Burgers equation in random environment is studied. In order to give the exact solutions of random Burgers equation, we only consider the Wick-type stochastic Burgers equation which is the perturbation of the Burgers e...Burgers equation in random environment is studied. In order to give the exact solutions of random Burgers equation, we only consider the Wick-type stochastic Burgers equation which is the perturbation of the Burgers equation with variable coefficients by white noise W（t）=Bt, where Bt is a Brown motion. The auto-Baecklund transformation and stochastic soliton solutions of the Wick-type stochastic Burgers equation are shown by the homogeneous balance and Hermite transform. The generalization of the Wick-type stochastic Burgers equation is also studied.展开更多
In this paper, we propose two physical schemes for teleporting an unknown atomic state through noisy channel in cavity QED. The quantum channel is a noisy one -- a mixed GHZ state, which is more realistic in quantum i...In this paper, we propose two physical schemes for teleporting an unknown atomic state through noisy channel in cavity QED. The quantum channel is a noisy one -- a mixed GHZ state, which is more realistic in quantum information processing. We solve analytically a master equation in the Lindblad form with （L2,z, L3,z, L4,z）-type of noise in cavity Q, ED. A comparison between the two protocols are discussed.展开更多
As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimiz...As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.展开更多
文摘Burgers equation in random environment is studied. In order to give the exact solutions of random Burgers equation, we only consider the Wick-type stochastic Burgers equation which is the perturbation of the Burgers equation with variable coefficients by white noise W（t）=Bt, where Bt is a Brown motion. The auto-Baecklund transformation and stochastic soliton solutions of the Wick-type stochastic Burgers equation are shown by the homogeneous balance and Hermite transform. The generalization of the Wick-type stochastic Burgers equation is also studied.
基金Supported by National Natural Science Foundation of China under Grant Nos. 60678022 and 10704001the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20060357008+2 种基金Anhui Provincial Natural Science Foundation under Grant No. 070412060the Key Program of the Education Department of Anhui Province under Grant Nos.KJ2008A28ZC,KJ2008B265,KJ2009A048Z, 2010SQRLI53ZD, and 2008JQI183the Talent Foundation of Anhui University and Anhui Key Laboratory of Information Materials and Devices (Anhui University)
文摘In this paper, we propose two physical schemes for teleporting an unknown atomic state through noisy channel in cavity QED. The quantum channel is a noisy one -- a mixed GHZ state, which is more realistic in quantum information processing. We solve analytically a master equation in the Lindblad form with （L2,z, L3,z, L4,z）-type of noise in cavity Q, ED. A comparison between the two protocols are discussed.
基金supported in part by the US National Science Foundation Grant Nos.ECCS-1101401 and ECCS-1230040
文摘As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.
