Development of methods for identifying the state of various dynamic objects

Authors

DOI:

https://doi.org/10.15587/2706-5448.2023.279437

Keywords:

heterogeneous dynamic objects, complex technical systems, complex analysis, processing of various types of data

Abstract

Artificial intelligence technologies are actively used to solve both general and highly specialized tasks. In the process of assessing (identifying) the condition of complex and heterogeneous objects, there is a high degree of a priori uncertainty regarding their condition and a small amount of initial data describing them. The trends of armed conflicts of the last decades and the regularities of the development of information systems, convincingly indicate the need to change approaches to the collection of information from various sources and their analysis. There is a constant transformation of the forms of information presentation and the order of storage and access to various types of data. The problem of integrating disparate sources of information collection into a single information space is also not fully resolved.

That is why the issue of improving the efficiency of assessing the state of complex and heterogeneous dynamic objects is an important and urgent issue. The objects of research are heterogeneous dynamic objects. The subject of the research is the identification of the state of heterogeneous dynamic objects. In the research, the method of identifying the state of heterogeneous dynamic objects was developed. The novelty of the proposed method consists in:

‒ taking into account the degree of uncertainty about the state of a heterogeneous dynamic object;

‒ taking into account the degree of data noise as a result of distortion of data characterizing the state of a heterogeneous dynamic object;

‒ reducing computing costs while assessing the state of heterogeneous dynamic objects;

‒ the possibility of performing calculations with source data that are different in nature and units of measurement.

It is advisable to implement the mentioned method in specialized software, which is used to analyze the state of complex technical systems and make decisions.

Author Biographies

Oleksii Romanov, Research Institute of Military Intelligence

PhD, Senior Researcher, Head of Institute

Andrii Shyshatskyi, Taras Shevchenko Kyiv National University

PhD, Senior Researcher

Educational and Scientific Institute of Public Administration and Civil Service

Oleh Shknai, Research Institute of Military Intelligence

PhD, Leading Researcher

Research Department

Volodymyr Yashchenok, Ivan Kozhedub Kharkiv National Air Force University

PhD, Associate Professor, Head of Department

Department of Design and Durability of Aircraft and Engines

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

Lecturer

Cyclic Commission of General Education Disciplines

Sergeant Military College

Oleksandr Trotsko, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

PhD, Associate Professor

Department of Automated Control Systems

Nadiia Protas, Poltava State Agrarian University

PhD, Associate Professor

Department of Information Systems and Technologies

Hennadii Miahkykh, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Department of Automated Control Systems

Vira Velychko, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Department of Automated Control Systems

Dmytro Balan, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Lecturer

Department of Automated Control Systems

References

  1. Shishatckii, A. V., Bashkirov, O. M., Kostina, O. M. (2015). Rozvitok іntegrovanikh sistem zv’iazku ta peredachі danikh dlia potreb Zbroinikh Sil. Ozbroennia ta vіiskova tekhnіka, 1 (5), 35–40.
  2. 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
  3. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. doi: https://doi.org/10.15587/1729-4061.2020.208554
  4. 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
  5. Rotshtein, A. P. (1999). Intellektualnye tekhnologii identifikatcii: nechetkie mnozhestva, geneticheskie algoritmy, neironnye seti. Vinnitca: UNIVERSUM, 320.
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Gorelova, G. V. (2013). Kognitivnyi podkhod k imitatcionnomu modelirovaniiu slozhnykh sistem. Izvestiia IuFU. Tekhnicheskie nauki, 3, 239–250.
Development of methods for identifying the state of various dynamic objects

Downloads

Published

2023-05-20

How to Cite

Romanov, O., Shyshatskyi, A., Shknai, O., Yashchenok, V., Stasiuk, T., Trotsko, O., Protas, N., Miahkykh, H., Velychko, V., & Balan, D. (2023). Development of methods for identifying the state of various dynamic objects. Technology Audit and Production Reserves, 3(2(71), 10–14. https://doi.org/10.15587/2706-5448.2023.279437

Issue

Section

Systems and Control Processes