Wireless sensor networks (WSNs) are important application for safety monitoring in underground coal mines, which are difficult to monitor due to natural conditions. Based on the characteristic of limited energy for WS...Wireless sensor networks (WSNs) are important application for safety monitoring in underground coal mines, which are difficult to monitor due to natural conditions. Based on the characteristic of limited energy for WSNs in confined underground area such as coal face and laneway, we presents an energy- efficient clustering routing protocol based on weight (ECRPW) to prolong the lifetime of networks. ECRPW takes into consideration the nodes' residual energy during the election process of cluster heads. The constraint of distance threshold is used to optimize cluster scheme. Furthermore, the protocol also sets up a routing tree based on cluster heads' weight. The results show that ECRPW had better perfor- mance in energy consumption, death ratio of node and network lifetime.展开更多
The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive spe...The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.展开更多
Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then p...Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.展开更多
基金supports provided by the National Natural Science Foundation of China (No.50904070)the China Postdoctoral Science Foundation (No.20100471009)+2 种基金the National High Technology Research and Development Program of China (Nos. 2008AA062200 and2007AA01Z180)the Key Project of Jiangsu (No. BG2007012)the Science Foundation of China University of Mining and Technology (No. OC080303)
文摘Wireless sensor networks (WSNs) are important application for safety monitoring in underground coal mines, which are difficult to monitor due to natural conditions. Based on the characteristic of limited energy for WSNs in confined underground area such as coal face and laneway, we presents an energy- efficient clustering routing protocol based on weight (ECRPW) to prolong the lifetime of networks. ECRPW takes into consideration the nodes' residual energy during the election process of cluster heads. The constraint of distance threshold is used to optimize cluster scheme. Furthermore, the protocol also sets up a routing tree based on cluster heads' weight. The results show that ECRPW had better perfor- mance in energy consumption, death ratio of node and network lifetime.
文摘The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long out- burst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously. Also, due to historical and economic reasons, some coal mines in China are equipped with poor safety equipment, and the staff professional capability is low. What＇s worse, artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction. Therefore, seeking a new fast and efficient coal and gas outburst prediction method is nec- essary. By using system engineering theory, combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis, a coal and gas outburst spatiotemporal forecasting system was established. This system can guide forecasting work schedule, optimize prediction technologies, carry out step-by-step prediction and eliminate hazard hier- archically. From the point of view of application, the proposed system improves the prediction efficiency and accuracy. On this basis, computational intelligence methods to construct disaster information analysis platform were used. Feed-back results pro- vide decision support to mine safety supervisors.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079046, 50909041, 50809025, and 50879024)the National Science and Technology Support Plan (Grant Nos. 2008BAB29B03and 2008BAB29B06)+6 种基金the Special Fund of State Key Laboratory of China (Grant Nos. 2009586012, 2009586912, and 2010585212)the Fundamental Research Funds for the Central Universities (Grant Nos. 2009B08514, 2010B20414, 2010B01414, and 2010B14114)the China Hydropower Engineering Consulting Group Co. Science and Technology Support Pro-ject (Grant No. CHC-KJ-2007-02)Jiangsu Province "333 High-Level Personnel Training Project" (Grant No. 2017-B08037)Graduate Innovation Program of Universities in Jiangsu Province (Grant No. CX09B_ 163Z)Dominant Discipline Construction Program Funded Projects of University in Jiangsu ProvineScience Foundation for the Excellent Youth Scholars of Ministry of Education of China (Grant No. 20070294023)
文摘Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.
