Junpeng Wu^1,2,*, Zhenpeng Liu², Xingfan Jiang², Xinguang Tao², Ye Zhang³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 677-694, 2024, DOI:10.32604/cmc.2023.045759

Abstract The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision. Because of the reliable, safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment, this paper uses the bottleneck attention module (BAM) attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode. Firstly, the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels, thereby improving the expression ability of the feature map; secondly, the weighted sum of CrossEntropy… More >

Rao Fu^1,*, Liming Wang², Xuesong Huo², Pei Pei², Haitao Jiang³, Zhongxing Fu⁴

Energy Engineering, Vol.119, No.6, pp. 2621-2636, 2022, DOI:10.32604/ee.2022.020873

Abstract The power infrastructure of the power system is massive in size and dispersed throughout the system. Therefore, how to protect the information security in the operation and maintenance of power equipment is a difficult problem. This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance. Compared with previous blockchain transactions, the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance. Finally, the effectiveness of the proposed method is verified by experiments. More >