Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.
Abstract. Pupillary response has been widely accepted as a physiological index of cognitive workload. It can be reliably measured with remote eye trackers in a non-intrusive way. However, pupillometric measurement might fail to assess cognitive workload due to the variation of luminance conditions. To overcome this problem, we study the characteristics of pupillary responses at different stages of cognitive process when performing arithmetic tasks, and propose a fine-grained approach for cognitive workload measurement. Experimental results show that cognitive workload could be effectively measured even under luminance changes.
Abstract. Electroencephalography (EEG) is an important physiological index of cognitive workload. While previous research has employed high-end EEG devices, this work investigates the feasibility of measuring cognitive workload with a low-cost EEG system. In our experiment, EEG signals are recorded from subjects performing silent reading tasks under different difficulty levels. Experimental results demonstrate the effectiveness of cognitive workload evaluation even with low-cost EEG equipment.
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