【作者】 刘敬擎；

【导师】 张建华；

【作者基本信息】 华东理工大学 ， 控制科学与工程， 2012， 硕士

【摘要】 复杂人机系统中,执行高危控制任务操作员的功能状态(Operator Functional State, OFS)骤降或失效往往会引发十分严重的事故。如何对操作员功能状态进行准确地预测以避免该类事故的发生己逐渐成为时下研究者致力解决的难题之一。解决该问题的一个有效途径是寻找恰当的分类器,使用操作员的电生理信号,对操作员功能状态进行有效的分类。在基于太空舱空气组分自动控制系统仿真软件(automation-enhanced Cabin Air Management System, aCAMS)的操作员功能状态实验数据的基础上,本文引入了一种基于相关性谱分析的被试个体最优电生理特征选取方法,结合其他电生理信号,作为经典的隐马尔科夫模型的输入进行分类建模。仿真结果表明隐马尔科夫模型以其优秀的时间序列信号建模能力,在OFS分类问题中有着良好的效果。最后本文对OFS,分类问题中不同参数下隐马尔科夫模型分类性能进行了对比,挖掘在OFS问题中模型参数选取的潜在规律。更多还原

【Abstract】 High risk operating task in complex human machine system is vulnerable to the operator’s break-down of functional state. To effectively estimate the OFS to avoid such serious issue is now one of the most challenging topics for researchers. One potential solution is to well classify the OFS.The experiment is based on aCAMS simulation software (automation-enhanced Cabin Air Management System). In this paper, a method of choosing the best feature for the individual subject based on correlation spectrum analysis is raised. With the new features extracted, classical Hidden Markov Model is introduced to OFS classification problem. The result shows that HMM is decent in OFS classification applications with its strong capability of modeling time serial signals. At last, the parameter choosing principals for HMM in OFS classification is researched.更多还原