A situation awareness assessment method based on fuzzy cognitive maps

doi:10.23919/JSEE.2022.000108

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1108-1122.doi: 10.23919/JSEE.2022.000108

收稿日期:2021-10-19 出版日期:2022-10-27 发布日期:2022-10-27

A situation awareness assessment method based on fuzzy cognitive maps

Jun CHEN^1,^2,*(), Xudong GAO¹(), Jia RONG³(), Xiaoguang GAO¹()

¹ School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
² Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China
³ Department of Data Science and AI, Monash University, Clayton VIC3800, Australia

Received:2021-10-19 Online:2022-10-27 Published:2022-10-27
Contact: Jun CHEN E-mail:junchen@nwpu.edu.cn;gxdcoolest@mail.nwpu.edu.cn;jiarong@acm.org;xggao@nwpu.edu.cn
About author:|CHEN Jun was born in 1979. He received his B.S., M.S., and Ph.D. degrees in system engineering from Northwestern Polytechnical University, China in 2001, 2005, and 2009, respectively. Since 2012, he has been an associate professor with the School of Electronics and Information, Northwestern Polytechnical University, China. His current research interests include modeling and application based on fuzzy cognitive map, and intelligent decision-making for complex autonomous system. E-mail: junchen@nwpu.edu.cn||GAO Xudong was born in 1996. He received his B.S. degree in detection guidance and control technology from Northwestern Polytechnical University, China in 2018. He is currently pursuing his M.S. degree from Northwestern Polytechnical University, China. His research interests include complex system modeling, data mining, and machine learning. E-mail: gxdcoolest@mail.nwpu.edu.cn||RONG Jia was born in 1981. She received her B.S., M.S., and Ph.D. degrees from Deakin University in 2012. Her current research interest lies in joint areas of machine learning, deep learning and image processing and their applications in digital health, medical image analysis, cancer diagnosis, cardiac disease detection, pavement condition and maintenance, and tourism management.E-mail: jiarong@acm.org||GAO Xiaoguang was born in 1957. She received her B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University, China, in 1982, 1986, and 1989, respectively. She joined the School of Electronics and Information in 1989 and became a professor in 1994. Her research interests include advanced control theory and its applications in complex systems. E-mail: xggao@nwpu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61305133) and the Aeronautical Science Foundation of China grant number 2020Z023053002.

摘要/Abstract

Abstract:

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions. Thus the measurement method of the situation awareness status is an important topic to research. So far, there are lots of methods designed for the measurement of situation awareness status, but there is no model that can measure it accurately in real-time, so this work is conducted to deal with such a gap. Firstly, collect the relevant physiological data of operators while they are performing a specific mission, simultaneously, measure their status of situation awareness by using the situation awareness global assessment technique (SAGAT), which is known for accuracy but cannot be used in real-time. And then, after the preprocessing of the raw data, use the physiological data as features, the SAGAT’s results as a label to train a fuzzy cognitive map (FCM), which is an explainable and powerful intelligent model. Also, a hybrid learning algorithm of particle swarm optimization (PSO) and gradient descent is proposed for the FCM training. The final results show that the learned FCM can assess the status of situation awareness accurately in real-time, and the proposed hybrid learning algorithm has better efficiency and accuracy.

Key words: situation awareness (SA), fuzzy cognitive map (FCM), particle swarm optimization (PSO), gradient descent

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1108-1122.

Jun CHEN, Xudong GAO, Jia RONG, Xiaoguang GAO. A situation awareness assessment method based on fuzzy cognitive maps[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1108-1122.

图/表 16

参考文献 32

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Learning algorithm	EV	ME	MAE	MSE	MedAE	R²	Running time/s
Random forest	0.8960	0.2020	0.0843	0.0109	0.0710	0.8869	0.1098
PSO	1.0000	0.0039	0.0012	0.0000	0.0005	1.0000	21244.7169
Gradient descent	0.8766	0.5404	0.1258	0.0479	0.0406	0.8753	9283.7482
PSO (in hybrid algorithm)	0.9757	0.2740	0.0614	0.0099	0.0368	0.9743	6376.7343
Hybrid algorithm	0.9994	0.0350	0.0119	0.0002	0.0091	0.9994	14767.0731

Learning algorithm	EV	ME	MAE	MSE	MedAE	R²	Running time/s
Random forest	0.8775	0.2025	0.0994	0.0259	0.0768	0.8780	0.1098
PSO	0.9957	0.0056	0.0306	0.0476	0.0118	0.9696	21244.7169
Gradient descent	0.8688	0.5866	0.1449	0.0599	0.0863	0.8708	9283.7482
PSO (in hybrid algorithm)	0.9500	0.2996	0.0705	0.0250	0.0593	0.9718	6376.7343
Hybrid algorithm	0.9680	0.0695	0.0165	0.0274	0.0189	0.9800	14767.0731

A situation awareness assessment method based on fuzzy cognitive maps

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