High performance hydraulic supports have a high requirement in strength,toughness and welding ability of socket mate- rial.Targeting this problem,we analyzed the properties of the high strength socket material 30Cr06,...High performance hydraulic supports have a high requirement in strength,toughness and welding ability of socket mate- rial.Targeting this problem,we analyzed the properties of the high strength socket material 30Cr06,used in high performance hy- draulic supports both at home and abroad and developed a new kind of high strength cast steel 30Cr06A,by making use of an or- thogonal experiment,which provided the design conditions for its optimal composition.The result shows that the strength and toughness of the newly developed high strength cast steel 30Cr06A is much better than that of 30Cr06.Theoretical calculations,mechanical property tests and hardness distribution tests of welded joints were carried out for a study of the welding ability of the new material,which is proved to be very good.Therefore,this 30Cr06A material has been successfully used in the socket of high performance hydraulic support.展开更多
The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The m...The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.展开更多
Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under d...Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.展开更多
文摘High performance hydraulic supports have a high requirement in strength,toughness and welding ability of socket mate- rial.Targeting this problem,we analyzed the properties of the high strength socket material 30Cr06,used in high performance hy- draulic supports both at home and abroad and developed a new kind of high strength cast steel 30Cr06A,by making use of an or- thogonal experiment,which provided the design conditions for its optimal composition.The result shows that the strength and toughness of the newly developed high strength cast steel 30Cr06A is much better than that of 30Cr06.Theoretical calculations,mechanical property tests and hardness distribution tests of welded joints were carried out for a study of the welding ability of the new material,which is proved to be very good.Therefore,this 30Cr06A material has been successfully used in the socket of high performance hydraulic support.
基金Supported by the Project of National Key Laboratory Fund
文摘The method extracting the electromagnetic parameters from scattering coefficients was studied in this paper. The Support Vector Machine (SVM) method is used to solve the inverse problem of parameters extraction. The mapping relationship is set up by calculating a large number of S pa-rameters from the samples with different permittivity by using transmission line theory. The simulated data set is used as training data set for SVM. After the training, the SVM is used to predict the permittivity of material from the scattering coefficients.
基金supported by the Program for New Century Excellent Talents in University of China (Grant No. NCET-07-0903)the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Ministry of Education, China (Grant No. 2008101-1)+2 种基金the Fundamental Research Funds for the Central Universities (Grant Nos. CDJXS10101107, CDJXS10100037)the Natural Science Foundation of Chongqing, China (Grant No. CSTC2006BB5240)the Innovative Talent Training Project of the Third Stage of "211 Project", Chongqing University (Grant No. S-09109)
文摘Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.
