As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail ...As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.展开更多
Speckle tracking imaging （STI） was employed to investigate the effect of right ventricular （RV） volume and pressure overload on left ventricular （LV） rotation and twist in 35 patients with atrial septal defect ...Speckle tracking imaging （STI） was employed to investigate the effect of right ventricular （RV） volume and pressure overload on left ventricular （LV） rotation and twist in 35 patients with atrial septal defect （ASD）, 18 of which with pulmonary hypertension, and 21 healthy subjects serving as controls. The peak rotations of 6 segments at the basal and apical short-axises and the average peak rotation and interval time of the 6 segments in the opposite direction during early systolic phase were measured respectively. LV twist versus time profile was drawn and the peak twist and time to peak twist were calculated. LV ejection fraction （EF） was measured by Biplane Simpson. Compared to ASD patients without pulmonary hypertension and healthy subjects, the peak rotations of posterior, inferior and postsept walls at the basal level were lower （P〈0.05）, and the average counterclockwise peak rotation of 6 segments at the basal level during early systolic phase was higher （P〈0.05）, and the average interval time was delayed （P〈0.05）. LV peak twist was also lower （P〈0.05）, and had a significant negative correlation with pulmonary arterial systolic pressure （r=-0.57, P=0.001）. No significant differences were found in LVEF among the three groups. It was suggested that although RV volume overload due to ASD has no significant effects on LV rotation and twist, LV peak twist is lower in ASD patients with pulmonary hypertension. Thus LV twist may serve as a new indicator of the presence of pulmonary hypertension in ASD patients.展开更多
基金supported by the Doctoral Fund Project(Grant No.X22003Z).
文摘As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.
文摘Speckle tracking imaging （STI） was employed to investigate the effect of right ventricular （RV） volume and pressure overload on left ventricular （LV） rotation and twist in 35 patients with atrial septal defect （ASD）, 18 of which with pulmonary hypertension, and 21 healthy subjects serving as controls. The peak rotations of 6 segments at the basal and apical short-axises and the average peak rotation and interval time of the 6 segments in the opposite direction during early systolic phase were measured respectively. LV twist versus time profile was drawn and the peak twist and time to peak twist were calculated. LV ejection fraction （EF） was measured by Biplane Simpson. Compared to ASD patients without pulmonary hypertension and healthy subjects, the peak rotations of posterior, inferior and postsept walls at the basal level were lower （P〈0.05）, and the average counterclockwise peak rotation of 6 segments at the basal level during early systolic phase was higher （P〈0.05）, and the average interval time was delayed （P〈0.05）. LV peak twist was also lower （P〈0.05）, and had a significant negative correlation with pulmonary arterial systolic pressure （r=-0.57, P=0.001）. No significant differences were found in LVEF among the three groups. It was suggested that although RV volume overload due to ASD has no significant effects on LV rotation and twist, LV peak twist is lower in ASD patients with pulmonary hypertension. Thus LV twist may serve as a new indicator of the presence of pulmonary hypertension in ASD patients.
