Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training
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 th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/3/1455 |
| _version_ | 1797624715609636864 |
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| author | Yong Liu Weiwen Zhan Yuan Li Xingrui Li Jingkai Guo Xiaoling Chen |
| author_facet | Yong Liu Weiwen Zhan Yuan Li Xingrui Li Jingkai Guo Xiaoling Chen |
| author_sort | Yong Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:46:28Z |
| format | Article |
| id | doaj.art-dfdb6efc5da441aa850d35a1d9bda999 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:46:28Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dfdb6efc5da441aa850d35a1d9bda9992023-11-16T16:37:43ZengMDPI AGEnergies1996-10732023-02-01163145510.3390/en16031455Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid TrainingYong Liu0Weiwen Zhan1Yuan Li2Xingrui Li3Jingkai Guo4Xiaoling Chen5School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Physical Education, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaSchool of Art and Media, China University of Geosciences, Wuhan 430074, ChinaSmart 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.https://www.mdpi.com/1996-1073/16/3/1455power-grid trainingcloud modelspatio-temporal convolutional neural networkaction segmentation |
| spellingShingle | Yong Liu Weiwen Zhan Yuan Li Xingrui Li Jingkai Guo Xiaoling Chen Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training Energies power-grid training cloud model spatio-temporal convolutional neural network action segmentation |
| title | Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training |
| title_full | Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training |
| title_fullStr | Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training |
| title_full_unstemmed | Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training |
| title_short | Grid-Related Fine Action Segmentation Based on an STCNN-MCM Joint Algorithm during Smart Grid Training |
| title_sort | grid related fine action segmentation based on an stcnn mcm joint algorithm during smart grid training |
| topic | power-grid training cloud model spatio-temporal convolutional neural network action segmentation |
| url | https://www.mdpi.com/1996-1073/16/3/1455 |
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