ВИВІЛЬНЕННЯ АВТОНОМНИХ СИЛ: ІНТЕГРАЦІЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ ІЗ ШТУЧНИМ ІНТЕЛЕКТОМ У СУЧАСНУ ВІЙСЬКОВУ СТРАТЕГІЮ
Ключові слова:
БПЛА, YOLOv8, дрон, ГІС, штучний інтелект, безпека, C5IRS
Анотація
Вплив штучного інтелекту (ШІ) на міжнародну безпеку на сьогодні є безсумнівним, оскільки тепер машини здатні виконувати завдання, які традиційно покладені на людський інтелект. Ця зміна породжує безліч викликів у міжнародній безпеці, впливаючи як на звичайні військові можливості, так і на гібридні загрози. Водночас ШІ відкриває нові можливості для вирішення цих викликів, впливаючи на ключові аспекти колективної оборони, кооперативних систем безпеки та управління кризами. Враховуючи його глибокі наслідки для процвітання та безпеки, ефективне управління ШІ вимагає спільних зусиль. Обсяг перспектив і небезпек, пов’язаних зі штучним інтелектом, величезний, що вимагає колективних дій для пом’якшення ризиків безпеці та використання його потенціалу для реструктуризації операційних процесів, підтримки місій і оптимізації операцій. Ця стаття в основному зосереджена на представленні дронів, оснащених штучним інтелектом і можливостями автономного навчання, досліджуючи їх застосування у військових умовах. У статті розглядається потенціал незалежного використання безпілотних літальних апаратів із штучним інтелектом як у бойових, так і в небойових армійських операціях. Завдяки використанню ГІС, C5IRS (командування, управління, комп’ютери, зв’язок, кіберзахист, розвідка, спостереження та розвідка) і штучного інтелекту ці дрони забезпечують значну перевагу на полі бою, працюючи автономно та адаптуючись до динамічних наземних ситуацій. У бойовому просторі, де поєднання людських, інформаційних і фізичних компонентів має вирішальне значення для стратегічної переваги, оперативна сумісність стає життєво важливим фактором.Завантаження
Дані про завантаження поки що недоступні.
Посилання
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2. Bayramov, A.A., Hashimov, E.G., and Amanov, R.R. Identification of invisible objects using GIS technology. In: Proceedings of the Azerbaijan Geographical Society Geography and Natural Resources, 2016, pp. 124-126.
3. Bares, M. Interoperability Modeling of the C4ISR Systems. In: RTO SCI Symposium on “System Concepts for Integrated Air Defense of Multinational Mobile Crisis Reaction Forces”, Valencia, Spain, 2021, pp. 16-1 – 16-16.
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8. Adamski, M. Effectiveness analysis of UCAV used in modern military conflicts. Aviation, 2020, 24(2), 66-71. https://doi.org/10.3846/aviation.2020.12144
9. Žnidaršič, V., Radovanović, M. and Stevanović, D. Modeling the organisational implementation of a drone and counterdrone operator into the Serbian Armed Forces rifle section. Vojno delo, 2020, 72(3), pp. 84-109. https://doi.org/10.5937/vojdelo2003084Z
10. Petrovski, A. And Radovanović, M. (2021). Application of detection reconnaissance technologies use by drones in collaboration with C4IRS for military interested. Contemporary Macedonian Defence, 2021, 21(40), pp. 117-126.
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12. Radovanović, M., Petrovski, A., Žindrašič, V., and Ranđelović, A. Application of the fuzzy AHP -VIKOR hybrid model in the selection of an unmanned aircraft for the needs of tactical units of the armed forces. Scientific Technical Review, 2021, 71(2), 26-35. https://doi.org/10.5937/str2102026R
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16. Szulc, T. Possibilities of using unmanned combat assets in tactical operations in the mountains. Scientific Journal of the Military University of Land Forces, 2023, 208(2), pp. 112-127. https://doi.org/10.5604/01.3001.0053.7270
17. Zaher, R. A. H. Drones and their Role in the Evolution of Generations of War. The International and Political Journal, 2023, 56(Sep. 2023), pp. 69–86. https://doi.org/10.31272/ipj.i56.246
18. Hashimov, E.G., Bayramov, A.A., and Khalilov, B.M. Orthophotomap making of terrain for detection military targets. National security and military sciences, 2016, 4(2), pp. 14-20.
19. Li, S., Huang, H., Meng, X., Wang, M., Li, Y. and Xie L. A Glove-Wearing Detection Algorithm Based on Improved YOLOv8. Sensors, 2023, 23(24):9906. https://doi.org/10.3390/s23249906
20. Roy A. M., Bhaduri, J. DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and SwinTransformer prediction head-enabled YOLOv5 with attention mechanism. Advanced Engineering Informatics, 2023, 56(12), 102007, https://doi.org/10.1016/j.aei.2023.102007
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22. Wang, H., Han, X., Song, X., Su J., Li Y., Zheng W. and Wu X. Research on automatic pavement crack identification Based on improved YOLOv8. Int. J. on Int. Design and Manufacturing, 2024, 18(2), https://doi.org/10.1007/s12008-024-01769-3
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26. Niu, S., Xu, X., Liang, A., Yun, Y., Li, L., Hao, F., Bai, J. and Ma, D. Research on a Lightweight Method for Maize Seed Quality Detection Based on Improved YOLOv8. IEEE Access, 2024, 12, pp. 32927-32937, https://doi.org/10.1109/ACCESS.2024.3365559
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28. Kania, E. B. (2019). Chinese Military Innovation in the AI Revolution. The RUSI Journal, 2019, 164(5–6), pp. 26–34. https://doi.org/10.1080/03071847.2019.1693803
29. Forrest E.M, Boudreaux, B., Lohn, J.A., Ashby, M., Curriden, C., Klima, K. and Grossman, D. Military Applications of Artificial Intelligence: Ethical Concerns in an Uncertain World. Santa Monica, CA: RAND Corporation, 2020. https://www.rand.org/pubs/research_reports/RR3139-1.html
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31. Bhagat R. S. Artificial Intelligence and Data Applications In Military Opera-tions. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 2023, 3(1), pp. 264-271, https://doi.org/10.48175/IJARSCT-12040
32. De Castro, B.A., Pochmann, P.G.C. and Neves, E.B. (2024). Artificial Intelligence Applica-tions in Military Logistics Operations. In: Developments and Advances in Defense and Security. MICRADS 2023. Smart Innovation, Systems and Technologies, 2024, 380, pp. 89-100 https://doi.org/10.1007/978-981-99-8894-5_8
33. Lee, M., Choi, M., Yang, T., Kim, J., Kim, J., Kwon, O. and Cho, N. A Study on the Advancement of Intelligent Military Drones: Focusing on Reconnaissance Operations. IEEE Access, 2024, 12, pp. 55964-55975, https://doi.org/10.1109/ACCESS.2024.3390035
34. Shah, I. A., Jhanjhi, N. Z. and Brohi, S. N. Use of AI-Based Drones in Smart Cities. In: Cybersecurity Issues and Challenges in the Drone Industry, IGI Global, 2024, pp. 362-380, https://doi.org/10.4018/979-8-3693-0774-8.ch015
35. POMORTSEVA О., KOBZAN, S., and SHTERNDOK Е. Use of geo-information technologies when conducting combat operations in modern conditions. Municipal Economy of Cities, 2023, 1(175), pp. 69–73. https://doi.org/10.33042/2522-1809- 2023-1-175-69-73
36. Agbeyangi, A., Odiete, J. and Olorunlomerue, A. Review on UAVs used for Aerial Surveillance. Journal of Multidisciplinary Engineering Science and Technology, 2016, 3(10), pp. 5713-5719.
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Опубліковано
2024-09-06
Як цитувати
Radovanovic Marko Вивільнення автономних сил: інтеграція безпілотних літальних апаратів із штучним інтелектом у сучасну військову стратегію / Marko Radovanovic, Aleksandar Petrovski, Aner Behlic, Mohamed Zied Chaari, Elshan Giyas Hashimov, Radoslaw Fellner, Abayomi Agbeyangi // Системи управління, навігації та зв’язку. Збірник наукових праць. – Полтава: ПНТУ, 2024. – Т. 3 (77). – С. 55-69. – doi:https://doi.org/10.26906/SUNZ.2024.3.055.
Розділ
Управління в складних системах
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