Yousef Ibrahim Daradkeh¹, Volodymyr Gorokhovatskyi², Iryna Tvoroshenko^2,*, Mujahed Al-Dhaifallah³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1785-1797, 2022, DOI:10.32604/cmc.2022.025499 - 24 February 2022

Abstract The results of the development of the new fast-speed method of classification images using a structural approach are presented. The method is based on the system of hierarchical features, based on the bitwise data distribution for the set of descriptors of image description. The article also proposes the use of the spatial data processing apparatus, which simplifies and accelerates the classification process. Experiments have shown that the time of calculation of the relevance for two descriptions according to their distributions is about 1000 times less than for the traditional voting procedure, for which the sets More >

Xiaolei Li^*

Energy Engineering, Vol.119, No.2, pp. 665-680, 2022, DOI:10.32604/ee.2022.019292 - 24 January 2022

Abstract Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. More >