1 天前 - … Deep learning models have high accuracy and good … -end fault diagnosis, However,
deep learning models involve … MA) for automated convolutional neural network architecture …

2 天前 - … Therefore, deep learning-based early diagnostic tools for epilepsy therapies using
… equipment capable of measuring, monitoring, and recording EEG signals to diagnose …

3 天前 - … Traditional domain adaptation methods address the performance degradation of
deep learning models in diagnostic tasks under varying working conditions, assuming that …

3 天前 - … techniques to improve the reliability of equipment operations, minimize … convolutional
neural networks (CNNs) are a prominent representation of deep learning in fault diagnosis…

4 天前 - … Objective evaluation lameness requires special equipment and experts. The impact
of … propose a convolutional neural network to diagnose lameness based on acoustic gaits. …

4 天前 - … -domain fault diagnosis of bearings. In sum, current transfer learning methods can
effectively mitigate the influence of domain shift by constructing deeper neural networks to …

4 天前 - … equipment autonomous monitoring and fault diagnosis detection system based on
deep learning … Wei, Y proposed a hybrid control strategy based on neural networks (NN) and …

5 天前 - … by the ECG/ EKG machines. Thus, reducing the … diagnostic ECG database [16].
The number of samples in both collections is large enough for training a deep neural network. …

5 天前 - … stacked sparse autoencoders and deep layered extreme learning machines,
achieving robust … 2) A novel residual convolutional neural network integrated with an attention …

5 天前 - … vision and deep learning, intelligent fault detection in … fault detection and
diagnosis of substation equipment can … for the radial basis function neural network nodes and …