Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and ...Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.展开更多
An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good contro...An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.展开更多
To deal with train delays in large high-speed railway stations,a multi-objective mixedinteger nonlinear programming(MO-MINLP)optimization model was proposed.The model used the arrival time,departure time,track occupat...To deal with train delays in large high-speed railway stations,a multi-objective mixedinteger nonlinear programming(MO-MINLP)optimization model was proposed.The model used the arrival time,departure time,track occupation,and route selection as the decision variables,and fully considered the station infrastructure layout,train operational requirements,and time standards as limiting factors.The optimization objectives were to minimize train delays and reduce track and to route adjustments.To realize the large-scale and rapid solution of the MO-MINLP model,this study proposed a rolling horizon optimization algorithm that used half an hour as a time interval and solved the rescheduling and platforming problem of each time interval step-by-step.In numerical experiments,227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution algorithm.The results show that the proposed MO-MINLP model could resolve route conflicts,compress unnecessary dwell times,and reduce train delays,and the solution algorithm could efficiently increase the computational speed.The maximum solution time for optimizing the 227 train movements is 15 min 24 s.展开更多
基金Supported by the National Natural Science Foundation of China(61701029)
文摘Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.
基金the National Natural Science Foundation of China (No. 60075012).
文摘An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.
基金supported by the National Key R&D Program of China,(No.2021YFB1600100)the Natural Science Foundation of Shanghai(Nos.21YF1450200 and 23ZR1467400)+1 种基金the National Natural Science Foundation of China(No.72101184)the Fundamental Research Funds for the Central Universities.
文摘To deal with train delays in large high-speed railway stations,a multi-objective mixedinteger nonlinear programming(MO-MINLP)optimization model was proposed.The model used the arrival time,departure time,track occupation,and route selection as the decision variables,and fully considered the station infrastructure layout,train operational requirements,and time standards as limiting factors.The optimization objectives were to minimize train delays and reduce track and to route adjustments.To realize the large-scale and rapid solution of the MO-MINLP model,this study proposed a rolling horizon optimization algorithm that used half an hour as a time interval and solved the rescheduling and platforming problem of each time interval step-by-step.In numerical experiments,227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution algorithm.The results show that the proposed MO-MINLP model could resolve route conflicts,compress unnecessary dwell times,and reduce train delays,and the solution algorithm could efficiently increase the computational speed.The maximum solution time for optimizing the 227 train movements is 15 min 24 s.
