KEYWORDS: Convolution, Feature extraction, Vibration, Machine learning, Deep learning, Lithium, Data modeling, Signal processing, Data centers, Convolutional neural networks
Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly available datasets, and the results demonstrate the excellent performance of the proposed method.
EEG signals are better able to show changes in human emotions. However, not all channels or spatial EEG signals are valid for emotion recognition. To capture the key effective features of EEG signals that are helpful for emotion recognition, we propose to use the channel space attention mechanism in EEG emotion recognition. We used channel attention to focus on the importance of different channel signals and spatial attention to focus on the importance of different brain regions. Experimental results show that the channel spatial attention mechanism achieves good performance and that it has significant benefits for EEG emotion recognition.
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