Analyzing point cloud of coal mining process in much dust environment based on dynamic graph convolution neural network.

Xing, Zhizhong; Zhao, Shuanfeng; Guo, Wei; Guo, Xiaojun; Wang, Shenquan; Li, Mingyue; Wang, Yuan; He, Haitao

Xing, Zhizhong; Zhao, Shuanfeng; Guo, Wei; Guo, Xiaojun; Wang, Shenquan; Li, Mingyue; Wang, Yuan; He, Haitao.

Affiliation

Xing Z; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Zhao S; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. zsf@xust.edu.cn.
Guo W; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Guo X; School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, 510641, China.
Wang S; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Li M; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
Wang Y; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.
He H; College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.

Environ Sci Pollut Res Int ; 30(2): 4044-4061, 2023 Jan.

Article de En | MEDLINE | ID: mdl-35963970

Sujet(s)
Mots clés

Coal energy; Deep learning; Dust explosion; Environmental perception; Fully mechanized mining face; Geologic model; Graph neural network; Point cloud

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Exposition professionnelle / Industrie minière charbon Limites: Humans Langue: En Journal: Environ Sci Pollut Res Int Sujet du journal: SAUDE AMBIENTAL / TOXICOLOGIA Année: 2023 Type de document: Article Pays d'affiliation: Chine Pays de publication: Allemagne

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