Based on the 1983~2011 CMAP data,the precipitation anomaly in East Asia and its nearby sea regions（hereafter called East Asia for short） demonstrates the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterw...Based on the 1983~2011 CMAP data,the precipitation anomaly in East Asia and its nearby sea regions（hereafter called East Asia for short） demonstrates the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component.However,the NCC_CGCM forecast results are quite different,which reveal the ＂＋-＋-＂ pattern before 1999 and the ＂-＋-＋＂ pattern afterwards.Meanwhile,the probability of improving NCC_CGCM＇s forecast accuracy based on these key SST areas is discussed,and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia.The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM＇s decadal change information contained in the summer precipitation in East Asia（especially in the area of 30°N–55°N）.The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area,and the mean ACC is 0.03 and ACR is 51% for the seven key areas,which are better than NCC_CGCM＇s system error correction results（ACC is -0.01 and ACR is 49%）.Besides,the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards.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
基金supported by the National Basic Research Program of China(Grant No.2012CB955203)the National Natural Science Foundation of China(Grant Nos.41205040,41105055)the Special Scientific Research Project for Public Interest(Grant No.GYHY201306021)
文摘Based on the 1983~2011 CMAP data,the precipitation anomaly in East Asia and its nearby sea regions（hereafter called East Asia for short） demonstrates the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component.However,the NCC_CGCM forecast results are quite different,which reveal the ＂＋-＋-＂ pattern before 1999 and the ＂-＋-＋＂ pattern afterwards.Meanwhile,the probability of improving NCC_CGCM＇s forecast accuracy based on these key SST areas is discussed,and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia.The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM＇s decadal change information contained in the summer precipitation in East Asia（especially in the area of 30°N–55°N）.The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area,and the mean ACC is 0.03 and ACR is 51% for the seven key areas,which are better than NCC_CGCM＇s system error correction results（ACC is -0.01 and ACR is 49%）.Besides,the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the ＂＋-＋＂ pattern before 1999 and the ＂-＋-＂ pattern afterwards.
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.
