【作者】 刘华； 张建华； 王娆芬； 王行愚；

【Author】 LIU Hua,ZHANG Jianhua,WANG Raofen,WANG Xingyu Department of Automation,East China University of Science and Technology,Shanghai 200237,P.R.Chian

【机构】 华东理工大学自动化系；

【摘要】 本文的主要目的是对人机系统中操作员功能状态(Operator Functional States,OFS)进行分类。在实验室环境下,用密封舱内空气管理自动化系统(automation enhanced Cabin Air Management System,aCAMS)模拟控制任务,记录操作员的生理信号和性能数据。采用模糊C均值算法对OFS进行模式分类,分别对应OFS的"好"、"一般"、"危险",并给出所属类别的隶属度。通过选择合适的输入变量,模糊C均值算法的分类精度在可接受的范围内。根据分类结果,可以调整控制策略,从而实现智能化人机交互。更多还原

【Abstract】 The primary objective of this paper is to classify the operator functional states(OFS)in the control of human-machine system.In a lab environment,11 subjects were employed to simulate a set of process control tasks in an automated cabin air management system(aCAMS),recording psychophysiological signal and performance appearance.The fuzzy C means classification algorithm is used to classify operator functional state,which is divided into three categories,corresponding to the status of "good","Medium" and "bad",and finally gives the exact classification of results,and given their respective categories the degree of membership.By selecting the appropriate input,fuzzy C means classification accuracy can be achieved within an acceptable scope.The final classification based on the results is used to adjust control strategies,achieving intelligent human-machine interaction.更多还原