Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation effi...Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.展开更多
Particle filter is a common algorithm in video target tracking.But there are still some shortcomings,for example,particle degradation phenomenon.For solving this problem,the general solution is to introduce resampling...Particle filter is a common algorithm in video target tracking.But there are still some shortcomings,for example,particle degradation phenomenon.For solving this problem,the general solution is to introduce resampling step.At present,four kinds of resampling algorithms are widely used:multinomial resampling,residual resampling,stratified resampling and systematic resampling algorithms.In this paper,the performances of these four resampling algorithms were analyzed from realization principle,uniform distribution theory and computational complexity.Finally,through a series of video target tracking experiments,the systematic resampling algorithm had the smallest calculation load,the shortest running time and the maximum number of effective particles.So,it can be concluded that in the field of video target tracking,the systematic resampling algorithm has more advantages than other three algorithms both in the running time and the number of effective particles.展开更多
基金funded by the China Academy of Railway Sciences Corporation Limited Scientific Research Project(No:2023YJ080).
文摘Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.
基金National Natural Science Foundations of China(Nos.61272097,61305014,61401257)China Scholarship Council(No.201508310033)+5 种基金Innovation Program of Shanghai Municipal Education Commission,China(No.14ZZ156)Natural Science Foundation of Shanghai,China(No.13ZR1455200)"Chen Guang"Project Supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation,China(No.13CG60)Funding Scheme for Training Young Teachers in Shanghai Colleges,China(No.ZZGJD13006)The Connotative Construction Projects of Shanghai Local Colleges in the 12th Five-Year,China(Nos.nhky-201442,nhrc-2015-11)The Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security,China(No.AGK2015006)
文摘Particle filter is a common algorithm in video target tracking.But there are still some shortcomings,for example,particle degradation phenomenon.For solving this problem,the general solution is to introduce resampling step.At present,four kinds of resampling algorithms are widely used:multinomial resampling,residual resampling,stratified resampling and systematic resampling algorithms.In this paper,the performances of these four resampling algorithms were analyzed from realization principle,uniform distribution theory and computational complexity.Finally,through a series of video target tracking experiments,the systematic resampling algorithm had the smallest calculation load,the shortest running time and the maximum number of effective particles.So,it can be concluded that in the field of video target tracking,the systematic resampling algorithm has more advantages than other three algorithms both in the running time and the number of effective particles.
