Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD based on multi-feature information fusion is proposed in this paper. Firstly, a recognition model based on quasi-Hausdorff distance is established according to the statistical characteristics of the phase-resolved partial discharge (PRPD) image, and then a modified convolutional neural network recognition model is established according to the image features of the PRPD image. Finally, Dempster–Shafer (D–S) evidence theory is used to fuse the two pattern recognition results and complement the advantages of the two approaches to improve the accuracy of partial discharge pattern recognition. The experimental results show that the total recognition accuracy rate of this method for four typical PD is more than 94.00%, and the recognition rate is significantly improved compared to support vector machine and normal convolution neural network. Maintaining stability in typical bipedal robots is challenging due to two main reasons.

This review systematically summarizes recent advances in moisture-electric generation technology from four perspectives: functional-material modification, device-structure design, multi-source energy-harvesting strategies, and practical applications. It discusses the classification and modification of moisture-responsive functional materials; analyzes how device architectures regulate ion transport, reviews the mechanisms of coupling moisture energy with solar, thermal, and mechanical energy; and summarizes representative applications of moisture-electric generators (MEGs) in power supply, self-powered sensing, and wearable electronics. The review further compares the advantages and limitations of different material and structural strategies and evaluates challenges related to output performance, environmental adaptability, long-term stability, power management, and scalable fabrication. Finally, future research directions are discussed to support the practical development of MEGs.