In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production...In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production and security conditions. The client computer can convert the analog signal about the safety in production and environmental parameters detected from the monitoring terminal into digital signal,and then,send the signal to the coal mine safety monitoring centre. This information can be analyzed,judged,and diagnosed by the monitoring-management-controlling software for helping the manager and technical workers to control the actual underground production and security situations. The system has many advantages including high reliability,better performance of real-time monitoring,faster data communicating and good practicability,and it can effectively prevent the occurrence of safety incidents in coal mines.展开更多
The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelli...The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.展开更多
Analyzed the monitor results under current systems concerning game and then pointed out that a way-out to improve chinese coal mine production safety control is to implement system innovation and punish the monitor＇s...Analyzed the monitor results under current systems concerning game and then pointed out that a way-out to improve chinese coal mine production safety control is to implement system innovation and punish the monitor＇s lazy behaviors strictly.展开更多
Advancing the application of safety and health(S&H)technologies is likely to remain a value in the mining industry.However,any information that technologies generate must be translated from the organization to the...Advancing the application of safety and health(S&H)technologies is likely to remain a value in the mining industry.However,any information that technologies generate must be translated from the organization to the workforce in a targeted way to result in sustainable change.Using a case study approach with continuous personal dust monitors(CPDMs),this paper argues for an organizational focus on technology integration.Although CPDMs provide mineworkers with near real-time feedback about their respirable coal dust exposure,they do not ensure that workers or the organization will continuously use the information to learn about and reduce exposure sources.This study used self-determination theory(SDT)to help three mines manage and communicate about information learned from the CPDM technology.Specifically,35 mineworkers participated in two mixed-method data collection efforts to discuss why they do or do not use CPDMs to engage in dust-reducing practices.Subsequently,the data was analyzed to better understand how organizations can improve the integration of technology through their management systems.Results indicate that using the CPDM to reduce sources of dust exposure is consistent with mineworkers’self-values to protect their health and not necessarily because of compliance to a manager or mine.展开更多
基金supported by Technologies R&D of State Administration of Work Safety (06-399)Technologies R&D of Hunan Province ( No.05FJ4071)
文摘In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production and security conditions. The client computer can convert the analog signal about the safety in production and environmental parameters detected from the monitoring terminal into digital signal,and then,send the signal to the coal mine safety monitoring centre. This information can be analyzed,judged,and diagnosed by the monitoring-management-controlling software for helping the manager and technical workers to control the actual underground production and security situations. The system has many advantages including high reliability,better performance of real-time monitoring,faster data communicating and good practicability,and it can effectively prevent the occurrence of safety incidents in coal mines.
基金supported in part by the National Natural Science Foundation of China(Grant No.61902311),in part by the Postdoctoral Research Foundation of China(Grant No.2019M663801)in part by the Scientific Research Project of Shaanxi Provincial Education Department(Grant No.22JK0459)+1 种基金Key R&D Foundation of Shaanxi Province(Grant No.2021SF-479)in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044 and JP21K17736.
文摘The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.
文摘Analyzed the monitor results under current systems concerning game and then pointed out that a way-out to improve chinese coal mine production safety control is to implement system innovation and punish the monitor＇s lazy behaviors strictly.
文摘Advancing the application of safety and health(S&H)technologies is likely to remain a value in the mining industry.However,any information that technologies generate must be translated from the organization to the workforce in a targeted way to result in sustainable change.Using a case study approach with continuous personal dust monitors(CPDMs),this paper argues for an organizational focus on technology integration.Although CPDMs provide mineworkers with near real-time feedback about their respirable coal dust exposure,they do not ensure that workers or the organization will continuously use the information to learn about and reduce exposure sources.This study used self-determination theory(SDT)to help three mines manage and communicate about information learned from the CPDM technology.Specifically,35 mineworkers participated in two mixed-method data collection efforts to discuss why they do or do not use CPDMs to engage in dust-reducing practices.Subsequently,the data was analyzed to better understand how organizations can improve the integration of technology through their management systems.Results indicate that using the CPDM to reduce sources of dust exposure is consistent with mineworkers’self-values to protect their health and not necessarily because of compliance to a manager or mine.
