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  • Open Access

    ARTICLE

    Fault Monitoring Strategy for PV System Based on I-V Feature Library

    Huaxing Zhao1, Yanbo Che1,*, Gang Wen2, Yijing Chen3

    Energy Engineering, Vol.121, No.3, pp. 643-660, 2024, DOI:10.32604/ee.2023.045204 - 27 February 2024

    Abstract Long-term use in challenging natural conditions is possible for photovoltaic modules, which are extremely prone to failure. Failure to diagnose and address faults in Photovoltaic (PV) power systems in a timely manner can cause permanent damage to PV modules and, in more serious cases, fires. Therefore, research into photovoltaic module defect detection techniques is crucial for the growth of the photovoltaic sector as well as for maintaining national economic prosperity and ensuring public safety. Considering the drawbacks of the current real-time and historical data-based methods for monitoring distributed PV systems, this paper proposes a method… More >

  • Open Access

    ARTICLE

    A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

    Hunmin Lee1, Inseop Na2, Kamoliddin Bultakov3, Youngchul Kim3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5413-5425, 2022, DOI:10.32604/cmc.2022.023172 - 14 January 2022

    Abstract In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as… More >

  • Open Access

    ARTICLE

    Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm

    Qian Wang1, Xiaolong Yang2,*, Di Pu3, Yingying Fan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 269-286, 2022, DOI:10.32604/cmes.2022.016437 - 29 November 2021

    Abstract This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model… More >

  • Open Access

    ARTICLE

    A Multi-Level Threshold Method for Edge Detection and Segmentation Based on Entropy

    Mohamed A. El-Sayed1, *, Abdelmgeid A. Ali2, Mohamed E. Hussien3, Hameda A. Sennary3

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 1-16, 2020, DOI:10.32604/cmc.2020.08444 - 30 March 2020

    Abstract The essential tool in image processing, computer vision and machine vision is edge detection, especially in the fields of feature extraction and feature detection. Entropy is a basic area in information theory. The entropy, in image processing field has a role associated with image settings. As an initial step in image processing, the entropy is always used the image’s segmentation to determine the regions of image which is used to separate the background and objects in image. Image segmentation known as the process which divides the image into multiple regions or sets of pixels. Many… More >

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