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[Display omitted] • The attention mechanism implements adaptive feature non-linear transformation. • A hyperparameter adaptive activation function is developed based on tanh function. • Designed activation function avoids non-zero mean and gradient vanishing problems. • A deep adaptive network based on ResNet is proposed for bearing fault diagnosis. • Comparative experiments verify the superiority of proposed fault diagnosis model. Bearing health condition directly affects the reliability of mechanical equipment. Although deep learning (DL) algorithms have achieved great results in the field of bearing fault diagnosis, traditional activation function uses a fixed mathematical formula to achieve non-linear feature transformation, which tend to compress part of the effective fault information and reduce the performance of fault diagnosis. To address this problem, this paper proposes a slope and threshold adaptive activation function with tanh function (STAC-tanh). Establish the relationship between non-linear feature transformation and input signal by automatically adjusting the shape of activation function. Finally, the model can retain valid fault information to improve fault diagnosis performance. Then, combining STAC-tanh and Residual Networks, this paper proposes ResNet-STAC-tanh for bearing fault diagnosis. Experimenting on the two bearing datasets with added noise, the average accuracy of the network reached 90.00% and 90.77%, respectively. The effectiveness of the new method was verified through comparative experiments. [ABSTRACT FROM AUTHOR]

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN. • A layered input gradient network (LIGN) based on a dual U-Net for high-quality image denoising is proposed. • Layered input and sharpening loss greatly improve the perceptual quality of the denoised image. • Multi-scale feature extraction block can capture more semantic information. • LIGN achieves the SoTA performance compared with the latest methods on synthetic and real noise datasets. [ABSTRACT FROM AUTHOR]

Data-driven deep learning approaches have been recently developed for guided wave-based structural health monitoring. However, the difficulty in collecting and labeling valid samples often leads to a small dataset in practical damage identification, lowering the performance of the trained model. In addition, conventional deep learning models often lack a certain degree of physical interpretability. In this study, an attention-based interpretable prototypical network is proposed for small-sample damage identification using ultrasonic guided waves. The prototypical network is utilized as the framework to calculate the prototype of each category and the similarity between samples based on metric space. Afterward, the channel attention module is constructed for feature extraction, which enables the network to highlight valid information across channels and alleviate the overfitting problem. Moreover, local interpretable model-agnostic explanation (LIME) is introduced to explain the intrinsic mechanism for damage identification performed by the network in terms of critical feature contributions. To implement efficient damage identification based on small data, both numerical and experimental studies are carried out, in which the dataset contains pinhole, crack, and corrosion damage. The classification performance shows that the proposed network can serve as an effective model to overcome the shortage of limited data for damage identification, and the LIME analysis significantly enhances the interpretability of the network. [ABSTRACT FROM AUTHOR]