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Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training

Author

Listed:
  • Yong Liu

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Weiwen Zhan

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Yuan Li

    (School of Physical Education, China University of Geosciences, Wuhan 430074, China)

  • Xingrui Li

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Jingkai Guo

    (School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China)

  • Xiaoling Chen

    (School of Art and Media, China University of Geosciences, Wuhan 430074, China)

Abstract

Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significance to sustainable power-grid operation and the personal safety of operators. In this paper, a joint algorithm of a spatio-temporal convolutional neural network and multidimensional cloud model (STCNN-MCM) is proposed to complete the segmentation of fine actions during power operation. Firstly, the spatio-temporal convolutional neural network (STCNN) is used to extract action features from the multi-sensor dataset of hand actions during power operation and to predict the next moment’s action to form a multi-outcome dataset; then, a multidimensional cloud model (MCM) is designed based on the motion features of the real power operation; finally, the corresponding probabilities are obtained from the distribution of the predicted data in the cloud model through the multi-outcome dataset for action-rsegmentation point determination. The results show that STCNN-MCM can choose the segmentation points of fine actions in power operation in a relatively efficient way, improve the accuracy of action division, and can be used to improve smart grid-training systems for the segmentation of continuous fine actions in power operation.

Suggested Citation

  • Yong Liu & Weiwen Zhan & Yuan Li & Xingrui Li & Jingkai Guo & Xiaoling Chen, 2023. "Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training," Energies, MDPI, vol. 16(3), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1455-:d:1054346
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    References listed on IDEAS

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    1. Fangqiuzi He & Yong Liu & Weiwen Zhan & Qingjie Xu & Xiaoling Chen, 2022. "Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid," Energies, MDPI, vol. 15(6), pages 1-17, March.
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