Skeleton‐based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non‐Euclidean graphs and achieve significant performance in skeleton‐based action recognition. However, existing GCN‐based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher‐order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi‐stream adaptive spatial‐temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial‐temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi‐stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state‐of‐the‐art performance on both the NTU‐RGBD and Kinetics‐Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.

Insulated gate bipolar transistors (IGBTs) are widely used in power electronic devices, and their health prediction problems have attracted much attention in the field of power electronic equipment health management. The performance degradation of IGBT gate oxide is one of the most important failure modes. In order to analyze this failure mechanism and the ease of implementation of a monitoring circuit, the gate leakage current of IGBTs was selected as the fault precursor parameter for the degradation of their gate oxide performance, and feature selection and fusion were carried out by using time domain characteristic analysis, grayscale correlation, Mahalanobis distance, Kalman filter, and other methods. Thus, a health indicator was obtained to characterize the degradation of IGBT performance, which was used to indicate the degree of aging of the IGBT gate oxide layer. In this paper, we propose an improved degradation prediction model called MultiScaleFormer, inspired by advanced design ideas of the iTransformer network architecture, combined with the health parameters of IGBTs to construct a degradation prediction model for the IGBT gate oxide layer. MultiScaleFormer showed the highest fitting accuracy compared with the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), CNN-LSTM, and Transformer models in our experiment. The mean absolute error (MAE) of the MultiScaleFormer prediction was as low as 0.0087. Extraction of the health indicator and the construction and verification of the degradation prediction model were carried out on the dataset released by the NASA-Ames Laboratory. These results demonstrate the feasibility of the gate leakage current as a fault precursor parameter for IGBT gate oxide failure, and the feasibility and accuracy of the MultiScaleFormer prediction model for IGBT performance degradation.

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods.

The progression in integrated circuit technology has necessitated advanced solutions for the storage and rapid transmission of extensive data generated by electronic modules. Compared to traditional signal compression and transmission techniques, compressed sensing (CS) transcends the limitations of the Shannon–Nyquist sampling theorem by enabling low-frequency signal sampling, thereby becoming a extensively utilized approach in the signal processing. Recent advancements in Artificial Intelligence have further propelled the reconstruction efficacy of deep learning-based CS methods, mitigating certain constraints inherent in traditional CS approaches. Nonetheless, the existing deep learning-based CS methods are not optimally efficient for electronic data processing. In this regard, this study proposes a novel double-path multiscale adaptive compressed sensing network (DMA-CS). This network is structured around four key modules: signal compression, preprocessing, initial reconstruction, and secondary reconstruction. The signal compression module samples the signal for compression, the preprocessing module prepares the sampled signal for subsequent processing, the initial reconstruction module employs a double-path complementary network comprising a multiscale residual module fused with a multihead attention module and an inverse residual module for the initial reconstruction. Then, the secondary reconstruction module uses the adaptive dilated convolution residual module to adaptively adjust the size of the convolution kernel to ensure the high-quality reconstruction of different signals and combines it with the tree-like structure residual block for enhanced reconstruction. Our experimental evaluation on the P2020 module fault signal dataset and NASA Lithium Battery dataset demonstrates that our scheme attains the lowest percentage root-mean-square difference and the highest signal-to-noise ratio, demonstrating substantial enhancement in reconstruction performance and robustness.

Multi-view learning improves node classification with missing attributes by mining complementary information from diverse views. Existing multi-view self-supervised completion approaches, which rely on graph autoencoders to reconstruct missing node attributes from available features and graph structures in an unsupervised manner, suffer from two main limitations: they often overlook inter-layer correlations within the encoder and excessively depend on the accuracy of the input graph structure, thereby restricting node classification performance. To address these issues, this paper proposes a multi-view multi-dimensional collaborative completion method (MV-MDCC) for node classification with missing attributes. Our approach first constructs two more disturbance-resistant views by integrating feature propagation with a personalized PageRank engine. Subsequently, a triple-constraint strategy is introduced to maximize cross-view consistency across three dimensions—inter-layer, attribute, and structure—enabling more precise representation alignment. Finally, a dual-decoder strategy is designed, comprising an attribute decoder and an edge decoder, to reconstruct node features and topological structure, respectively. The reconstruction constraints facilitate learning representations with heterogeneous complementarity, thereby boosting node classification accuracy. Extensive experiments demonstrate that MV-MDCC consistently outperforms existing methods on four benchmark datasets under various attribute missing rates, confirming its effectiveness and robustness.