Psychophysiological Signal Processing And Functional State Modeling Of Process Control Operator

During the booming development of automation technique in decades, human becomes the monitor and decision maker in the automatic system. Consequently, it brings higher authority and broader ranges of responsibilities to individuals. The operator's performance degradation may be the reason of some serious disasters, particularly in safety-critical applications such as public transportation (aviation, railway, shipping, etc) and manufacturing industries (nuclear and chemical plants). In order to solve the above problems, researchers proposed adaptive automation in which the control tasks can be relocated according to the operator functional state (OFS). The assessment of OFS would help predict the operator's periods of high operational risk. And then the adaptive aiding is applied so as to avoid disasters which are caused by operator performance degradation. Therefore, the accurate assessement of OFS is the key to apply adaptive automation successfully. In the dissertation, the experimental environment of process control is designed, and the psychophysiological measurements of operators under multi-level of task loads are sampled. According to the characteristics of electroencephalogram (EEG) and electrocardiogram (ECG), the author eliminates the artifacts of EEG, extracts EEG and ECG features, and indicates the salience features significantly correlating to OFS. Subsequently, intelligent modeling and optimization methods are used to establish the corresponding OFS models. The main results in this dissertation can be summarized as follows:(1) Currently, the operator functional state is studied in the applications cases, such as the aviation, navigation, driving, etc, but rarely in the process control. An Automation-enhanced Cabin Air Management System (AutoCAMS) is used to simulate the process control environment, and the multi-level taskload experiment with single task is designed on this environment. The EEG, ECG and electrooculagram (EOG) signals of the subjects are recorded by EEG 1100. The visual analogue scale method is adopted to measure the subjective evaluations on fatigue, anxiety and effort while the task performance data are automatically recorded by AutoCAMS.(2) EEG is the most important psychophysiological signal that reflects the operator functional state. But EEG signal is easily interfered by noises and artifacts, especially by ocular artifact. An automatic ocular artifact suppression method is proposed. Independent component analysis is used to separate the original EEG signals first. Then, five features of independent components are calculated. EEG and ocular artifact components are recognized through fuzzy c-means clustering and finally clean EEG are obtained. The result shows that the method can remove the ocular artifact from EEG signal effectively and is a reliable ocular artifact suppression method.(3) The empirical mode decomposition (EMD) which is developed specially for analyzing nonlinear and nonstationary signals is employed for EEG signal analysis. The segmented EEG data are analyzed via EMD. The Welch method is applied to estimate power spectrum density of intrinsic mode functions (IMFs), whose features including peak power, peak frequency, gravity frequency, absolute power and relative power are calculated. Then the correlations between EEG features and OFS are analyzed. Finally, the salience features for each subject are obtained.(4) Wavelet packet transform is introduced to analyze the heart rate variability (HRV) of process control operator in the multi-task OFS experiment. Five features, including the energy of low frequency (LF), mid-frequency (MF), high frequency (HF), MF/HF ratio and wavelet packet entropy, are investigated. Through the study of their correlations with the operator's primary task performance, subjective measurement and task load, the HRV features that can be used to assess the operator's mental load are obtained. The results indicate that some of the five features have significant correlations with the operator functional state and can be adopted as important indices for representing OFS.(5) In order to estimate and predict OFS, adaptive network based fuzzy inference system (ANFIS) is employed to build up the operator functional state model, whose parameters are optimized by using a proposed particle swarm optimization with crossover (PSOC). In the PSOC algorithm, the original swarm implements the crossover with an introduced auxiliary population after each iteration so as to preserve the diversity of the original swarm. The operation improves the optimizing capability and avoids premature convergence. The empirical results illustrate that PSOC-ANFIS based OFS model can describe the complicated nolinear relationship between the psychophysiological variables and the functional state of operators.(6) Based on the idea of positive feedback in the foraging process of ant colony, a novel differential evolution algorithm with ant colony search (DEACS) is proposed, whose control parameters are selected adaptively. When mutation and crossover, these control parameters for each individual are determined according to the optimization performance at the last iteration. The results of typical benchmark functions show that the proposed DEACS algorithm outperforms three other algorithms and converges more rapidly. The DEACS is applied in optimizing the parameters of OFS model. It is indicated by the simulation results that DEACS can reduce the influence of the parameter settings on the model accuracy of different subjects. The OFS model can provide good representation of the mental load and is applicable of realtime online assessment of OFS.