Xindai An¹, Di Wu^1,2,*, Xiangwen Xie¹, Kefeng Song¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670 - 31 August 2022

Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we 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 - 14 September 2022

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 More >