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Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid

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成果类型:
期刊论文
作者:
He, Fangqiuzi;Liu, Yong;Zhan, Weiwen;Xu, Qingjie;Chen, Xiaoling
通讯作者:
Chen, XL
作者机构:
[He, Fangqiuzi] Wuhan Polytech Univ, Sch Art & Design, Wuhan 430023, Peoples R China.
[He, Fangqiuzi; Liu, Yong; Zhan, Weiwen; Xu, Qingjie] China Univ Geosci, Sch Mech Engn & Elect Informat, Wuhan 430074, Peoples R China.
[Chen, Xiaoling] China Univ Geosci, Sch Art & Media, Wuhan 430074, Peoples R China.
通讯机构:
[Chen, XL ] C
China Univ Geosci, Sch Art & Media, Wuhan 430074, Peoples R China.
语种:
英文
关键词:
Graph convolutional neural network;Manual operation accuracy evaluation;Virtual reality
期刊:
Energies
ISSN:
1996-1073
年:
2022
卷:
15
期:
6
页码:
2071
基金类别:
Funding: This work was supported in part by State Grid Corporation Headquarters Science and Technology Project Grant (Project No.: 5700-202217206A-1-1-ZN); by Science and Technology Project of State Grid Corporation of China: Research and demonstration application of key technology of full range calibration of metering low voltage current transformer under live condition; by the Humanities and social sciences research project of Hubei Provincial Education Department (No. 21q136).
机构署名:
本校为第一机构
院系归属:
艺术设计学院
摘要:
The standard of manual operation in smart grid, which require accurate manipulation, is high, especially in experimental, practice, and training systems based on virtual reality (VR). In the VR training system, data gloves are often used to obtain the accurate dataset of hand movements. Previous works rarely considered the multi-sensor datasets, which collected from the data gloves, to complete the action evaluation of VR training systems. In this paper, a vectorized graph convolutional deep learning model is proposed to evaluate the accuracy o...

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