Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network-Reference-Cited by-同舟云学术

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Research on work-stress recognition for deep ground miners based on depth-separable convolutional neural network

Published:2024-10 Issue: Volume:91 Page:105410
ISSN:0950-4230
Container-title:Journal of Loss Prevention in the Process Industries
language:en
Short-container-title:Journal of Loss Prevention in the Process Industries

Author:

Chen Ying,Liu Yuehan,Lu Caiwu,Chai Peishuo,Li Shengwei,Zou Yuan

Publisher

Elsevier BV

Reference30 articles.

1. The perceived stress reactivity scale for adolescent athletes;Britton;Pers. Indiv. Differ.,2017

2. Characteristics of direct causes and human factors in major gas explosion accidents in Chinese coal mines: case study spanning the years 1980–2010;Chen;J. Loss Prev. Process. Ind.,2013

3. Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas;Chen;Comput. Biol. Med.,2021

4. Research on the fatigue level of underground mine truck drivers with integration of cardiovascular and brain features;Chen;J. Loss Prev. Process. Ind.,2024

5. Monitoring and detecting coal miners' fatigue status using MPA-LSSVM in the vision of smart mine;Chen;Process Saf. Environ. Protect.,2023

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