In order to enhance the stability and safety of railway driving and effectively identify the influence of the dispatcher's fatigue state on the driving organization,a method for identifying the fatigue state of the dispatcher was proposed based on the characteristics of EEG signals.The fatigue state of the dispatcher was divided according to the working time period,and the high-speed rail scheduling simulation experiment was designed to collect EEG data.The three types of brainwave frequency-domain amplitudes of high-speed rail dispatching subjects were extracted as the characteristic value by wavelet series expansion and Fourier transform,and the classification results of fatigue state were verified by combining the operation characteristics and EEG signal characteristics of dispatchers.The ResNet18+SoftMax model and MobileNet V2+SoftMax model were built through the Python language environment.The input features were converted into a three-dimensional rectangular model based on deep learning.The weights were optimized and adjusted to obtain the optimal model,so as to judge the fatigue state of high-speed rail dispatchers.The research results show that the fatigue state recognition accuracy of the participants in the high-speed rail scheduling experiment by ResNet18+SoftMax and MobileNet V2+SoftMax two models is 92.78%and 99.17%,respectively,compared with support vector machines(SVM)model to improve the awake state and fatigue state recognition accuracy,and reduce the model computing time.Among them,the MobileNet V2+SoftMax model can better identify the fatigue state of the dispatcher.With the principle of MobileNet V2+SoftMax model as the core,the potential fatigue risk of high-speed rail dispatchers under long-term working conditions can be identified more quickly and accurately.
维普
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