Studied on multi-component combustible gas,methane mainly,explosion char- acteristics of high gas mine,obtained the rules of gas explosive limit that influenced by environment temperature,pressure,concentration of oxy...Studied on multi-component combustible gas,methane mainly,explosion char- acteristics of high gas mine,obtained the rules of gas explosive limit that influenced by environment temperature,pressure,concentration of oxygen,other combustible gas,coal dust,energy of fire source,and the inert gas,proposed a new method of divide gas explo- sive triangle partition,and gave new partition linear equations.The gas explosive triangle and its new partition has important directive significance in distinguishing if the fire area has a gas explosion when sealing or opening fire area,or fire extinguishing in sealed fire area,and judging if there will be a gas explosion or other trend while fire extinguishing with inert gas.展开更多
The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework t...The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.展开更多
基金the National Natural Science Fund of China(50474010)the National"Eleventh Five-year Plan"Science and Technology Support Plan of China(2006BAK03B0503)+1 种基金the Fund of Education Department Liaoning Province(05L-174)the Fund of Education Department Liaoning Province(20060389)
文摘Studied on multi-component combustible gas,methane mainly,explosion char- acteristics of high gas mine,obtained the rules of gas explosive limit that influenced by environment temperature,pressure,concentration of oxygen,other combustible gas,coal dust,energy of fire source,and the inert gas,proposed a new method of divide gas explo- sive triangle partition,and gave new partition linear equations.The gas explosive triangle and its new partition has important directive significance in distinguishing if the fire area has a gas explosion when sealing or opening fire area,or fire extinguishing in sealed fire area,and judging if there will be a gas explosion or other trend while fire extinguishing with inert gas.
基金Financial support for this work,provided by the National Natural Science Foundation of China(No.60974126)the Natural Science Foundation of Jiangsu Province(No.BK2009094)
文摘The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.
