This paper is concerned with the convergence of a sequence of discrete-time Markov decision processes(DTMDPs)with constraints,state-action dependent discount factors,and possibly unbounded costs.Using the convex analy...This paper is concerned with the convergence of a sequence of discrete-time Markov decision processes(DTMDPs)with constraints,state-action dependent discount factors,and possibly unbounded costs.Using the convex analytic approach under mild conditions,we prove that the optimal values and optimal policies of the original DTMDPs converge to those of the"limit"one.Furthermore,we show that any countablestate DTMDP can be approximated by a sequence of finite-state DTMDPs,which are constructed using the truncation technique.Finally,we illustrate the approximation by solving a controlled queueing system numerically,and give the corresponding error bound of the approximation.展开更多
基于Wi-Fi技术的方法以其无需穿戴、易于部署等优点日益成为动作识别领域的热门研究方向。然而在有干扰的情况下,Wi-Fi设备易受到影响从而造成识别精度的下降。据此设计一种基于信道状态信息(Channel Status Information,CSI)的高鲁棒...基于Wi-Fi技术的方法以其无需穿戴、易于部署等优点日益成为动作识别领域的热门研究方向。然而在有干扰的情况下,Wi-Fi设备易受到影响从而造成识别精度的下降。据此设计一种基于信道状态信息(Channel Status Information,CSI)的高鲁棒性动作识别方法。提出动态子载波选择算法,动态地选取与动作相关性最大的子载波;针对无线设备在干扰情况下数据采集质量不佳、分割不精确导致动作识别准确率下降的问题,提出分割辅助算法,有效提高动作区间的分割精度和分类准确性。实验结果显示,该方法在无干扰和有干扰的环境下对五种动作的识别准确度分别可达到92%和81%,具有较强的鲁棒性。展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 61374067 and 41271076)
文摘This paper is concerned with the convergence of a sequence of discrete-time Markov decision processes(DTMDPs)with constraints,state-action dependent discount factors,and possibly unbounded costs.Using the convex analytic approach under mild conditions,we prove that the optimal values and optimal policies of the original DTMDPs converge to those of the"limit"one.Furthermore,we show that any countablestate DTMDP can be approximated by a sequence of finite-state DTMDPs,which are constructed using the truncation technique.Finally,we illustrate the approximation by solving a controlled queueing system numerically,and give the corresponding error bound of the approximation.
文摘基于Wi-Fi技术的方法以其无需穿戴、易于部署等优点日益成为动作识别领域的热门研究方向。然而在有干扰的情况下,Wi-Fi设备易受到影响从而造成识别精度的下降。据此设计一种基于信道状态信息(Channel Status Information,CSI)的高鲁棒性动作识别方法。提出动态子载波选择算法,动态地选取与动作相关性最大的子载波;针对无线设备在干扰情况下数据采集质量不佳、分割不精确导致动作识别准确率下降的问题,提出分割辅助算法,有效提高动作区间的分割精度和分类准确性。实验结果显示,该方法在无干扰和有干扰的环境下对五种动作的识别准确度分别可达到92%和81%,具有较强的鲁棒性。
