I.P. Kameneva, V.O. Artemchuk, A.V. Іatsyshyn, А.A. Vladimirsky

Èlektron. model. 2024, 46(1):03-20

https://doi.org/10.15407/emodel.46.01.003

ABSTRACT

In order to systematize and integrate the acquired experience necessary for decision-making in conditions of war and man-made danger, as well as for the purpose of controlling emissions of greenhouse gases or other harmful substances, knowledge presentation models have been developed that take into account both the results of the analysis of available data and probabilistic assessments of the state safety of man-made enterprises and adjacent territories. In order to improve the decision-making process, a number of probabilistic models are considered, which are based on the calculation of subjective probability estimates regarding the occurrence of dangerous events and forecasting the corresponding risks. Factors of various nature are considered during modeling: external influences, concentrations of harmful substances, greenhouse gas emissions, indicators of the state of safety of man-made productions, efficiency of equipment, accounting of violations, and other indicators. Also, the knowledge system provides for calculating the risks of dangerous events, the probability of which increases under the interaction of two or a number of hazardous factors.

On the basis of the conducted research, an algorithm for building and the structure of a probabilistic model of knowledge focused on software implementation in the decision-making support system for managing the safety of man-made enterprises that pose threats to the population and the natural environment has been developed.

KEYWORDS

knowledge presentation models, emissions monitoring, decision-making, probabilistic assessments, subjective risks.

REFERENCES

1. Anderson J.R. Cognitive psychology and its implications (7th ed.). Worth Publishers, 2008. 469 p.
2. Eysenck M., Keane M. Cognitive Psychology: A Student’s Handbook: London, 2020. 980 p.
   https://doi.org/10.4324/9781351058513
3. Luger J. Artificial intelligence: strategies and methods of solving complex problems / Luger. M.: Izd. "Williams" house, 2003. 864 p.
4. Bishop Ch. Pattern Recognition and Machine Learning: Springer, 2008. 760 p.
5. Barber D. Bayesian Reasoning and Machine Learning: Cambridge University Press, 2012. www.cambridge.org
   https://doi.org/10.1017/CBO9780511804779
6. Kahneman D., Slovyk P., Tversky A. Making decisions in uncertainty: Rules and assumptions / Trans. with English — "Humanitarian Making. Academy of Management Review 32.1 (2007): 33-
7. Betsch T. The nature of intuition and its neglect in research on judgment and decision-making. Intuition in Judgment and Decision Making. New York: Lawrence Erlbaum Associates (2008): 3-
8. Dane E, Pratt MG. Exploring Intuition and Its Role in Managerial Decision Making. Acade­my of Management Review 32.1 (2007): 33-
   https://doi.org/10.5465/amr.2007.23463682
9. Salas E., Rosen M.A., DiazGranados D. Expertise-based Intuition and Decision Making in Organizations. Journal of Management 36 (2010): 941-
   https://doi.org/10.1177/0149206309350084
10. Greenhouse gas emissions should be reduced 10 times faster — research, 2021 [Electronic resource] Access mode: https://ecoaction.org.ua/vykydy-parnykovykh-haziv-10.html
11. Kameneva I.P., Artemchuk V.O., Iatsyshyn A.V. Probabilistic modeling of expert know­ledge using psychosemantic methods // Electronic modeling, 2019, 41, No. 2, p. 81-96.
    https://doi.org/10.15407/emodel.41.02.081
12. Kameneva I.P., Artemchuk V.O. The problem of informativeness and the definition of informative structures to support decision-making in the field of environmental safety // Electronic modeling, 2022, 44, No. 3, p. 50-64.
    https://doi.org/10.15407/emodel.44.03.050
13. Nalymov V.V. Spontaneity of consciousness: Probability theory of meanings and semantic architectonics of personality / V.V. Nalymov. M.: "Prometheus", 1989. 288 p.
14. Kalishchuk S. Subjective Model of Reality: Origin of Construction //
15. Problems of modern psychology, 2020 [Electronic resource] Access mode: https://www.researchgate.net/publication/340094021
16. Kiny R. Decision-making theory / Operations research: in 2 volumes. Vol. 1. M.: Mir, 1981. Pр. 481-512.
17. Lysychenko G.V., Khmil G.A., Barbashev S.V. Methodology of environmental risk assessment: monograph. — Odesa: Astroprint, 2011. 368 p.
18. Artemchuk V.O., Kameneva I.P., Kovach V.O., Popov O.O., Yatsyshyn A.V. Mathematical and software tools for solving the problems of atmospheric air monitoring of technogenically loaded territories: monograph. K.: FOP Yamchinsky, 2018. 116 p.
19. Popov O., Iatsyshyn A., Kovach V., Artemchuk V. et al. Risk Assessment for the Population of Kyiv, Ukraine as a Result of Atmospheric Air Pollution. Journal of Health and Pollution. 2020. Vol. 10(25). 200303. 
    https://doi.org/10.5696/2156-9614-10.25.200303
20. NATIONAL PLAN for reducing emissions from large combustion plants. APPROVED by the order of the Cabinet of Ministers of Ukraine dated November 8, 2017 No. 796. https://zakon.rada.gov.ua/laws/show/796-2017-%D1%80#Text
21. RESOLUTION OF THE CABINET OF MINISTERS OF UKRAINE dated August 14, 2019 No. 827 "Some issues of state monitoring in the field of atmospheric air protection" https://zakon.rada.gov.ua/laws/show/827-2019-%D0%BF#n187
22. Information technologies of spatial inventory of greenhouse gases in the energy sector and uncertainty analysis: [monograph] / R.A. Bun, H.V. Boychuk, A.R. Bun, M.Yu. Lesiv; National Lviv University. polytechnic". L.: PP Soroka TB, 2012. 464 p.
23. WMO Greenhouse Gas Bulletin (GHG Bulletin): The State of Greenhouse Gases in the Atmosphere Global Observations through 2021 (No. 18 | 26 October 2022).

Full text: PDF

V.I. Havrysh

Èlektron. model. 2024, 46(1):21-40

https://doi.org/10.15407/emodel.46.01.021

ABSTRACT

Linear and non-linear mathematical models for the determination of the temperature field, and subsequently for the analysis of temperature regimes in isotropic spatial heat-active media subjected to internal and external local heat load, have been developed. In the case of nonlinear boundary-value problems, the Kirchhoff transformation was applied, using which the original nonlinear heat conduction equations and nonlinear boundary conditions were linearized, and as a result, linearized second-order differential equations with partial derivatives and a discontinuous right-hand side and partially linearized boundary conditions were obtained. For the final linearization of the partially linearized differential equation and boundary conditions, the approximation of the temperature according to one of the spatial coordinates on the boundary surfaces of the inclusion was performed by piecewise constant functions. To solve linear boundary-value problems, as well as obtained linearized boundary-value problems with respect to the Kirchhoff transformation, the Henkel integral transformation method was used, as a result of which analytical solutions of these problems were obtained. For a heat-sensitive environment, as an example, a linear dependence of the coefficient of thermal conductivity of the structural material of the structure on temperature, which is often used in many practical problems, was chosen. As a result, analytical relations for determining the temperature distribution in this environment were obtained. Numerical analysis of temperature behavior as a function of spatial coordinates for given values of geometric and thermophysical parameters was performed. The influence of the power of internal heat sources and environmental materials on the temperature distribution was studied. To determine the numerical values of the temperature in the given structure, as well as to analyze the heat exchange processes in the middle of these structures, caused by the internal and external heat load, software tools were developed, using which a geometric image of the temperature distribution depending on the spatial coordinates was made.

KEYWORDS

temperature field; isotropic spatial heat-active environment; thermal conductivity; convective heat exchange; local internal and external heating; heat flow; thermosensitivity.

REFERENCES

1. Haopeng, S., Kunkun, X., & Cunfa, G. (2021). Temperature, thermal flux and thermal stress distribution around an elliptic cavity with temperature-dependent material properties. International Journal of Solids and Structures, 216, 136-144.
   https://doi.org/10.1016/j.ijsolstr.2021.01.010
2. Zhang, Z., Zhou, D., Fang, H., Zhang, J., & Li, X. (2021). Analysis of layered rectangular plates under thermo-mechanical loads considering temperature-dependent material Applied Mathematical Modelling, 92, 244-260.
   https://doi.org/10.1016/j.apm.2020.10.036
3. Gong, J., Xuan, L., Ying, B., & Wang, H. (2019). Thermoelastic analysis of functionally gra­ded porous materials with temperature-dependent properties by a staggered finite volume Composite Structures, 224, 111071. 
   https://doi.org/10.1016/j.compstruct.2019.111071
4. Demirbas, M. D. (2017). Thermal stress analysis of functionally graded plates with temperature-dependent material properties using theory of elasticity. Composites Part B: Engineering, 131, 100-124. 
   https://doi.org/10.1016/j.compositesb.2017.08.005
5. Ghannad, M., & Yaghoobi, M. P. (2015). A thermoelasticity solution for thick cylinders subjected to thermo-mechanical loads under various boundary conditions. International Journal of Advanced Design Manufacturing Technology, 8 (4), 1-12.
6. Yaghoobi, M.P., & Ghannad, M. (2020). An analytical solution for heat conduction of FGM cylinders with varying thickness subjected to non-uniform heat flux using a first-order temperature theory and perturbation technique. International Communications in Heat and Mass Transfer, 116, 104684.
   https://doi.org/10.1016/j.icheatmasstransfer.2020.104684
7. Eker, M., Yarımpabuç, D., & Celebi, K. (2020). Thermal stress analysis of functionally graded solid and hollow thick-walled structures with heat generation. Engineering Computations, 38(1), 371-391.
   https://doi.org/10.1108/EC-02-2020-0120
8. Bayat, A., Moosavi, H., & Bayat, Y. (2015). Thermo-mechanical analysis of functionally graded thick spheres with linearly time-dependent temperature. Scientia Iranica, Vol. 22, issue 5, 1801-1812.
9. Evstatieva, N. (2023). Modelling the Temperature Field of Electronic Devices with the Use of Infrared Thermography. Y & Evstatiev, B. (Ред.), 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania, (1-5). 
   https://doi.org/10.1109/ATEE58038.2023.10108375
10. Haoran, L., Jiaqi, Y., & Ruzhu, W. (2023). Dynamic compact thermal models for skin temperature prediction of porta-ble electronic devices based on convolution and fitting methods. International Journal of Heat and Mass Trans-fer, 210, 124170, ISSN 0017-9310. 
    https://doi.org/10.1016/j.ijheatmasstransfer.2023.124170
11. Vincenzo Bianco, Mattia De Rosa, & Kambiz Vafai (2022). Phase-change materials for thermal manage-ment of electronic devices. Applied Thermal Engineering. 214, 118839, ISSN 1359-4311. 
    https://doi.org/10.1016/j.applthermaleng.2022.118839
12. Mathew J., & Krishnan, S. (2021). A Review on Transient Thermal Management of Electronic Devices. Journal of Electronic Packaging. 
    https://doi.org/10.1115/1.4050002
13. Kun Zhou, Haohao Ding, Michael Steenbergen, Wenjian Wang, Jun Guo, & Qiyue Liu (2021. August). Temperatute field and material response as a function of rail grinding parameters. Internation Journal of Heat and Mass Transfe. 121366. 
    https://doi.org/10.1016/j.ijheatmasstransfer.2021.121366
14. Xu Liu, Wei Peng, Zhiqiang Gong, Weien Zhou, & Wen Yao. (2022). Temperature Field Inversion of Heat-Source System via Physics-Informed Neurual Networks. Cornell University.
    https://doi.org/10.1016/j.engappai.2022.104902
15. Qian Kong, Genshan Jiang, Yuechao Liu, & Miao Yu. (2020. April). Numerical and experimental study on temperature field reconstruction based on acoustic tomography. Applied Thermal Engineering. 170, 114720.
    https://doi.org/10.1016/j.applthermaleng.2019.114720
16. Vasyl Havrysh. (2023). Mathematical models to determine temperature fields in heterogeneous elements of digital with thermal sensitivity taken into account. У & Volodymyr Kochan (Ред.), Proceedings of the 12 th IEEE International Conference on Intelligent Data Acguisition and Advanced Computing Systems: Technology and Applications, IDAACS, 2, Dortmund, Germany, September 7-9, (983-991).
    https://doi.org/10.1109/IDAACS58523.2023.10348875
17. Havrysh V.I., Kolyasa L.I., Ukhanska O.M., & Loik V.B. (2019). Determination of temperature fielde in thermally sensitive layered medium with inclusions. Naukovyi Visnyk Natsionalnoho Hirnychoho Universetety. 1, 94-100.
    https://doi.org/10.29202/nvngu/2019-1/5
18. Vasyl Havrysh, Lubov Kolyasa, & Svitlana Vozna (2021). Temperature field in a layered plate with local heating. International scientific journal “Mathematical modeling”. Vol. 5, issue 3, 90-94.

Full text: PDF

H. Zemlianko, V. Kharchenko

Èlektron. model. 2024, 46(1):41-54

https://doi.org/10.15407/emodel.46.01.041

ABSTRACT

The modern world is becoming increasingly dependent on the security of critical infrastructure facilities (CIF), which is monitored by UAVs, their fleets and multifunctional fleet systems (MFS UAVs). The UAV MFS have a complex digital infrastructure (DIS). The DIS is based on new information technologies that have certain security deficiencies and create new cyber threats, in particular, due to specific vulnerabilities that can be exploited externally. The provision of cyber security of the CIS of the MBF of UAVs has been studied thanks to the development of a sequence of analysis of cyber threats using the IMECA procedure. An overview of existing cyber security assessment methods and their limitations was conducted; developed models of the OKI monitoring system based on the UAV MBF; analyzed cyber threats to its CIS; the criticality of cyber attacks and the impact of countermeasures; formulated recommendations for ensuring cyber security and general conclusions based on research results. A method of ensuring cyber security of the CIS of the MBF UAV was created, which consists of determining its specific features as an object of cyber threats, analyzing violators, vulnerabilities, risks of critical violations and choosing countermeasures, the use of which allows you to increase the level of cyber security and reliability of the monitoring system and ensure a temporary response to cyber threats.

KEYWORDS

cyber threats, unmanned aerial vehicles, cybersecurity assessment, monitoring of critical infrastructures, UAV fleets, selection of countermeasures.

REFERENCES

1. Allianz Rise of the drones. (б. д.). Allianz.com. https://www.allianz.com/en/press/news/stud ies/160914-rise-of-the-drones.html.
2. Torianyk, V., Kharchenko, V., Zemlianko, H. (2021, March). IMECA Based Assessment of Internet of Drones Systems Cyber SecurityConsidering Radio Frequency Vulnerabilities. In IntelITSIS, pp. 460-470.
3. Niyonsaba, S., Konate, K., & Soidridine, M.M. (2023). A Survey on Cybersecurity in Unmanned Aerial Vehicles: Cyberattacks, Defense Techniques and Future Research Directions. International Journal of Computer Networks and Applications, 10(5), 688. 
   https://doi.org/10.22247/ijcna/2023/223417
4. Arshad, I., Alsamhi, S.H., Qiao, Y., Lee, B., & Ye, Y. (2023). A Novel Framework for Smart Cyber Defence: A Deep-Dive Into Deep Learning Attacks and Defences. IEEE Access, 11, 88527-
   https://doi.org/10.1109/ACCESS.2023.3306333
5. Shafik, W., Mojtaba Matinkhah, S., & Shokoor, F. (2023). Cybersecurity in Unmanned Aerial Vehicles: a Review. International Journal on Smart Sensing and Intelligent Systems, 16(1). 
   https://doi.org/10.2478/ijssis-2023-0012
6. Omolara, A.E., Alawida, M., & Abiodun, O.I. (2023). Drone cybersecurity issues, solutions, trend insights and future perspectives: a survey. Neural Computing and Applications.
   https://doi.org/10.1007/s00521-023-08857-7
7. Altaweel, A., Mukkath, H., & Kamel, I. (2023). GPS Spoofing Attacks in FANETs: A Systematic Literature Review. IEEE Access, 1. 
   https://doi.org/10.1109/ACCESS.2023.3281731
8. The "Menatir" system is an automated, richly functional girder system for monitoring an additional UAV. (б. д.). MENATIR. https://menatir.com/uk/.
9. Zemlianko, H., & Kharchenko, V. (2023). Cybersecurity risk analysis of multifunctional UAV fleet systems: a conceptual model and IMECA-based technique. Radioelectronic and Computer Systems, (4), 152-
   https://doi.org/10.32620/reks.2023.4.11
10. Halahan, S., Muzychenko, I., Kovalyov, V., & Sluisarenko, A. (2023). Feature Supplication and Classification Maritime Unmanned Surface Vessels in Ukraine Naval For­ces. Випробування та сертифікація, (1(1)), 31-
    https://doi.org/10.37701/ts.01.2023.04
11. Shao, G., Ma, Y., Malekian, R., Yan, X., & Li, Z. (2019). A Novel Cooperative Platform Design for Coupled USV-UAV Systems. IEEE Transactions on Industrial Informa­tics, 15(9), 4913-
    https://doi.org/10.1109/TII.2019.2912024
12. On the national security of Ukraine, the Law of Ukraine № 2469-VIII (2023) (Ukraine). https://zakon.rada.gov.ua/laws/show/2469-19#Text.
13. On the main principles of ensuring cyber security of Ukraine, Law of Ukraine № 2163-VIII (2022) (Ukraine). https://zakon.rada.gov.ua/laws/show/2163-19#Text.
14. Implementing a Zero Trust Architecture | NCCoE. (б. д.). NCCoE. https://www.nccoe. nist.gov/projects/implementing-zero-trust-architecture.
15. Pevnev, V., Frolov, A., Tsuranov, M., & Zemlianko, H. (2022). Ensuring the Data Integrity in Infocommunication Systems. International Journal of Computing, 228-
    https://doi.org/10.47839/ijc.21.2.2591
16. Chen, D., Shi, S., & Gu, X. (2023). Chaos detection scheme for multiple variable-frequency signals with overlapping frequencies. EURASIP Journal on Advances in Signal Processing, 2023(1).
    https://doi.org/10.1186/s13634-023-01050-x
17. Adil, M., Song, H., Mastorakis, S., Abulkasim, H., Farouk, A., & Jin, Z. (2023). UAV-Assisted IoT Applications, Cybersecurity Threats, AI-Enabled Solutions, Open Challenges with Future Research Directions. IEEE Transactions on Intelligent Vehicles, 1-21.
    https://doi.org/10.1109/TIV.2023.3309548

Full text: PDF

O.V. Lebid

Èlektron. model. 2024, 46(1):55-69

https://doi.org/10.15407/emodel.46.01.055

ABSTRACT

Cyber security, energy conservation, minimization of electricity losses, fault diagnosis, and renewable energy sources were analyzed. Specific engineering problems have been defined for each field of energy, for which the use of artificial intelligence algorithms has been analyzed. Research has shown that AI algorithms can improve the processes of energy production, distribution, storage, consumption and trading.

KEYWORDS

artificial intelligence, neural networks, energy, machine learning, cyberse­curity, electrical power generation, renewable energy, energy sector.

REFERENCES

1. National Strategy for the Development of Artificial Intelligence in Ukraine 2021-2030. (2021). Ministry of Education and Science of Ukraine, National Academy of Sciences of Ukraine, Institute of Artificial Intelligence Problems. Kyiv
2. Shevchenko А.І, Baranovskyi V., Bilokobylskyi O.V. and others (2023). Strategy for the development of artificial intelligence in Ukraine: monograph [According to general ed. A.I. Shevchenko]. Kyiv: IPSHI, 305 p.
3. Sukhоdоlіa О.M. (2022). "Shchutchnуі іntеlеkt v еnеrhеtуtsі: analіtуchna dоpоvіd." Kуіv: NІSD. 49 p
4. Rоmanuk P. (2023). "Pеrspеktуvу zastоsuvannіa іnfоrmatsііnуkh tеkhnоlоhіі v еnеrhе­tуchnіі sfеrі." Matеrіals оf thе Іntеrnatіоnal Scіеntіfіc Іntеrnеt Cоnfеrеncе. Tеrnоpіl, Ukraіnе — Przеwоrsk, Pоland, 6-7 Fеbruarу 2023, pp. 56-59
5. Stadnіk M.І., Vіdmуsh A.A., Shtuts A.A., Kоlіsnуk M.A. (2020). "Іntеlеktual’nі sуstеmу v еlеktrоеnеrhеtіtsі. Tеоrііa ta praktуka: navchal’nуі pоsіbnуk." Vіnnіtsіa: TОV "TVОRІ". 332 p.
6. Zhоu S., Hu Z., Gu W., Jіang M., Zhang X.-P. (2019). "Artіfіcіal Іntеllіgеncе Basеd Smart Еnеrgу Cоmmunіtу Managеmеnt: A Rеіnfоrcеmеnt Lеarnіng Apprоach." CSЕЕ J. Pоwеr Еnеrgу Sуst., 5, 1-
7. Іbrahіm B., Rabеlо L., Gutіеrrеz-Francо Е., Clavіjо-Burіtіca N. (2022). "Machіnе Lеarnіng fоr Shоrt-Tеrm Lоad Fоrеcastіng іn Smart Grіds." Еnеrgіеs, 15, 8079.
   https://doi.org/10.3390/en15218079
8. Xu C., Lі C., Zhоu X. (2022). "Іntеrprеtablе LSTM Basеd оn Mіxturе Attеntіоn Mеchanіsm fоr Multі-Stеp Rеsіdеntіal Lоad Fоrеcastіng." Еlеctrоnіcs, 11, 2189.
   https://doi.org/10.3390/electronics11142189
9. Saіd, D.; Еllоumі, M.; Khоukhі, L. (2022). Cуbеr-Attack оn P2P Еnеrgу Transactіоn bеtwееn Cоnnеctеd Еlеctrіc Vеhіclеs: A Falsе Data Іnjеctіоn Dеtеctіоn Basеd Machіnе Lеarnіng Mоdеl.Р.63640–63647.
   https://doi.org/10.1109/ACCESS.2022.3182689
10. Zarуba D.S., Shvеts M.У., Khоkhlоv У.V. Machіnе lеarnіng fоr еlеctrіcіtу cоnsumptіоn and gеnеratіоn fоrеcastіng. MіcrоsуstЕlеctrоnAcоust, 2019, vоl. 24, nо. 6. pp. 17-21.
    https://doi.org/10.20535/2523-4455.2019.24.6.186996

Full text: PDF