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  • Open Access


    Classification of Images Based on a System of Hierarchical Features

    Yousef Ibrahim Daradkeh1, Volodymyr Gorokhovatskyi2, Iryna Tvoroshenko2,*, Mujahed Al-Dhaifallah3

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

    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 of descriptors are compared. The… More >

  • Open Access


    Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

    Xiaolei Li*

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

    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. To further improve the anti-noise… More >

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