A systematic approach was presented to develop the empirical model for predicting the ultimate tensile strength of AA5083-H111 aluminum alloy which is widely used in ship building industry by incorporating friction st...A systematic approach was presented to develop the empirical model for predicting the ultimate tensile strength of AA5083-H111 aluminum alloy which is widely used in ship building industry by incorporating friction stir welding(FSW) process parameters such as tool rotational speed,welding speed,and axial force.FSW was carried out considering three-factor five-level central composite rotatable design with full replications technique.Response surface methodology(RSM) was applied to developing linear regression model for establishing the relationship between the FSW process parameters and ultimate tensile strength.Analysis of variance(ANOVA) technique was used to check the adequacy of the developed model.The FSW process parameters were also optimized using response surface methodology(RSM) to maximize the ultimate tensile strength.The joint welded at a tool rotational speed of 1 000 r/min,a welding speed of 69 mm/min and an axial force of 1.33 t exhibits higher tensile strength compared with other joints.展开更多
At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming the...At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming these problems of foregone moni-toring methods,using multi-parameters which include fire image,smog,CO,CO_2,O_2,etc,the paper put forward a synthetical analysis monitor with advanced technology of neuralnetwork.The research and application of this method has significance in theory and prac-tical value for coal mine fire forecast.展开更多
The commercial FEM software ANSYS was used to analyze the failure characteristics of overburden strata under the conditions of different lengths of mining faces. It was shown that the parameters of mining faces, espec...The commercial FEM software ANSYS was used to analyze the failure characteristics of overburden strata under the conditions of different lengths of mining faces. It was shown that the parameters of mining faces, especially the length was the important factor to the failure heights and shapes of overburden strata. Fuzzy mathematics and statistical methods were used to analyze the forecasting method of the failure height of overburden strata influenced by the parameters of mining face based on the measured data under the conditions of fully-mechanized mining of general hardness cover rocks. On the basis of these analyses, a new forecasting formula was gotten. The forecasting result conforms to the in situ measured value. The result has a very important application value in safe and high-efficient mining, and has a very important advancing function to theoretical studies.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
文摘A systematic approach was presented to develop the empirical model for predicting the ultimate tensile strength of AA5083-H111 aluminum alloy which is widely used in ship building industry by incorporating friction stir welding(FSW) process parameters such as tool rotational speed,welding speed,and axial force.FSW was carried out considering three-factor five-level central composite rotatable design with full replications technique.Response surface methodology(RSM) was applied to developing linear regression model for establishing the relationship between the FSW process parameters and ultimate tensile strength.Analysis of variance(ANOVA) technique was used to check the adequacy of the developed model.The FSW process parameters were also optimized using response surface methodology(RSM) to maximize the ultimate tensile strength.The joint welded at a tool rotational speed of 1 000 r/min,a welding speed of 69 mm/min and an axial force of 1.33 t exhibits higher tensile strength compared with other joints.
基金Supported by Special Funded Project on PhD Subject for Colleges(20050290010)
文摘At present,coal mine fires were forecasted with some temperature,smog,CO,CO_2,etc,however,this method can't meet the requirements for safe production of coalmines in monitoring accuracy and validity.Overcoming these problems of foregone moni-toring methods,using multi-parameters which include fire image,smog,CO,CO_2,O_2,etc,the paper put forward a synthetical analysis monitor with advanced technology of neuralnetwork.The research and application of this method has significance in theory and prac-tical value for coal mine fire forecast.
文摘The commercial FEM software ANSYS was used to analyze the failure characteristics of overburden strata under the conditions of different lengths of mining faces. It was shown that the parameters of mining faces, especially the length was the important factor to the failure heights and shapes of overburden strata. Fuzzy mathematics and statistical methods were used to analyze the forecasting method of the failure height of overburden strata influenced by the parameters of mining face based on the measured data under the conditions of fully-mechanized mining of general hardness cover rocks. On the basis of these analyses, a new forecasting formula was gotten. The forecasting result conforms to the in situ measured value. The result has a very important application value in safe and high-efficient mining, and has a very important advancing function to theoretical studies.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
