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  • Open Access

    ARTICLE

    Bearing Fault Diagnosis Based on the Markov Transition Field and SE-IShufflenetV2 Model

    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 >

  • Open Access

    ARTICLE

    Lightweight Method for Plant Disease Identification Using Deep Learning

    Jianbo Lu1,2,*, Ruxin Shi2, Jin Tong3, Wenqi Cheng4, Xiaoya Ma1,3, Xiaobin Liu2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287 - 29 April 2023

    Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into… More >

  • Open Access

    ARTICLE

    Identification of Rice Leaf Disease Using Improved ShuffleNet V2

    Yang Zhou, Chunjiao Fu, Yuting Zhai, Jian Li, Ziqi Jin, Yanlei Xu*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4501-4517, 2023, DOI:10.32604/cmc.2023.038446 - 31 March 2023

    Abstract Accurate identification of rice diseases is crucial for controlling diseases and improving rice yield. To improve the classification accuracy of rice diseases, this paper proposed a classification and identification method based on an improved ShuffleNet V2 (GE-ShuffleNet) model. Firstly, the Ghost module is used to replace the convolution in the two basic unit modules of ShuffleNet V2, and the unimportant convolution is deleted from the two basic unit modules of ShuffleNet V2. The Hardswish activation function is applied to replace the ReLU activation function to improve the identification accuracy of the model. Secondly, an effective… More >

  • Open Access

    ARTICLE

    Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1847-1861, 2023, DOI:10.32604/cmc.2023.035698 - 06 February 2023

    Abstract Deep learning has been constantly improving in recent years, and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, the detection of such defects is key to the production of high-quality… More >

  • Open Access

    ARTICLE

    Leaf Blights Detection and Classification in Large Scale Applications

    Abdul Muiz Fayyaz1, Kawther A. Al-Dhlan2, Saeed Ur Rehman1, Mudassar Raza1, Waqar Mehmood3, Muhammad Shafiq4, Jin-Ghoo Choi4,*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 507-522, 2022, DOI:10.32604/iasc.2022.016392 - 03 September 2021

    Abstract Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to… More >

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