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  • Open Access


    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan1, Binqiang Chen1,*, Bin Yao1, Wangpeng He2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378

    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when the cutting conditions undergo any… More >

  • Open Access


    Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

    Yuhan Liu, Yuqiao Zheng*, Zhuang Ma, Cang Wu

    Energy Engineering, Vol.120, No.5, pp. 1223-1237, 2023, DOI:10.32604/ee.2023.026329

    Abstract An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition (SCADA) normal data drive. Firstly, the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted. Then the spindle speed is employed as the output parameter, and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the pre-processed data, with the evaluation indexes selected as the optimal model. Finally, calculating the spindle operation status index according to the sliding window principle, ascertaining the threshold value for identifying the abnormal spindle… More >

  • Open Access


    Online Rail Fastener Detection Based on YOLO Network

    Jun Li1, Xinyi Qiu1, Yifei Wei1,*, Mei Song1, Xiaojun Wang2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5955-5967, 2022, DOI:10.32604/cmc.2022.027947

    Abstract Traveling by high-speed rail and railway transportation have become an important part of people’s life and social production. Track is the basic equipment of railway transportation, and its performance directly affects the service lifetime of railway lines and vehicles. The anomaly detection of rail fasteners is in a priority, while the traditional manual method is extremely inefficient and dangerous to workers. Therefore, this paper introduces efficient computer vision into the railway detection system not only to locate the normal fasteners, but also to recognize the fasteners states. To be more specific, this paper mainly studies the rail fastener detection based… More >

  • Open Access


    A Method Based on Knowledge Distillation for Fish School Stress State Recognition in Intensive Aquaculture

    Siyuan Mei1,2, Yingyi Chen1,2,*, Hanxiang Qin1,2, Huihui Yu3, Daoliang Li1,2, Boyang Sun1,2, Ling Yang1,2, Yeqi Liu1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1315-1335, 2022, DOI:10.32604/cmes.2022.019378

    Abstract Fish behavior analysis for recognizing stress is very important for fish welfare and production management in aquaculture. Recent advances have been made in fish behavior analysis based on deep learning. However, most existing methods with top performance rely on considerable memory and computational resources, which is impractical in the real-world scenario. In order to overcome the limitations of these methods, a new method based on knowledge distillation is proposed to identify the stress states of fish schools. The knowledge distillation architecture transfers additional inter-class information via a mixed relative loss function, and it forces a lightweight network (GhostNet) to mimic… More >

  • Open Access


    Affective State Recognition Using Thermal-Based Imaging: A Survey

    Mustafa M. M. Al Qudah, Ahmad S. A. Mohamed*, Syaheerah L. Lutfi

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 47-62, 2021, DOI:10.32604/csse.2021.015222

    Abstract The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and… More >

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