Hong Zhang^1,*, Qi Wang¹, Lixing Chen¹, Jiaming Zhou¹, Haijian Shao²

Energy Engineering, Vol.120, No.8, pp. 1867-1883, 2023, DOI:10.32604/ee.2023.028453 - 05 June 2023

Abstract Induction motors (IMs) typically fail due to the rate of stator short-circuits. Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories, non-destructive fault detection is universally perceived as a difficult issue. This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs. Based on the publicly available thermal images More > Graphic Abstract

Pingping Yu¹, Jiayu Wang¹, Ning Cao^2,*, Heiner Dintera³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3861-3876, 2021, DOI:10.32604/cmc.2021.017527 - 06 May 2021

Abstract Along with the rapid development of biometric authentication technology, face recognition has been commercially used in many industries in recent years. However, it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. In order to solve this problem, this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images. The normal temperature distribution of the human face is stable and characteristic, and the important physiological information of the human body can be… More >