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Number of results: 8
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Abstract

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DCS.
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Authors and Affiliations

Berik Akhmetov
1
Valerii Lakhno
2
Vitalyi Chubaievskyi
3
Serhii Kaminskyi
3
Saltanat Adilzhanova
4
Moldir Ydyryshbayeva
4

  1. Yessenov University, Aktau, Kazakhstan
  2. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine
  3. Kyiv National University of Trade and Economics, Kyiv, Ukraine
  4. Al-Farabi Kazakh National University, Almaty, Kazakhstan
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Abstract

The article is devoted to some critical problems of using Bayesian networks for solving practical problems, in which graph models contain directed cycles. The strict requirement of the acyclicity of the directed graph representing the Bayesian network does not allow to efficiently solve most of the problems that contain directed cycles. The modern theory of Bayesian networks prohibits the use of directed cycles. The requirement of acyclicity of the graph can significantly simplify the general theory of Bayesian networks, significantly simplify the development of algorithms and their implementation in program code for calculations in Bayesian networks..
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Bibliography

[1] A. Nafalski and A.P. Wibawa, “Machine translation with javanese speech levels’ classification,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, vol. 6, no 1, pp 21-25, 2016. https://doi.org/10.5604/20830157.1194260
[2] Z.Omiotek and P. Prokop, “The construction of the feature vector in the diagnosis of sarcoidosis based on the fractal analysis of CT chest images,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, vol. 9, no. 2, pp. 16-23, 2019. https://doi.org/10.5604/01.3001.0013.2541
[3] A. Litvinenko, O. Mamyrbayev, N. Litvinenko, A. Shayakhmetova, “Application of Bayesian networks for estimation of individual psychological characteristics,” Przeglad Elektrotechniczny, vol. 95, no. 5, pp. 92-97, 2019
[4] X.Q. Cai, X.Y. Wu, X. Zhou, “Stochastic scheduling subject to breakdown-repeat breakdowns with incomplete information,” Operations Research, vol. 57, no. 5, pp. 1236–1249, 2009. doi: 10.1287/opre.1080.0660
[5] K.W. Fornalski, “The Tadpole Bayesian Model for Detecting Trend Changes in Financial Quotations,” R&R Journal of Statistics and Mathematical Sciences, vol. 2, no. 1, pp. 117–122, 2016.
[6] J. Pearl “Artificial Intelligence Applications”, in How to Do with Probabilities what people say you can't,/ Editor Weisbin C.R., IEEE, North Holland, pp. 6–12, 1985.
[7] J. Pearl “Probabilistic Reasoning in Intelligent Systems”. San Francisco: Morgan Kaufmann Publishers, 1988,
[8] A. Tulupiev “Algebraic Bayesian networks,” in “Logical-probabilistic approach to modeling knowledge bases with uncertainty,” SPb.: SPIIRAS, 2000.
[9] S. Nikolenko, A. Tulupiev “The simplest cycles in Bayesian networks: Probability distribution and the possibility of its contradictory assignment,” SPIIRAS. Edition 2, 2004. vol.1.
[10] F.V. Jensen, T.D. Nielsen “Bayesian Networks and Decision Graphs,” Springer, 2007.
[11] D. Barber, “Bayesian Reasoning and Machine Learning,” 2017, 686 p. http://web4.cs.ucl.ac.uk/ staff/D.Barber/ textbook/020217.pdf
[12] R.E. Neapolitan “Learning Bayesian Networks,” 704p. http://www.cs.technion.ac.il/~dang/books/Learning%20Bayesian%20Networks(Neapolitan,%20Richard).pdf
[13] O. Mamyrbayev, M. Turdalyuly, N. Mekebayev, and et al. “Continuous speech recognition of kazakh language», AMCSE 2018 Int. conf. On Applied Mathematics, Computational Science and Systems Engineering, Rom, Italy, 2019, vol. 24, pp. 1-6.
[14] A. Litvinenko, N. Litvinenko, O. Mamyrbayev, A. Shayakhmetova, M. Turdalyuly “Clusterization by the K-means method when K is unknown,” Inter. Conf. Applied Mathematics, Computational Science and Systems Engineering. Rome, Italy, 2019, vol. 24, pp. 1-6.
[15] O.Ore “Graph theory,” Мoscow: Science, 1980, 336 p.
[16] Ph. Kharari “Graph theory,” Мoscow: Mir, 1973, 300 p.
[17] V. Gmurman “Theory of Probability and Mathematical Statistics: Tutorial,” Moscow: 2003, 479 p.
[18] A.N. Kolmogorov “Theory: Manual,” in “Basic Concepts of Probability,” Moscow: Science, 1974.
[19] N. Litvinenko, A. Litvinenko, O. Mamyrbayev, A. Shayakhmetova “Work with Bayesian Networks in BAYESIALAB,” Almaty: IPIC, 2018, 311 p. (in Rus). ISBN 978-601-332-206-3.

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Authors and Affiliations

Assem Shayakhmetova
1 2
Natalya Litvinenko
3
Orken Mamyrbayev
1
Waldemar Wójcik
4 5
Dusmat Zhamangarin
6

  1. Institute of Information and Computational Technology, 050010 Almaty, Kazakhstan
  2. Al-Farabi Kazakh National University, Almaty, Kazakhstan
  3. Information and Computational Technology, 050010 Almaty, Kazakhstan
  4. Institute of Information and Computational Technologies CS MES RK, Almaty
  5. Lublin Technical University, Poland
  6. Kazakh University Ways of Communications, Kazakhstan
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Abstract

A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model.
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Authors and Affiliations

Olena Kryvoruchko
1
Alona Desiatko
1
Igor Karpunin
1
Dmytro Hnatchenko
1
Myroslav Lakhno
2
Feruza Malikova
3
Ayezhan Turdaliev
4

  1. State University of Trade and Economics, Kyiv, Ukraine
  2. National University of Life and EnvironmentalSciences of Ukraine, Kyiv
  3. Almaty Technological University, Almaty,Kazakhstan
  4. Kazakh University of Railways andTransportation, Almaty, Kazakhstan
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Abstract

Nowadays, the main challenge in maintenance is to establish a dynamic maintenance strategy to significantly track and improve the performance measures of multi-state systems in terms of production, quality, security and even the environment. This paper presents a quantitative approach based on Dynamic Bayesian Network (DBN) to model and evaluate the maintenance of multi-state system and their functional dependencies. According to transition relationships between the system states modeled by the Markov process, a DBN model is established. The objective is to evaluate the reliability and the availability of the system with taking into account the impact of maintenance strategies (perfect repair and imperfect repair). Using the proposed approach, the dynamic probabilities of system states can be determined and the subsystems contributing to system failure can also be identified. A practical application is demonstrated by a case study of a blower system. Through the result of the diagnostic inference, to improve the performances of the blower, the critical components C, F, W, and P should be given more attention. The results indicate also that the perfect repair strategy can improve significantly the performances of the blower, while the imperfect repair strategy cannot degrade the performances in comparison to the perfect repair strategy. These results show the effectiveness of this approach in the context of a predictive evaluation process and in providing the opportunity to evaluate the impact of the choices made on the future measurement of systems performances. Finally, through diagnostic analysis, intervention management and maintenance planning are managed efficiently and optimally.
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Authors and Affiliations

Zakaria Dahia
Ahmed Bellaouar
Jean-Paul Dron
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Abstract

The numerous overruns of the investor’s budget during tenders for the construction of railway infrastructure in Poland resulted in the widespread use of a new procedure for awarding public contracts – electronic auction. This procedure has many advantages and potential risks. One of the biggest benefits for an investor is the potential gains from reducing bids. Contractors competing against each other allow for the achievement of optimal prices for the planned construction investment. However, this may cause the originally calculated risks, should they materialize, lead to significant budget overruns. This, in turn, may imply further negative consequences, including exceeding the assumed investment deadlines. The article presents a method of modeling the influence of an electronic auction on a tender procedure with the use of a Bayesian network. Data from completed tender procedures announced by the PKP Polskie Linie Kolejowe S.A. were used to build the network. The created network was then validated, verified and calibrated using new data from 8 tender procedures.
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Authors and Affiliations

Filip Janowiec
1
ORCID: ORCID

  1. Cracow University of Technology, Faculty of Civil Engineering, Ul.Warszawska 24, 31-155 Cracow, Poland
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Abstract

In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multisource information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequencydomain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multifeature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.
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Bibliography

[1] Yao Yuantao, Wang Jin, Xie Min, Hu Liqin and Wang Jianye, ”A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant”, Annals of Nuclear Energy, 2020, 141, 1-9.
[2] Lei Koua, Chuang Liua, Guo-wei Caia, Zhe Zhangb, ”Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression”, Electric Power Systems Research, 2020, 185, 1-9.
[3] Haibo Zhang, Kai Jia, Weijin Shi, Jianzhao Guo, Weizhi Su and Li Zhang, ”Power Grid Fault Diagnosis Based on Information Theory and Expert System”, Proceedings of the CSU-EPSA,, 2017, 29(8), 111-118.
[4] Jianfeng Zhou, Genserik Reniers and Laobing Zhang, ”A weighted fuzzy Petri-net based approach for security risk assessment in the chemical industry”, Chemical Engineering Science, 2017, 174, 136-145.
[5] Sen Wang and Xiaorun Li, ”Circuit Breaker Fault Detection Method Based on Bayesian Approach”, Industrial Control Computer, 2018, 31(4), 147-151.
[6] Kaikai Gu and Jiang Guo, ”Transformer Fault Diagnosis Method Based on Compact Fusion of Fuzzy Set and Fault Tree”, High Voltage Engineering , 2014, 40(05), 1507-1513.
[7] Jun Miao, Qikun Yuan, Liwen Liu, Zhipeng You and Zhang Wang, ”Research on robot circuit fault detection method based on dynamic Bayesian network”, Electronic Design Engineering, 2020, 28(9), 184- 188.
[8] Bangcheng Lai and Genxiu Wu, ”The Evidence Combination Method Based on Information Entropy”, Journal of Jiangxi Normal University (Natural Science), 2012, 36(5), 519-523.
[9] Libo Liu, Tingting Zhao, Yancang Li and Bin Wang, ”An Improved Whale Algorithm Based on Information Entry”, Mathematics in practice and theory, 2020, 50(2), 211-219.
[10] Juan Yan, Minfang Peng, et al., ”Fault Diagnosis of Grounding Grids Based on Information Entropy and Evidence Fusion”, Proceedings of the CSU-EPSA, 2017, 29(12),8-13.
[11] Ershadi, Mohammad Mahdi and Seifi, Abbas, ”An efficient Bayesian network for differential diagnosis using experts’ knowledge”, International Journal of Intelligent Computing and Cybernetics, 2020, 13(1), 103-126.
[12] Guan Li, Zhifeng Liu, Ligang Cai and Jun Yan, ”Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory”, Sensors, 2020, 20(4), 1- 17.
[13] Xiaofei He, Xiaoyang Tong and Shu Zhou, ”Power system fault diagnosis based on Bayesian network and fault section location”, Power system protection and control, 2010, 38(12), 29-34.
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Authors and Affiliations

Xin Zeng
1 2
Xingzhong Xiong
1 3
Zhongqiang Luo
1 3

  1. School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China
  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, China
  3. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan Universityof Science and Engineering, Yibin, China
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Abstract

The article describes a shock safety modeling method for low-voltage electric devices, based on using a Bayesian network. This method allows for taking into account all possible combinations of the reliability and unreliability states for the shock protection elements under concern. The developed method allows for investigating electric shock incidents, analysing and assessing shock risks, as well as for determining criteria of dimensioning shock protection means, also with respect to reliability of the particular shock protection elements. Dependencies for determining and analysing the probability of appearance of reliability states of protection as well as an electric shock risk are presented in the article.
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Authors and Affiliations

Włodzimierz Korniluk
Dariusz Sajewicz
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Abstract

The article presents a shock safety model of an indirect contact with a low-voltage electric device. This model was used for computations and analyses concerning the following: the probabilities of appearance of the particular shock protection unreliability states, electric shock states (ventricular fibrillation), contributions of the unreliability of different shock protection elements to the probability of occurrence of these states, as well as the risk of electric shock (and the shock safety), and contributions of the intensity of occurrence of damages to different shock protection elements to this risk. An example of a possibility to reduce the risk of an electric shock through changing the intensity of occurrence of damages to the selected protection elements was provided.

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Authors and Affiliations

Włodzimierz Korniluk
Dariusz Sajewicz

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