Classifier Models in Intelligent CAPP Systems”, in Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol. 59, pp. 311–319, eds. K.A. Cyran, S. Kozielski, J.F. Peters, U. Stańczyk, and A. Wakulicz-Deja, Springer, Berlin, Heidelberg, 2009.
  16.  S. Igari, F. Tanaka, and M. Onosato, “Customization of a Micro Process Planning System for an Actual Machine Tool based on Updating a Machining Database and Generating a Database-Oriented Planning Algorithm”, J. Trans. Inst. Syst. Control Inf. Eng. 26(3), 87–94 (2013).
  17.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in eco-design”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199–206 (2020).
  18.  M. Hazarika, S. Deb, U.S. Dixit, and J.P. Davim, “Fuzzy set-based set-up planning system with the ability for online learning”, Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. 225(2), 247–263 (2011).
  19.  N. Guo and M.C. Leu, “Additive manufacturing: Technology, applications and research needs”, Front. Mech. Eng. 215–243 (2013).
  20.  J. Yang, Y. Chen, W. Huang, and Y. Li, “Survey on artificial intelligence for additive manufacturing”, in 23rd International Conference on Automation and Computing (ICAC), Huddersfield, 2017, pp. 1–6, doi: 10.23919/IConAC.2017.8082053.
  21.  I.J. Petrick and T.W. Simpson, “3D printing disrupts manufacturing: how economies of one create new rules of competition”, Res.-Technol. Manage. 56(6), 12–16 (2013).
  22.  B. Stucker, “Additive manufacturing technologies: Technology introduction and business implications”, in Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2011 Symposium, pp. 19–21, National Academies Press: Washington, DC, USA, 2012.
  23.  Y. Wang, P. Zheng, and T. Peng, “Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives”, Sci. China Technol. Sci. 1–12 (2020).
  24.  H. Chen, and Y.F. Zhao, “Process parameters optimization for improving surface quality and manufacturing accuracy of binder jetting additive manufacturing process”, Rapid Prototyp. J. 22, 527–538 (2016).
  25.  M.A. Kaleem and M. Khan, “Significance of Additive Manufacturing for Industry 4.0 With Introduction of Artificial Intelligence in Additive Manufacturing Regimes”, in 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 2020, pp. 152–156, doi: 10.1109/IBCAST47879.2020.9044574.
  26.  L. Meng, B. McWilliams, and W. Jarosinski, “Machine Learning in Additive Manufacturing” A Review. JOM 72, 2363–2377 (2020).
  27.  Z. Jin, Z. Zhang, and G.X. Gu, “Automated Real-Time Detection and Prediction of Interlayer Imperfections in Additive Manufacturing Processes Using Artificial Intelligence”, Adv. Intell. Syst. 2(1), 1900130(1–7) (2020).
  28.  K. Wasmer, C. Kenel, C. Leinenbach, and S.A. Shevchik, “In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence.”, in Industrializing Additive Manufacturing – Proceedings of Additive Manufacturing in Products and Applications – AMPA2017, pp. 200–209, eds. M. Meboldt and C. Klahn, Springer, Cham, 2018, https://doi.org/10.1007/978-3-319- 66866-6_20.
  29.  C. Wang, S Li, D Zeng, and X. Zhu, “An Artificial-intelligence/Statistics Solution to Quantify Material Distortion for Thermal Compensation in Additive Manufacturing”, Cornell University, arXiv:2005.09084v1 [cs.CE], 2020.
  30.  P. Hong-Seok and N. Dinh-Son, “AI-Based Optimization of Process Parameters in Selective Laser Melting”, in Advances in Manufacturing Technology XXXII, eds. P. Thorvald and K. Case, IOS Press, 2018, doi: 10.3233/978-1-61499-902-7-119.
  31.  J. Kopowski, D. Mikołajewski, M. Macko, and I. Rojek, “Bydgostian hand exoskeleton – own concept and the biomedical factors”, Bio- Algorithms and Med-Systems 15(1), 20190003 (2019).
  32.  J. Kopowski, I. Rojek, D. Mikołajewski, and M. Macko, “3D Printed Hand Exoskeleton – Own Concept”, in Advances in Manufacturing II. MANUFACTURING 2019. Lecture Notes in Mechanical Engineering, pp. 306‒298, J. Trojanowska, O. Ciszak, J. Machado, and I. Pavlenko, Springer, Cham, 2019, https://doi.org/10.1007/978-3-030-18715-6_25.
  33.  R. Tadeusiewicz, R. Chaki, and N. Chaki, “Exploring Neural Networks with C#”, CRC Press Taylor & Francis Group, Boca Raton, 2014.
  34.  L.A. Zadeh, “Fuzzy sets. Information and Control”, 8, pp. 338–353 (1965).
  35.  S. Jige Quan, J. Park, A. Economou, and S. Lee, “Artificial intelligence-aided design: Smart Design for sustainable city development,” Environment and Planning B 46(8), 1581‒1599 (2019).
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Authors and Affiliations

Izabela Rojek
1
ORCID: ORCID
Dariusz Mikołajewski
1
ORCID: ORCID
Piotr Kotlarz
1
ORCID: ORCID
Marek Macko
2
ORCID: ORCID
Jakub Kopowski
1 3
ORCID: ORCID

  1. Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  2. Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  3. Faculty of Psychology, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
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Abstract

Machine learning methods, such as the random forests algorithm, have revolutionized how we analyze growing volumes of data. The algorithm can be usefully applied in studying… real forests.
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Authors and Affiliations

Łukasz Pawlik
1
Marcin K. Dyderski
2

  1. Institute of Earth Sciences,Faculty of Natural Sciences,University of Silesia in Katowice
  2. Institute of Dendrology,Polish Academy of Sciences in Kórnik
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Abstract

We all face a wide array of different choices every day of our lives. Asst. Prof. Miłosz Kadziński explains how artificial intelligence could be used to help us make decisions.

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Authors and Affiliations

Miłosz Kadziński
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Abstract

Dr. Aleksandra Przegalinska explains why we find humanoid robots so creepy and considers whether watching machines play football is actually fun.

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Authors and Affiliations

Aleksandra Przegalińska
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Abstract

The article provides an overview of Brain Computer Interface (BCI) solutions for intelligent buildings. A significant topic from the smart cities point of view. That solution could be implemented as one of the human-building interfaces. The authors presented an analysis of the use of BCI in specific building systems. The article presents an analysis of BCI solutions in the context of controlling devices/systems included in the Building Management System (BMS). The Article confirms the possibility of using this method of communication between the user and the building’s central unit. Despite many confirmations of repeatable device inspections, the article presents the challenges faced by the commercialization of the solution in buildings.
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Bibliography

[1] Gan V.J.L., Lo I.M.C., Ma J., Tse K.T., Cheng J.C.P., Chan C.M., Simulation optimisation towards energy efficient green buildings: Current status and future trends, Journal of Cleaner Production, Elsevier Ltd, vol. 254, p. 120012 (2020), DOI: 10.1016/j.jclepro.2020.120012.
[2] Ericsson, 10 hot consumer trends 2030 (2019), https://www.ericsson.com/en/reports-and-papers/consumerlab/reports/10-hot-consumer-trends-2030, accessed December 29, 2020.
[3] Ramadan R.A., Vasilakos A.V., Brain computer interface: control signals review, Neurocomputing, vol. 223, pp. 26–44 (2017), DOI: 10.1016/j.neucom.2016.10.024.
[4] Donoghue J.P., Connecting cortex to machines: Recent advances in brain interfaces, Nature Neuroscience, Nature Publishing Group, vol. 5, no. 11s, pp. 1085–1088 (2002), DOI: 10.1038/nn947.
[5] Schwartz A.B., Cortical neural prosthetics, Annual Review of Neuroscience, vol. 27, Annual Reviews, pp. 487–507 (2004), DOI: 10.1146/annurev.neuro.27.070203.144233.
[6] Nicolas-Alonso L.F., Gomez-Gil J., Brain computer interfaces, a review, Sensors, vol. 12, no. 2, pp. 1211–1279 (2012), DOI: 10.3390/s120201211.
[7] Jafar M.R., Nagesh D.T., A beginner’s guide to Brain Machine Interface – Review, SSRN, pp. 6–10 (2020), DOI: 10.2139/ssrn.3645960.
[8] Shi K., GaoN., Li Q., Bai O., A P300 brain-computer interface design for virtual remote control system, 2017 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017, pp. 326–329 (2017), DOI: 10.1109/CCSSE.2017.8087950.
[9] Birbaumer N., Brain-computer-interface research: Coming of age, Clinical Neurophysiology, vol. 117, no. 3, pp. 479–483 (2006), DOI: 10.1016/j.clinph.2005.11.002.
[10] Marx S. et al., Validation of mobile eye-tracking as novel and efficient means for differentiating progressive supranuclear palsy from Parkinson’s disease, Frontiers in Behavioral Neuroscience, vol. 6, no. DEC (2012), DOI: 10.3389/fnbeh.2012.00088.
[11] Birbaumer N., Cohen L.G., Brain-computer interfaces: Communication and restoration of movement in paralysis, in Journal of Physiolog., vol. 579, no. 3, pp. 621–636 (2007), DOI: 10.1113/jphysiol.2006.125633.
[12] Bhemjibhaih D.P., Sanjay G.D., Sreejith V., Prakash B., Brain-computer interface based home automation system for paralysed people, 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS 2018), pp. 230–233 (2019), DOI: 10.1109/RAICS.2018.8635060.
[13] Gao X., Xu D., Cheng M., Gao S., A BCI-based environmental controller for the motion-disabled, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137–140 (2003), DOI: 10.1109/TNSRE.2003.814449.
[14] Daly J.J., Huggins J.E., Brain-computer interface: Current and emerging rehabilitation applications, Archives of Physical Medicine and Rehabilitation, W.B. Saunders, vol. 96, no. 3., pp. S1–S7 (2015), DOI: 10.1016/j.apmr.2015.01.007.
[15] Bonneau L., Ramahandry V., Probst T., Pedersen L., Dakkak-Arnoux B., Smart Building: Energy Efficiency Application (2017).
[16] Minh K.N., Van D.L., Duc T.D., An T.N., An advanced IoT system for monitoring and analysing chosen power quality parameters in micro-grid solution, Archives of Electrical Engineering, vol. 70, no. 1, pp. 173–188 (2021), DOI: 10.24425/aee.2021.136060.
[17] Hannan M.A. et al., A review of internet of energy based building energy management systems: Issues and recommendations, IEEE Access, vol. 6, pp. 38997–39014 (2018), DOI: 10.1109/ACCESS.2018.2852811.
[18] Minoli D., Sohraby K., Occhiogrosso B., IoT Considerations, Requirements, and Architectures for Smart Buildings-Energy Optimization and Next-Generation Building Management Systems, IEEE Internet of Things Journal, vol. 4, no. 1, pp. 269–283 (2017), DOI: 10.1109/JIOT.2017.2647881.
[19] Kastner W., Neugschwandtner G., Soucek S., Newman H.M., Communication systems for building automation and control, Proceedings of the IEEE, vol. 93, no. 6, pp. 1178–1203 (2005), DOI: 10.1109/JPROC.2005.849726.
[20] Wang M., Qiu S., Dong H., Wang Y., Design an IoT-based building management cloud platform for green buildings, Proceedings – 2017 Chinese Automation Congress, CAC 2017, vol. 2017, January, pp. 5663–5667 (2017), DOI: 10.1109/CAC.2017.8243793.
[21] Lilis G., Conus G., Kayal M., A distributed, event-driven building management platform on web technologies, Proceedings of 1st International Conference on Event-Based Control, Communication and Signal Processing, EBCCSP 2015 (2015), DOI: 10.1109/EBCCSP.2015.7300702.
[22] Ahmad M.W., Mourshed M., Yuce B., Rezgui Y., Computational intelligence techniques for HVAC systems: A review, Building Simulation, vol. 9, no. 4, pp. 359–398 (2016), DOI: 10.1007/s12273-016-0285-4.
[23] Chen Z., Xu P., Feng F., Qiao Y., LuoW., Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings, Building Simulation, vol. 14, no. 1, pp. 63-74 (2021), DOI: 10.1007/s12273-019-0599-0.
[24] Sun F., Yu J., Indoor intelligent lighting control method based on distributed multi-agent framework, Optik, vol. 213, no. March, p. 164816 (2020), DOI: 10.1016/j.ijleo.2020.164816.
[25] Khanchuea K., Siripokarpirom R., A Multi-Protocol IoT Gateway and WiFi/BLE Sensor Nodes for Smart Home and Building Automation: Design and Implementation, 2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), pp. 1–6 (2019), DOI: 10.1109/ICTEmSys.2019.8695968.
[26] Anwar F., Boby R.I., Rashid M.M., Alam M.M., Shaikh Z., Network-Based Real-time Integrated Fire Detection and Alarm (FDA) System with Building Automation, IOP Conference Series: Materials Science and Engineering, vol. 260, no. 1 (2017), DOI: 10.1088/1757-899X/260/1/012025.
[27] Luo R.C., Lin S.Y., Su K.L., A multiagent multisensor based security system for intelligent building, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol. 2003-January, pp. 311–316 (2003), DOI: 10.1109/MFI-2003.2003.1232676.
[28] Hui H., Ding Y., Shi Q., Li F., Song Y., Yan J., 5G network-based Internet of Things for demand response in smart grid: A survey on application potential, Applied Energy, vol. 257, p. 113972 (2020), DOI: 10.1016/j.apenergy.2019.113972.
[29] Chauhan R.K., Chauhan K., Building automation system for grid-connected home to optimize energy consumption and electricity bill, Journal of Building Engineering, vol. 21, no. May 2018, pp. 409-420 (2019), DOI: 10.1016/j.jobe.2018.10.032.
[30] Afroz Z., Shafiullah G.M., Urmee T., Higgins G., Modeling techniques used in building HVAC control systems: A review, Renewable and Sustainable Energy Reviews, vol. 83, pp. 64–84, Elsevier Ltd (2018), DOI: 10.1016/j.rser.2017.10.044.
[31] Amana H.A.C., ASXC16 Air Conditioner Best Suited For Homes|Amana (2021), https://www.amanahac.com/products/air-conditioners/16-seer-asxc16, accessed March 23, 2021.
[32] Goodman, Air Conditioner/GSXC18/Up To 18 SEER/Goodman, 2021, https://www.goodmanmfg.com/products/air-conditioners/18-seer-dsxc18, accessed March 23, 2021.
[33] Bryant, Two stage air conditioners – air conditioners/Bryant (2021), https://www.bryant.com/en/us/products/air-conditioners/189bnv, accessed March 23, 2021.
[34] Trane, Air Conditioner/$400 Rebate on Quietest AC/Trane®Cooling (2021), https://www.trane.com/residential/en/products/air-conditioners/xv18-air-conditioners, accessed March 23, 2021.
[35] Cheng Y., Fang C., Yuan J., Zhu L., Design and application of a smart lighting system based on distributed wireless sensor networks, Applied Sciences, Switzerland, vol. 10, no. 23, pp. 1–21 (2020), DOI: 10.3390/app10238545.
[36] Kaminska A., Ozadowicz A., Lighting control including daylight and energy efficiency improvements analysis, Energies, vol. 11, no. 8 (2018), DOI: 10.3390/en11082166.
[37] Toub M., Reddy C.R., Robinett R.D., Shahbakhti M., Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions, Energies, vol. 14, no. 3, p. 730 (2021), DOI: 10.3390/en14030730.
[38] Kang J., Han J., Park J.H., Design of IP camera access control protocol by utilizing hierarchical group key, Symmetry, vol. 7, no. 3, pp. 1567–1586 (2015), DOI: 10.3390/sym7031567.
[39] Froiz-Míguez I., Fernández-Caramés T.M., Fraga-Lamas P., Castedo L., Design, implementation and practical evaluation of an iot home automation system for fog computing applications based on MQTT and ZigBee-WiFi sensor nodes, Sensors, Switzerland, vol. 18, no. 8, pp. 1–42 (2018), DOI: 10.3390/s18082660. [40] KNX Association KNX Association [official website] (2020), https://www.knx.org/knx-en/forprofessionals/index.php, accessed January 06, 2021.
[41] Tran T.N., Grid Search of Convolutional Neural Network model in the case of load forecasting, Archives of Electrical Engineering, vol. 70, no. 1, pp. 25–36 (2021), DOI: 10.24425/aee.2021.136050.
[42] Prashant P., Joshi A., GandhiV., Brain computer interface: A review, (2016), DOI: 10.1109/NUICONE.2015.7449615.
[43] Lee S., Shin Y., Woo S., Kim K., Lee H.-N., Review of Wireless Brain-Computer Interface Systems, Brain-Computer Interface Systems – Recent Progress and Future Prospects (2013), DOI: 10.5772/56436.
[44] Hall J.E., Guyton and Hall Textbook of Medical Physiology, Saunders, vol. 13 (2015).
[45] Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M., Brain-computer interfaces for communication and control, Clinical Neurophysiology, Elsevier, vol. 113, no. 6, pp. 767–791 (2002), DOI: 10.1016/S1388-2457(02)00057-3.
[46] Laureys S., Boly M., Tononi G., Functional neuroimaging in The Neurology of Consciousness, pp. 31–42 (2009), DOI: 10.1016/B978-0-12-374168-4.00003-4.
[47] Sanei S., Chambers J.A., EEG Signal Processing (2007).
[48] Baillet S., Mosher J.C., Leahy R.M., Electromagnetic brain mapping, IEEE Signal Processing Magazine, vol. 18, no. 6, pp. 14–30 (2001), DOI: 10.1109/79.962275.
[49] Kübler A., Kotchoubey B., Kaiser J., Birbaumer N., Wolpaw J.R., Brain-computer communication: Unlocking the locked, Psychological Bulletin, vol. 127, no. 3, pp. 358–375 (2001), DOI: 10.1037/0033- 2909.127.3.358.
[50] Anand B.K., Chhina G.S., Singh B., Some aspects of electroencephalographic studies in Yogis, Electroencephalography and Clinical Neurophysiology, vol. 13, no. 3, pp. 452–456 (1961), DOI: 10.1016/0013-4694(61)90015-3.
[51] Aftanas L.I., Golocheikine S.A., Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: High-resolution EEG investigation of meditation, Neuroscience Letters, vol. 310, no. 1, pp. 57–60 (2001), DOI: 10.1016/S0304-3940(01)02094-8.
[52] Kübler A., Neumann N., Wilhelm B., Hinterberger T., Birbaumer N., Predictability of braincomputer communication, Journal of Psychophysiology, vol. 18, no. 2–3, pp. 121–129 (2004), DOI: 10.1027/0269-8803.18.23.121.
[53] Ortiz V.H., Tapia J.J., Mathematical model for classification of EEG signals, Optics and Photonics for Information Processing IX, vol. 9598, no. September 2015, p. 95981C (2015), DOI: 10.1117/12.2187092.
[54] EMOTIV/Brain Data Measuring Hardware and Software Solutions (2020), https://www.emotiv.com, accessed January 06, 2021.
[55] EEG – ECG – Biosensors (2020), http://neurosky.com, accessed January 06, 2021.
[56] MuseTM – Meditation Made Easy with the Muse Headband (2020), https://choosemuse.com, accessed January 06, 2021.
[57] OpenBCI – Open Source Biosensing Tools (EEG, EMG, EKG, and more) (2020), https://openbci.com, accessed January 06, 2021.
[58] DSI Series Dry EEG Headsets – Wearable Sensing (2020), https://wearablesensing.com, accessed January 06, 2021.
[59] ANT Neuro inspiring technology for the human brain (2020), https://www.ant-neuro.com, accessed January 06, 2021.
[60] Reinventing brain health, Neuroelectrics (2020), https://www.neuroelectrics.com, accessed January 06, 2021.
[61] Fully mobile EEG devices mBrainTrain, Home new (2020), https://mbraintrain.com, accessed January 06, 2021.
[62] Advanced Brain Monitoring (2020), https://www.advancedbrainmonitoring.com, accessed January 06, 2021.
[63] Dry EEG Headset, CGX, United States (2020), https://www.cgxsystems.com/home-old, accessed January 06, 2021.
[64] Ghodake A.A., Shelke S.D., Brain controlled home automation system (2016), DOI: 10.1109/ISCO.2016.7727050.
[65] Lee W.T., Nisar H., Malik A.S., Yeap K.H., A brain computer interface for smart home control, Proceedings of the International Symposium on Consumer Electronics, ISCE (2013), pp. 35–36, DOI: 10.1109/ISCE.2013.6570240.
[66] Holzner C., Guger C., Edlinger G., Grönegress C., Slater M., Virtual smart home controlled by thoughts, Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2009, pp. 236–239 (2009), DOI: 10.1109/WETICE.2009.41.
[67] Edlinger G., Holzner C., Guger C., Groenegress C., Slater M., Brain-computer interfaces for goal orientated control of a virtual smart home environment, 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER’09, pp. 463–465 (2009), DOI: 10.1109/NER.2009.5109333.
[68] Alrajhi W., Alaloola D., Albarqawi A., Smart home: Toward daily use of BCI-based systems (2017), DOI: 10.1109/ICIHT.2017.7899002.
[69] Alshbatat A.I.N., Vial P.J., Premaratne P., Tran L.C., EEG-based Brain-computer Interface for Automating Home Appliances, Journal of Computers, vol. 9, no. 9 (2014), DOI: 10.4304/jcp.9.9.2159-2166.
[70] Virdi P., Syal P., Kumari P., Home automation control system implementation using SSVEP based brain computer interface, Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017, pp. 1068–1073 (2018), DOI: 10.1109/ICICI.2017.8365304.
[71] Boucha D., Amiri A., Chogueur D., Controlling electronic devices remotely by voice and brain waves, Proceedings of the 2017 International Conference on Mathematics and Information Technology, ICMIT 2017, pp. 38–42 (2018), DOI: 10.1109/MATHIT.2017.8259693.
[72] Putze F., Weib D., Vortmann L.M., Schultz T., Augmented reality interface for smart home control using SSVEP-BCI and eye gaze, Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics, pp. 2812–2817 (2019), DOI: 10.1109/SMC.2019.8914390.
[73] Kosmyna N., Tarpin-Bernard F., Bonnefond N., Rivet B., Feasibility of BCI control in a realistic smart home environment, Frontiers in Human Neuroscience, vol. 10, p. 10 (2016), DOI: 10.3389/fnhum.2016.00416.
[74] Cortez S.A., Flores C., Andreu-Perez J., A Smart Home Control Prototype Using a P300-Based Brain– Computer Interface for Post-stroke Patients, Smart Innovation, Systems and Technologies, vol. 202, pp. 131–139 (2021), DOI: 10.1007/978-3-030-57566-3_13.
[75] Miah M.O., Khan S.S., Shatabda S., Al Mamun K.A., Farid D.M., Real-Time EEG Classification of Voluntary Hand Movement Directions using Brain Machine Interface, Proceedings of 2019 IEEE Region 10 Symposium, TENSYMP 2019, pp. 473–478 (2019), DOI: 10.1109/TENSYMP46218.2019.8971255.
[76] Käthner I. et al., A P300 BCI for e – inclusion, cognitive rehabilitation and smart home control, pp. 60–63 (2014), DOI: 10.3217/978-3-85125-378-8-15.
[77] Edlinger G., Holzner C., Guger C., A hybrid brain-computer interface for smart home control, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6762 LNCS, no. PART 2, pp. 417–426 (2011), DOI: 10.1007/978-3- 642-21605-3_46.
[78] Kim H.J., Lee M.H., Lee M., A BCI based Smart Home System Combined with Event-related Potentials and Speech Imagery Task (2020), DOI: 10.1109/BCI48061.2020.9061634.
[79] Park S., Cha H.S., Im C.H., Development of an Online Home Appliance Control System Using Augmented Reality and an SSVEP-Based Brain-Computer Interface, IEEE Access, vol. 7, pp. 163604– 163614 (2019), DOI: 10.1109/ACCESS.2019.2952613.
[80] Vaughan T.M. et al., The wadsworth BCI research and development program: At home with BCI, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 229–233 (2206), DOI: 10.1109/TNSRE.2006.875577.
[81] Qin L.Y. et al., Smart home control for disabled using brain computer interface, International Journal of Integrated Engineering, vol. 12, no. 4, pp. 74–82 (2020), DOI: 10.30880/ijie.2019.11.06.004.
[82] Dobosz K., Wittchen P., Brain-computer interface for mobile devices, Journal of Medical Informatics and Technologies, vol. 24, pp. 215–222 (2015).
[83] Kawa B., Borkowski P., Data analysis of the latency in the building with using telecommunication technology, Przegl˛ad Elektrotechniczny, vol. 1, no. 2, pp. 131–137 (2021), DOI: 10.15199/48.2021.02.28.

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Authors and Affiliations

Bartłomiej Kawa
1
ORCID: ORCID
Piotr Borkowski
1
ORCID: ORCID
Michał Rodak
1
ORCID: ORCID

  1. Lodz University of Technology, Poland
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Abstract

Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models.
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Authors and Affiliations

V. Devisurya
1
R. Devi Priya
1
N. Anitha
1

  1. Department of Information Technology, Kongu Engineering College, Perundurai, India
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Abstract

This paper presents a deep learning-based image texture recognition system. The methodology taken in this solution is formed in a bottom-up manner. It means we swipe a moving window through the image in order to categorize if a given region belongs to one of the classes seen in the training process. This categorization is done based on the Deep Neural Network (DNN) of fixed architecture. The training process is fully automated regarding the training data preparation, investigation of the best training algorithm, and its hyper-parameters. The only human input to the system is the definition of the categories for further recognition and generation of the samples (region markings) in the external application chosen by the user. The system is tested on road surface images where its task is to categorize image regions to a different road category (e.g. curb, road surface damage, etc.) and is featured with 90% and above accuracy.

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Authors and Affiliations

R. Kapela
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Abstract

Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting.
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Authors and Affiliations

Izabela Rojek
1
ORCID: ORCID
Dariusz Mikołajewski
1
ORCID: ORCID
Joanna Nowak
2
ORCID: ORCID
Zbigniew Szczepański
2
ORCID: ORCID
Marek Macko
2
ORCID: ORCID

  1. Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
  2. Faculty of Mechatronics, Kazimierz Wielki University, Bydgoszcz, Poland
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Abstract

Artificial Intelligence begins to play an increasingly important role in medicine, in particular in diagnostics, therapy selection and drug design. This article shows how the latest machine learning algorithms support the work of physicians and pharmacists. However, the effective implementation of Artificial Intelligence methods in everyday medical practice requires overcoming a number of barriers. These challenges are discussed in the article. The objectives and functioning of the Artificial Intelligence Center in Medicine of the Medical University of Bialystok were also discussed, as an example of Polish contribution to the development of the latest computer algorithms supporting diagnostics and therapy.
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Authors and Affiliations

Konrad Wojdan
1 2
Marcin Moniuszko
3

  1. Politechnika Warszawska, Instytut Techniki Cieplnej
  2. Transition Technologies Science sp. z o.o.
  3. Uniwersytet Medyczny w Białymstoku
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Abstract

Science and technology frequently contribute to one another: scientific advances lead to the development of new technologies, and new technologies broaden the experimental potential of science, enabling advancement of research. This is a motivation behind introduction of the concept of technoscience addressing the integration of science and technology – the process progressing from the beginning of the twentieth century, which has been the source of extraordinary achievements of our civilisation, but – at the same time – has engendered global socioeconomic transformations whose negative side effects may endanger humanity. This paper is devoted to an outline of ethical challenges implied by the development of technoscience, with special emphasis of those which are rooted in the development of information technologies. It is suggested that those challenges should be met by people of technoscience in a concerted effort undertaken with philosophers and educators.
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Authors and Affiliations

Roman Z. Morawski
1

  1. Politechnika Warszawska, Wydział Elektroniki Technik Informacyjnych
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Abstract

The aim of the study was to investigate the relationships between emotional intelligence (EI) and temperament. It was assumed that the two main components of EI – experiential and strategic – have different temperament correlates. One hundred and four Polish university students aged 19 to 26 completed self-descriptive questionnaires of temperament and emotional intelligence. The results confirmed that the relationship with temperament depends on the examined component of EI. Acceptance of emotions (which is a subcomponent of experiential EI) only correlated with two temperamental traits – activity and briskness. Many more dependencies were found in relation to strategic EI. Endurance, strength of inhibition, sensory sensitivity and perseveration turned out to be significant predictors of emotional control, which jointly explained 44% of the variance in results, while perseveration and sensory sensitivity explained 28% of the variance in results on the understanding emotions scale. Based on the results obtained, it can be assumed that the configuration of temperament traits that determines a high capacity for processing stimulation is most conductive to strategic EI. Other propitious traits include those that determine the speed of neural processes, flexibility and ease of adaptation to changing conditions as well as a low sensitivity threshold to sensory stimulus.

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Authors and Affiliations

Anna Matczak
Katarzyna A. Knopp
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Abstract

The process of cognitive aging in global sense can be characterised by changes of the fluid and crystallised intelligence. In the context of this explanation the basic question is which cognitive functions and regulatory mechanisms play the basic role of the determinants for cognitive aging. Probable, mechanism of associative memory play a central role in top-down direction of cognitive processing. This type of memory connect the resources/networks of long term memory with the current processing in working memory. Another set of mechanisms concerns with bottom-up direction based on procedural memory, which is fundamental for the functioning of the mind as whole (Tulving theory,1985). Unfortunately, our knowledge about associative memory and its relations to working and procedural memory is incomplete and unclear. The importance of associative memory are partly, empirically supported by classic research on decreasing the cognitive components of intelligence aging, since the fluid and crystallized intelligence where discovered (Horn, Cattell, 1967). Changes of the mind functioning and its cognitive growth/aging can be characterised as a complex chain from primary, biologically determined mind, through Piagetian and Vygotsky’s type of mind to relatively balanced mind.

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Authors and Affiliations

Czesław S. Nosal

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