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Deep neural networks have been widely applied to bearing fault diagnosis systems and achieved impressive success recently. To address the problem that the insufficient fault feature extraction ability of traditional fault diagnosis methods results in poor diagnosis effect under variable load and noise interference scenarios, a rolling bearing fault diagnosis model combining Multi-Scale Convolutional Neural Network (MSCNN) and Long Short-Term Memory (LSTM) fused with attention mechanism is proposed. To adaptively extract the essential spatial feature information of various sizes, the model creates a multi-scale feature extraction module using the convolutional neural network (CNN) learning process. The learning capacity of LSTM for time information sequence is then used to extract the vibration signal's temporal feature information. Two parallel large and small convolutional kernels teach the system spatial local features. LSTM gathers temporal global features to thoroughly and painstakingly mine the vibration signal's characteristics, thus enhancing model generalization. Lastly, bearing fault diagnosis is accomplished by using the SoftMax classifier. The experiment outcomes demonstrate that the model can derive fault properties entirely from the initial vibration signal. It can retain good diagnostic accuracy under variable load and noise interference and has strong generalization compared to other fault diagnosis models. [ABSTRACT FROM AUTHOR]

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the adaptive representation of information in various regions of the source image, thereby enhancing the contrast of the fused image. The encoder and the EEM module extract features, which are then combined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test the algorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preserves background and detail information in both infrared and visible images, yielding superior outcomes in subjective and objective evaluations. [ABSTRACT FROM AUTHOR]