Recently, the concept of topological insulators has been generalized to topological semimetals, including three-dimensional （3D） Weyl semimetals, 3D Dirac semimetMs, and 3D node-line semimetals （NLSs）. In particul...Recently, the concept of topological insulators has been generalized to topological semimetals, including three-dimensional （3D） Weyl semimetals, 3D Dirac semimetMs, and 3D node-line semimetals （NLSs）. In particular, several compounds （e.g., certain 3D graphene networks, Cu3PdN, Ca3P2 ） were discovered to be 3D NLSs, in which the conduction and valence bands cross at closed lines in the Brillouin zone. Except for the two-dimensional （2D） Dirac semimetal （e.g., graphene）, 2D topological semimetals are much less investigated. Here we propose a new concept of a 2D NLS and suggest that this state could be realized in a new mixed lattice （named as HK lattice） composed by Kagome and honeycomb lattices. It is found that A3B2 （A is a group-liB cation and B is a group-VA anion） compounds （such as Hg3As2） with the HK lattice are 2D NLSs due to the band inversion between the cation Hg-s orbital and the anion As-pz orbital with respect to the mirror symmetry. Since the band inversion occurs between two bands with the same parity, this peculiar 2D NLS could be used as transparent conductors. In the presence of buckling or spin-orbit coupling, the 2D NLS state may turn into a 2D Dirac semimetal state or a 2D topological crystalline insulating state. Since the band gap opening due to buckling or spin-orbit coupling is small, Hg3As3 with the HK lattice can still be regarded as a 2D NLS at room temperature. Our work suggests a new route to design topological materials without involving states with opposite parities.展开更多
针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双...针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。展开更多
基金Supported by the National Natural Science Foundation of China under Grant No 11374056the Special Funds for Major State Basic Research under Grant No 2015CB921700+1 种基金the Program for Professor of Special Appointment(Eastern Scholar)the Qing Nian Ba Jian Program,and the Fok Ying Tung Education Foundation
文摘Recently, the concept of topological insulators has been generalized to topological semimetals, including three-dimensional （3D） Weyl semimetals, 3D Dirac semimetMs, and 3D node-line semimetals （NLSs）. In particular, several compounds （e.g., certain 3D graphene networks, Cu3PdN, Ca3P2 ） were discovered to be 3D NLSs, in which the conduction and valence bands cross at closed lines in the Brillouin zone. Except for the two-dimensional （2D） Dirac semimetal （e.g., graphene）, 2D topological semimetals are much less investigated. Here we propose a new concept of a 2D NLS and suggest that this state could be realized in a new mixed lattice （named as HK lattice） composed by Kagome and honeycomb lattices. It is found that A3B2 （A is a group-liB cation and B is a group-VA anion） compounds （such as Hg3As2） with the HK lattice are 2D NLSs due to the band inversion between the cation Hg-s orbital and the anion As-pz orbital with respect to the mirror symmetry. Since the band inversion occurs between two bands with the same parity, this peculiar 2D NLS could be used as transparent conductors. In the presence of buckling or spin-orbit coupling, the 2D NLS state may turn into a 2D Dirac semimetal state or a 2D topological crystalline insulating state. Since the band gap opening due to buckling or spin-orbit coupling is small, Hg3As3 with the HK lattice can still be regarded as a 2D NLS at room temperature. Our work suggests a new route to design topological materials without involving states with opposite parities.
文摘针对无线和电力线通信混合组网的信道竞争接入问题,提出了一种基于深度强化学习的电力线与无线双模通信的MAC接入算法。双模节点根据网络广播信息和信道使用等数据自适应接入双媒质信道。首先建立了基于双模通信网络交互和统计信息的双模通信节点数据采集模型;接着定义了基于协作信息的深度强化学习(deep reinforcement learning,DRL)状态空间、动作空间和奖励,设计了联合α-公平效用函数和P坚持接入机制的节点决策流程,实现基于双深度Q网络(double deep Q-network,DDQN)的双模节点自适应接入算法;最后进行算法性能仿真和对比分析。仿真结果表明,提出的接入算法能够在保证双模网络和信道接入公平性的条件下,有效提高双模通信节点的接入性能。
