Chaozhi Cai^*, Tiexin Xu, Jianhua Ren, Yingfang Xue

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 125-144, 2025, DOI:10.32604/sdhm.2024.052813 - 15 November 2024

Abstract A bearing fault diagnosis method based on the Markov transition field (MTF) and SEnet (SE)-IShufflenetV2 model is proposed in this paper due to the problems of complex working conditions, low fault diagnosis accuracy, and poor generalization of rolling bearing. Firstly, MTF is used to encode one-dimensional time series vibration signals and convert them into time-dependent and unique two-dimensional feature images. Then, the generated two-dimensional dataset is fed into the SE-IShufflenetV2 model for training to achieve fault feature extraction and classification. This paper selects the bearing fault datasets from Case Western Reserve University and Paderborn University… More >

Yu Yan¹, Yu Yang^1,*, Shen Fang¹, Minna Gao², Yiding Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 875-896, 2024, DOI:10.32604/cmc.2024.051685 - 18 July 2024

Abstract In the face of the effective popularity of the Internet of Things (IoT), but the frequent occurrence of cybersecurity incidents, various cybersecurity protection means have been proposed and applied. Among them, Intrusion Detection System (IDS) has been proven to be stable and efficient. However, traditional intrusion detection methods have shortcomings such as low detection accuracy and inability to effectively identify malicious attacks. To address the above problems, this paper fully considers the superiority of deep learning models in processing high-dimensional data, and reasonable data type conversion methods can extract deep features and detect classification using… More >