The development of a method for visualizing the states of the national security system

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.285986

Keywords:

graphical and numerical display, national security, cognitive modeling, operational efficiency of decision making, hierarchical systems

Abstract

The scientific task, which is solved in the research, is the cognitive display of the state of the national security system with a complex hierarchical structure. As a rule, images are created individually taking into account a specific application field and interpreted by an expert (a group of experts) based on accumulated knowledge. Cognitive mapping is designed to support decision making by an expert (group of experts), monitoring and managing in real time. The object of research is the system of ensuring national security. The subject of the research is the functioning of the national security system. The research developed a method of visualization of the states of the national security system. An overview of the methods of visual graphic presentation of information about the state of multidimensional objects and systems was carried out.

The novelties of the proposed method are:

‒ creation of a visual, multi-level and interconnected description of the national security system;

‒ increasing the efficiency of decision making while assessing the state of the national security system;

‒ solving the problem of falling into global and local extremes while assessing the state of the national security system;

‒ combination of graphic and numerical display of controlled state parameters of the national security system;

‒ avoiding the problem of loops while visualizing the state of the national security system in real time.

The specified method should be implemented in specialized software, which is used to analyze the state of the national security system and make management decisions.

Author Biographies

Nina Kuchuk, National Technical University «Kharkiv Polytechnic Institute»

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Andrii Shyshatskyi, National Aviation University

PhD, Senior Researcher, Associate Professor

Department of Computerized Management Systems

Yurii Zhuravskyi, Koroliov Zhytomyr Military Institute

Doctor of Technical Science, Senior Researcher, Head of Department

Department of Electrical Engineering and Electronics

Tetiana Stasiuk, Sergeant Military College Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Cyclic Commission of General Education Disciplines

Oleksii Nalapko, Central Scientifically-Research Institute of Armaments and Military Equipments of the Armed Forces of Ukraine

PhD, Senior Researcher

Scientific-Research Laboratory of Automation of Scientific Researches

Peter Sliusar, Research Institute of Military Intelligence

Researcher

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Olena Shaposhnikova, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Systems

Sergii Pronin, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Computer Systems

Oksana Havryliuk, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Researcher

Scientific Center

References

  1. Shevchenko, A. I., Baranovskyi, S. V., Bilokobylskyi, O. V., Bodianskyi, Ye. V., Bomba, A. Ya. et al.; Shevchenko, A. I. (Ed.) (2023). Stratehiia rozvytku shtuchnoho intelektu v Ukraini. Kyiv: IPShI, 305.
  2. Shyshatskyi, A. V., Bashkyrov, O. M., Kostyna, O. M. (2015). Rozvytok intehrovanykh system zv’iazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viiskova tekhnika, 1 (5), 35–40.
  3. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. doi: https://doi.org/10.15587/1729-4061.2020.203301
  4. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. doi: https://doi.org/10.21303/2461-4262.2021.001940
  5. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. doi: https://doi.org/10.21303/2461-4262.2020.001353
  6. Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (5), 37–44. doi: https://doi.org/10.46338/ijetae0521_05
  7. Rotshtein, A. P. (1999). Intellektualnye tekhnologii identifikatcii: nechetkie mnozhestva, geneticheskie algoritmy, neironnye seti. Vinnitca: UNIVERSUM, 320.
  8. Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. doi: https://doi.org/10.1016/j.autcon.2018.02.025
  9. Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. doi: https://doi.org/10.1016/j.eswa.2018.11.023
  10. Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. doi: https://doi.org/10.1016/j.procs.2018.04.233
  11. Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. doi: https://doi.org/10.1016/j.dss.2019.113114
  12. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. doi: https://doi.org/10.1016/j.future.2018.06.046
  13. Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. doi: https://doi.org/10.1016/j.cirp.2019.04.001
  14. Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. doi: https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
  15. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. doi: https://doi.org/10.1016/s0020-7373(86)80040-2
  16. Gorelova, G. V. (2013). Kognitivnyi podkhod k imitatcionnomu modelirovaniiu slozhnykh sistem. Izvestiia IuFU. Tekhnicheskie nauki, 3, 239–250.
  17. Orouskhani, M., Orouskhani, Y., Mansouri, M., Teshnehlab, M. (2013). A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems. International Journal of Information Technology and Computer Science, 5 (11), 32–41. doi: https://doi.org/10.5815/ijitcs.2013.11.04
  18. Meyer, P., Roubens, M. (2006). On the use of the Choquet integral with fuzzy numbers in multiple criteria decision support. Fuzzy Sets and Systems, 157 (7), 927–938. doi: https://doi.org/10.1016/j.fss.2005.11.014
  19. Lau, N., Jamieson, G. A., Skraaning, G., Burns, C. M. (2008). Ecological Interface Design in the Nuclear Domain: An Empirical Evaluation of Ecological Displays for the Secondary Subsystems of a Boiling Water Reactor Plant Simulator. IEEE Transactions on Nuclear Science, 55 (6), 3597–3610. doi: https://doi.org/10.1109/tns.2008.2005725
The development of a method for visualizing the states of the national security system

Downloads

Published

2023-08-22

How to Cite

Kuchuk, N., Shyshatskyi, A., Zhuravskyi, Y., Stasiuk, T., Nalapko, O., Sliusar, P., Protas, N., Shaposhnikova, O., Pronin, S., & Havryliuk, O. (2023). The development of a method for visualizing the states of the national security system. Technology Audit and Production Reserves, 5(2(73), 18–21. https://doi.org/10.15587/2706-5448.2023.285986

Issue

Vol. 5 No. 2(73) (2023): Information and control systems

Section

Systems and Control Processes

License

Copyright (c) 2023 Nina Kuchuk, Andrii Shyshatskyi, Yurii Zhuravskyi, Tetiana Stasiuk, Oleksii Nalapko, Peter Sliusar, Nadiia Protas, Olena Shaposhnikova, Sergii Pronin, Oksana Havryliuk

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.