Urban rail transit(URT) has been playing an important role in urban sustainable development with its advantages of high speed,large capacity,high efficiency and low pollution.Estimating URT network scale is the key to...Urban rail transit(URT) has been playing an important role in urban sustainable development with its advantages of high speed,large capacity,high efficiency and low pollution.Estimating URT network scale is the key to ensure the scientificity and feasibility of its construction.The existing studies on rational scale of URT network have not dealt with the interaction of supply and demand.This paper describes the establishment of a system dynamics model of rational URT network scale determination,considering the interaction between URT construction and city social economic development as well as the dynamic equilibrium of capital supply and traffic demand,and the verification of the model validity by applying it to the case of Wuhan City's URT construction.展开更多
An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive ...An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.展开更多
基金Funded by Independent Innovation Grant of Huazhong University of Science & Technology (No. M2009013)
文摘Urban rail transit(URT) has been playing an important role in urban sustainable development with its advantages of high speed,large capacity,high efficiency and low pollution.Estimating URT network scale is the key to ensure the scientificity and feasibility of its construction.The existing studies on rational scale of URT network have not dealt with the interaction of supply and demand.This paper describes the establishment of a system dynamics model of rational URT network scale determination,considering the interaction between URT construction and city social economic development as well as the dynamic equilibrium of capital supply and traffic demand,and the verification of the model validity by applying it to the case of Wuhan City's URT construction.
基金Supported by the National Natural Science Foundation of China(No.51174151)the Key Scientific Research Project of Education Department of Hubei Province(No.D20151102)the Key Scientific and Technological Project of Wuhan Technology Bureau(No.2014010202010088)
文摘An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.
