In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is establ...In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.展开更多
Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, ...Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, and also the relation between gas distributed condition and dangerous degrees. Various prediction method, index and technique were applied to realize the data visualization; the accuracy of region prediction was increased. The system has successfully applied in Huainan minging area and Pingdingshan minging area.展开更多
In view of the deficiency of current gas monitoring systems in coal mine roadwayexcavation, a two-level information fusion technology, which adopted the adaptiveweighted algorithm and the BP neural network technology,...In view of the deficiency of current gas monitoring systems in coal mine roadwayexcavation, a two-level information fusion technology, which adopted the adaptiveweighted algorithm and the BP neural network technology, was applied to gas monitoring.The results show that the adaptive weighted algorithm can realize self-regulation by decreasingthe weight value of the failed sensor automatically, so as to eliminate the effect ofthe failed sensor and ensure the effectiveness and accuracy of the gas monitoring system.The BP neural network can not only effectively predict the gas gush quantity of the excavationroadway, but also accurately calculate the gas concentration in the region whereone or more sensors have failed, so as to provide the basis for judging the safety status ofthe roadway excavation.The experiments prove the superiority and feasibility of the applicationof information fusion in gas monitoring.展开更多
基金Project BK2001073 supported by Natural Science Foundation of Jiangsu
文摘In order to make more exact predictions of gas emissions, information fusion and chaos time series are com- bined to predict the amount of gas emission in pits. First, a multi-sensor information fusion frame is established. The frame includes a data level, a character level and a decision level. Functions at every level are interpreted in detail in this paper. Then, the process of information fusion for gas emission is introduced. On the basis of those data processed at the data and character levels, the chaos time series and neural network are combined to predict the amount of gas emission at the decision level. The weights of the neural network are gained by training not by manual setting, in order to avoid subjectivity introduced by human intervention. Finally, the experimental results were analyzed in Matlab 6.0 and prove that the method is more accurate in the prediction of the amount of gas emission than the traditional method.
基金Supported by China Postdoctoral Science Foundation(2005038319)the Science Research Plan of Educational Department of Liaoning Province(05L177)
文摘Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, and also the relation between gas distributed condition and dangerous degrees. Various prediction method, index and technique were applied to realize the data visualization; the accuracy of region prediction was increased. The system has successfully applied in Huainan minging area and Pingdingshan minging area.
基金Supported by the National Natural Science Foundation of China(50874106)the National High Technology Research and Development Program of China(2007AA06Z114)
文摘In view of the deficiency of current gas monitoring systems in coal mine roadwayexcavation, a two-level information fusion technology, which adopted the adaptiveweighted algorithm and the BP neural network technology, was applied to gas monitoring.The results show that the adaptive weighted algorithm can realize self-regulation by decreasingthe weight value of the failed sensor automatically, so as to eliminate the effect ofthe failed sensor and ensure the effectiveness and accuracy of the gas monitoring system.The BP neural network can not only effectively predict the gas gush quantity of the excavationroadway, but also accurately calculate the gas concentration in the region whereone or more sensors have failed, so as to provide the basis for judging the safety status ofthe roadway excavation.The experiments prove the superiority and feasibility of the applicationof information fusion in gas monitoring.
