Applied and Computational Engineering

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Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 31 May 2023


Open Access | Article

Overview of capability requirement analysis methods for operational concept development

Jing An * 1 , Xue chao Zhang 2 , Chun lan You 3
1 Joint Logistics College of NDU, Beijing, China, 100000
2 Joint Logistics College of NDU, Beijing, China, 100000
3 Joint Logistics College of NDU, Beijing, China, 100000

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 54-61
Published 31 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Jing An, Xue chao Zhang, Chun lan You. Overview of capability requirement analysis methods for operational concept development. ACE (2023) Vol. 5: 54-61. DOI: 10.54254/2755-2721/5/20230528.

Abstract

Scientific and reasonable analysis and determination of operational capability requirements can not only optimize and improve the operational concept, but also ensure the transformation and application of the operational concept. The development and construction of the traction force and the improvement of operational capability play a key role in the transformation of operational theory to actual combat capability. It is urgent to study scientific and applicable operational capability requirements analysis methods to support the development process of the operational concept. On the basis of defining the components of operational capability requirements, this paper combs, summarizes, analyzes and compares the main operational capability requirements analysis methods, points out the problems of existing analysis methods in combination with current research, and summarizes and prospects the next research direction.

Keywords

operational concept development, Capability requirement analysis method, overview

References

1. Chen Shitao, Sun Peng, Li Daxi. Analysis of the New Concept of Operations [M]. Xi'an: Xi'an University of Electronic Science and Technology Press, October, 2019

2. Qi Xiaogang, Liu Xuexing. Emergence measurement model of weapon equipment system based on structural equation model [J]. Journal of Military Industry, 2020, 41 (2): 406-416

3. MAJ Lindsay S. Maples .Sustainment Considerations for the Multi-Domain Battle[D]:[Master’s Thesis].Fort Leaveworth,KS:U.S.Army Command and General Staff College.2018:1-3

4. Zhang Ziwei, Li Liang, Dong Zhiming, Wang Yifei, Duan Li. Research on the construction method of combat effectiveness simulation evaluation index of combat concept traction [J]. Journal of System Simulation. 2021

5. Du Guohong. Characteristics of the Development of US Military Operational Concept [J]. National Defense Science and Technology, 2020 (4)

6. Ding Wei, Geng Li. Research on Optimization of Complex System Design Method Based on System of Systems Operational Requirements [C]. Proceedings of Complex System of Systems Engineering, 2019

7. Chen Wuying, Dou Yajie, Cheng Ben, et al. Research on generation of capability requirements of weapon equipment system based on operational activity decomposition [J]. System Engineering Theory and Practice, 2011, 31 (1): 154-163

8. Yu Tonggang, Sun Zhiming, Zhang Xiaokang, et al. Research on the generation process of equipment system requirements based on joint operation capability [J]. Journal of the Academy of Ordnance Engineering, 2009, 21 (3): 10-13

9. Xu Xiufeng, Si Guangya, Wang Yanzheng. A conceptual model framework for military operations based on OPM [J]. Command Control and Simulation, 2020, 42 (6): 1673-3819

10. United States Army Redstone Technical Test Center(RTTC). "What We Do; Modeling, Simulation, and Hardware/Human-in-the-Loop Technology Integrated into Testing."([cited15August2004])Available on the World Wide Web @ http://www.rttc.army.mil/whatwedo/primary_ser/modeling.htm

11. Doshi-Velez,Finale,and BeenKim. "Towards arigorous science of interpretable machine learning." arXivpreprintarXiv:1702.08608(2017).

12. Si Guangya, Yang Jingyu, Wang Yanzheng, Hu Xiaofeng Understanding and Thinking on the Concept of War Experiment [J] Military Operations Research and Systems Engineering, 2008, 22 (3): 14-19

13. Hu Runtao, Hu Xiaofeng Design of exploratory simulation analysis framework based on data cultivation [J] Computer simulation 2009 (01): 521-526

14. Joint Interoperability Test Command. Joint combat identification evaluation team,single integrated airpicture system engineer. Millennium challenge 2002 data management and analysis plan[R/OL],2002-07-27.[2013-08-20]

15. DANIEL EGEL, RYAN ANDREW BROWN, LINDA ROBINSON, MARY KATE ADGIE,JASMIN LÉVEILLÉ, LUKE J. MATTHEWS. Leveraging Machine Learning for Operation Assessment[R]. RAND Corporation, Santa Monica, Calif. 2021.

16. Ou Wei. An Intelligent Evaluation Model for the Decision Effect of Wargame Entities Based on Deep Learning [J]. Military Operations Research and System Engineering, 2018, 32 (04): 29-34

17. Guo Shengming, He Xiaoyuan, Wu Lin, et al. Effectiveness retrospective analysis method of air defense combat system based on forced sparse self coding neural network [J] Chinese Science: Information Science, 2015, 48:824-840, doi: 10.1360/N112017-00303

18. Li Xiaoxi, Chen Haoguang, Li Daxi, et al. Research on the combat effectiveness prediction model based on Elman neural network [J]. System Simulation. 2015.27 (1): 43-49

19. C4ISR Architecture Working Group. C4ISR Architecture Framework Version2.0[R]. U.S.: Department of Defense, 1997.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230528
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated