Purpose： The purpose of this study was to develop and validate a method that would facilitate immediate feedback on linear hammer speed during training. Methods： Three-dimensional hammer head positional data were me...Purpose： The purpose of this study was to develop and validate a method that would facilitate immediate feedback on linear hammer speed during training. Methods： Three-dimensional hammer head positional data were measured and used to calculate linear speed （calculated speed） and cable force. These data were used to develop two linear regression models （shifted and non-shifted） that would allow prediction of hammer speed from measured cable force data （predicted speed）. The accuracy of the two models was assessed by comparing the predicted and calculated speeds. Averages of the coefficient of multiple correlation （CMC） and the root mean square （RMS） of the difference between the predicted and calculated speeds for each throw of each participant were used to assess the level of accuracy of the predicted speeds. Results： Both regression models had high CMC values （0.96 and 0.97） and relatively low RMS values （1.27 m/s and 1.05 m/s） for the non-shifted and shifted models, respectively. In addition, the average percentage differences between the predicted and calculated speeds were 6.6% and 4.7% for the non-shifted and shifted models, respectively. The RMS differences between release speeds attained via the two regression models and those attained via three-dimensional positional data were also computed. The RMS differences between the predicted and calculated release speeds were 0.69 m/s and 0.46 m/s for the non-shifted and shifted models, respectively. Conclusion： This study successfully derived and validated a method that allows prediction of linear hammer speed from directly measured cable force data. Two linear regression models were developed and it was found that either model would be capable of predicting accurate speeds. However, data predicted using the shifted regression model were more accurate.展开更多
A numerical ensemble-mean approach was employed to solve a nonlinear barotropic model with chastic basic flows to analyze the nonlinear effects in the formation of the North Atlantic Oscillation （NAO）. The nonlinear...A numerical ensemble-mean approach was employed to solve a nonlinear barotropic model with chastic basic flows to analyze the nonlinear effects in the formation of the North Atlantic Oscillation （NAO）. The nonlinear response to external forcing was more similar to the NAO mode than the linear response was, indicating the importance of nonlinearity. With increasing external forcing and enhanced low-frequency anomalies, the effect of nonlinearity increased. Therefore, for strong NAO events, nonlinearity should be considered.展开更多
In this paper, we propose a new criterion, named PICa, to simultaneously select explanatory variables in the mean model and variance model in heteroscedastic linear models based on the model structure. We show that th...In this paper, we propose a new criterion, named PICa, to simultaneously select explanatory variables in the mean model and variance model in heteroscedastic linear models based on the model structure. We show that the new criterion can select the true mean model and a correct variance model with probability tending to 1 under mild conditions. Simulation studies and a real example are presented to evaluate the new criterion, and it turns out that the proposed approach performs well.展开更多
文摘Purpose： The purpose of this study was to develop and validate a method that would facilitate immediate feedback on linear hammer speed during training. Methods： Three-dimensional hammer head positional data were measured and used to calculate linear speed （calculated speed） and cable force. These data were used to develop two linear regression models （shifted and non-shifted） that would allow prediction of hammer speed from measured cable force data （predicted speed）. The accuracy of the two models was assessed by comparing the predicted and calculated speeds. Averages of the coefficient of multiple correlation （CMC） and the root mean square （RMS） of the difference between the predicted and calculated speeds for each throw of each participant were used to assess the level of accuracy of the predicted speeds. Results： Both regression models had high CMC values （0.96 and 0.97） and relatively low RMS values （1.27 m/s and 1.05 m/s） for the non-shifted and shifted models, respectively. In addition, the average percentage differences between the predicted and calculated speeds were 6.6% and 4.7% for the non-shifted and shifted models, respectively. The RMS differences between release speeds attained via the two regression models and those attained via three-dimensional positional data were also computed. The RMS differences between the predicted and calculated release speeds were 0.69 m/s and 0.46 m/s for the non-shifted and shifted models, respectively. Conclusion： This study successfully derived and validated a method that allows prediction of linear hammer speed from directly measured cable force data. Two linear regression models were developed and it was found that either model would be capable of predicting accurate speeds. However, data predicted using the shifted regression model were more accurate.
基金supported by the National Basic Research Program of China (973 program) (Grant No. 2010CB950400) the National Natural Science Foundation of China (NSFC) (Grant Nos. 41030961 and 40805022)
文摘A numerical ensemble-mean approach was employed to solve a nonlinear barotropic model with chastic basic flows to analyze the nonlinear effects in the formation of the North Atlantic Oscillation （NAO）. The nonlinear response to external forcing was more similar to the NAO mode than the linear response was, indicating the importance of nonlinearity. With increasing external forcing and enhanced low-frequency anomalies, the effect of nonlinearity increased. Therefore, for strong NAO events, nonlinearity should be considered.
基金supported by National Natural Science Foundation of China (Grant No.10971007)Beijing Natural Science Fund (Grant No. 1072003)Science Fund of Beijing Education Committee
文摘In this paper, we propose a new criterion, named PICa, to simultaneously select explanatory variables in the mean model and variance model in heteroscedastic linear models based on the model structure. We show that the new criterion can select the true mean model and a correct variance model with probability tending to 1 under mild conditions. Simulation studies and a real example are presented to evaluate the new criterion, and it turns out that the proposed approach performs well.
