Browse > Article
http://dx.doi.org/10.14248/JKOSSE.2022.18.1.090

A Comparison for the Maturity Level of Defense AI Technology to Support Situation Awareness and Decision Making  

Kwon, Hyuk Jin (Seoul National University of Science and Technology University)
Joo, Ye Na (Yonsei University)
Kim, Sung Tae (Korea Institute for Defense Analyses)
Publication Information
Journal of the Korean Society of Systems Engineering / v.18, no.1, 2022 , pp. 90-98 More about this Journal
Abstract
On February 12, 2019, the U.S. Department of Defense newly established and announced the "Defense AI Strategy" to accelerate the use of artificial intelligence (AI) technology for military purposes. As China and Russia invested heavily in AI for military purposes, the U.S. was concerned that it could eventually lose its advantage in AI technology to China and Russia. In response, China and Russia, which are hostile countries, and especially China, are speeding up the development of new military theories related to the overall construction and operation of the Chinese military based on AI. With the rapid development of AI technology, major advanced countries such as the U.S. and China are actively researching the application of AI technology, but most existing studies do not address the special topic of defense. Fortunately, the "Future Defense 2030 Technology Strategy" classified AI technology fields from a defense perspective and analyzed advanced overseas cases to present a roadmap in detail, but it has limitations in comparing private technology-oriented benchmarking and AI technology's maturity level. Therefore, this study tried to overcome the limitations of the "Future Defense 2030 Technology Strategy" by comparing and analyzing Chinese and U.S. military research cases and evaluating the maturity level of military use of AI technology, not AI technology itself.
Keywords
AI; Machine Learning; Military; Situation Awareness; Object Recognition;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 "미래국방 2030 기술전략", 국방기술진흥연구소, 2022.
2 윤정현, "국방 분야의 인공지능 활용성 제고 방안과 시사점", FUTURE HORIZON, 44-52, 2020.
3 J. Michael Dahm, "Chinese Debates on the Military Utility of Artificial Intelligence", War on the Rocks, 2020.
4 Lao, G and Jiang. S. "Risk Analysis of Third-Party Online Payment Based on PEST Model", Management and Service Science 2009 International Conference, pp. 1-5.
5 Jae Ho Yoo. Yeon Kyu Jung. "A Study on the Applications of ICT/IoT for Jeju Haenyeo Culture, an UNESCO Intangible Cultural Heritage", Journal of Information Technology Services, pp. 213-222, 2017.
6 Wigness. Maggie et al. "Efficient and Resilient Edge Intelligence for the Internet of Battlefield Things".
7 과학기술정보통신부, "과기정통부, 디지털 뉴딜핵심 '데이터 댐' 구축에 나서다", 과학기술정보통신부 보도자료, 2020, https://www.msit.go.kr/bbs/view.do?sCode=user&nttSeqNo=2937329&bbsSeqNo=94&mId=113&mPid=112.
8 국군재정관리단(2020.03.19), 20년 AI 면접체계구축 시험적용.
9 Xiaoran Shi, Feng Zhou, Shuang Yang, Zijing Zhang and Tao Su, "Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network", Remote Sens., Vol 11, No 2, 2019.
10 Ae Ri Lee. Beomsoo Kim. Jaeyoung Jang, "Risk Analysis for Protecting Personal Information in IoT Environments", Journal of Information Technology Services, pp. 41-62, 2016.
11 Jha, S. Raj. S. Fernandes, S. Jha, S.K., Jha, S., Jalaian, B., et al, "Attribution-Based Confidence Metric for Deep Neural Networks", In Advances in Neural Information Processing Systems, 2019.
12 김중명, 차승훈, 이혜진, 유제상, 최상욱, "국방무기체계 연구개발 사업에서 진화적 개발의 실효적 수행 방안에 관한 연구", 시스템엔지니어링학술지, 2021, 35-42.   DOI
13 해군본부(2021,04,19), AI 챗봇 기반 스트레스해소 및 심리치유체계 구축방안.
14 국군재정관리단(2022.04.11), 빅데이터/AI 기반 기뢰전 작전지원 체계 구축 ISP.
15 국군재정관리단(2022.05.11), 야간 개채식별 AI 학습용 데이터셋 구축.
16 Kang, Hae-Young et al. "Evaluating the Operational Capabilities and Security of the IoBT Network Architecture", Proceedings of the Korea Information Processing Society Conference, pp. 140-143, May 2021.
17 DoD, "Summary Of the 2018 Department of Defense artificial inteligence strategy", 2018.
18 Shank, G, "Qualitative Research. A Personal Skills Aproach", New Jersey: Merril Prentice Hall, 2020.
19 Hua, X. Wang, X. Wang, D. Huang, J.Hu, X, "Military Object Real-Time Detection Technology Combined with Visual Salience and Psychology", Electronics 2018, 7, 216.   DOI
20 박경진, "기술성숙도(TRL)평가방법 수립 및 적용사례", 시스템엔지니어링학술지, 2009, 43-48.