This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process.A reinforcement learning(RL)agent successfully tracks an interface between two liquids...This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process.A reinforcement learning(RL)agent successfully tracks an interface between two liquids,which is often a critical variable to track in many chemical,petrochemical,metallurgical,and oil industries.This method utilizes less than 100 images for creating an environment,from which the agent generates its own data without the need for expert knowledge.Unlike supervised learning(SL)methods that rely on a huge number of parameters,this approach requires far fewer parameters,which naturally reduces its maintenance cost.Besides its frugal nature,the agent is robust to environmental uncertainties such as occlusion,intensity changes,and excessive noise.From a closed-loop control context,an interface location-based deviation is chosen as the optimization goal during training.The methodology showcases RL for real-time object-tracking applications in the oil sands industry.Along with a presentation of the interface tracking problem,this paper provides a detailed review of one of the most effective RL methodologies:actor–critic policy.展开更多
针对现存盲直接法码辅助技术抑制直接序列码分多址系统窄带干扰性能不佳的问题,提出盲子空间法码辅助技术及其自适应算法,实现对三类窄带干扰的抑制。对于音频干扰和数字窄带干扰,提出盲自适应带收缩的投影近似子空间跟踪(PASTd,Project...针对现存盲直接法码辅助技术抑制直接序列码分多址系统窄带干扰性能不佳的问题,提出盲子空间法码辅助技术及其自适应算法,实现对三类窄带干扰的抑制。对于音频干扰和数字窄带干扰,提出盲自适应带收缩的投影近似子空间跟踪(PASTd,Projection approximation subspace tracking with deflation)算法;对于AR随机过程,由于上述盲自适应算法的低秩判定困难,提出改进的盲自适应递归最小二乘(RLS,Recursive least square)预测-PASTd码辅助算法。仿真分析试验结果表明:该算法具有优越性。展开更多
文摘This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process.A reinforcement learning(RL)agent successfully tracks an interface between two liquids,which is often a critical variable to track in many chemical,petrochemical,metallurgical,and oil industries.This method utilizes less than 100 images for creating an environment,from which the agent generates its own data without the need for expert knowledge.Unlike supervised learning(SL)methods that rely on a huge number of parameters,this approach requires far fewer parameters,which naturally reduces its maintenance cost.Besides its frugal nature,the agent is robust to environmental uncertainties such as occlusion,intensity changes,and excessive noise.From a closed-loop control context,an interface location-based deviation is chosen as the optimization goal during training.The methodology showcases RL for real-time object-tracking applications in the oil sands industry.Along with a presentation of the interface tracking problem,this paper provides a detailed review of one of the most effective RL methodologies:actor–critic policy.
文摘针对现存盲直接法码辅助技术抑制直接序列码分多址系统窄带干扰性能不佳的问题,提出盲子空间法码辅助技术及其自适应算法,实现对三类窄带干扰的抑制。对于音频干扰和数字窄带干扰,提出盲自适应带收缩的投影近似子空间跟踪(PASTd,Projection approximation subspace tracking with deflation)算法;对于AR随机过程,由于上述盲自适应算法的低秩判定困难,提出改进的盲自适应递归最小二乘(RLS,Recursive least square)预测-PASTd码辅助算法。仿真分析试验结果表明:该算法具有优越性。
