Review of Equipment Effectiveness Evaluation and Supporting Technologies
SU Hongjia， LUO Yucheng， LIU Fei
Software School of South China University of Technology， Guangzhou 510000， Guangdong， China
Abstract Operational effectiveness evaluation has been widely used in various weapon and equipment validation studies to assess the capability of specific operational mission objectives. This paper has provided a summarized analysis of the current problems and challenges. Then, it reviewed three categories of equipment system operational effectiveness evaluation methods: multi-attribute decision-making, complex networks, and machine learning algorithms. The research status of the above approaches was analyzed. In addition, this paper has summarized the supporting technologies for equipment system operational effectiveness evaluation, respectively, indicator reduction and optimization. Finally, according to the current research progress, expected research directions for future evaluation are proposed.
Key words ：
weapon equipment
operational effectiveness evaluation methods
multi-attribute decision-making
complex network
machine learning
index reduction
Received: 30 June 2023
Published: 27 September 2023
[1]
SUN Qianyang, ZHOU Li, DING Shifeng, LIU Renwei, DING Yi. An Artificial Neural Network-Based Method for Prediction of Ice Resistance of Polar Ships
[2]
BAO Zhujie, LI Zhen, WANG Feiliang, PANG Bo, YANG Jian. Prediction of Slip and Torsion Performance of Right-Angle Fasteners Based on Machine Learning Methods
[3]
FAN Hong, HE Jie, TIAN Shuxin. Robust Evaluation Method of Integrated Energy System Based on Variable Step Simulation and Improved Entropy Weight Method
[4]
LI Mingai1,2,3∗ (李明爱), XU Dongqin1 (许东芹). Transfer Learning in Motor Imagery Brain Computer Interface: A Review
[5]
SUN Jie, LI Zihao, ZHANG Shuyu. Application of Machine Learning in Chemical Synthesis and Characterization
[6]
GAN Ezhong, LIU Yan, WANG Hairong, WANG Chengguang. Research on Comprehensive Tradeoff of Supportability Based on Machine Learning Performance Measurement Theory
[7]
LIAO Xin, ZHU Jianhua, BIAN Fuguo, LI Keyong. Research and Practice of Improving the Reliability of Missile Weapons by "Three-Re" Method
[8]
DU Jian1∗ (杜 剑), ZHAO Xu2 (赵 旭), GUO Liming2 (郭力铭), WANG Jun2 (王 军). Machine Learning-Based Approach to
Liner Shipping Schedule Design
[9]
LIANG Liang1 (梁 良), SHI Ying1∗ (石 英), MOU Junmin2∗ (牟军敏). Submarine Multi-Model Switching Control Under
Full Working Condition Based on Machine Learning
[10]
JIA Dao, CHEN Lei, ZHU Zhipeng, YU Yao, CHI Dejian. Application of Machine Learning in Fuze- Warhead System Design
[11]
WANG Zhuoxin, ZHAO Haitao, XIE Yuehan, REN Hantao, YUAN Mingqing, ZHANG Boming, CHEN Ji’an. Prediction of Modulus of Composite Materials by BP Neural Network Optimized by Genetic Algorithm
[12]
XIA Ming (夏明), XU Tianyi (徐天意), JIANG Hong∗ (姜虹). Progress and Perspective of Artificial Intelligence and Machine Learning of Prediction in Anesthesiology
[13]
ZHOU Yi, QIN Kangping, SUN Jinwen, FAN Dongqi, ZHENG Yiming. Real-Time Risk Evaluation Method of Power System Equipment and N -m Fault Contingency Plan Generation Under Typhoon Meteorological Environment
[14]
HE Xiawei, CAI Yunze, YAN Lingling. A Combined Residual Detection Method of Reaction Wheel for Fault Detection
[15]
ZHU Song, QIAN Xiaochao, LU Yingbo, LIU Fei. An XGBoost-Based Effectiveness Prediction Method of Equipment System-of-Systems
2023, Vol. 6 
