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2020 2

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Active surface wave 1

CD 1

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Mixed-source surface wave 1

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Multichannel analysis of passive surface waves 1

Passive surface wave 1

Reinforcement learning 1

Spatial autocorrelation 1

Vehicular ad-hoc networks 1

chirality 1

enantiomeric excess 1

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Diporphyrin tweezer for multichannel spectroscopic analysis of enantiomeric excess

Daniel T. Payne, Mandeep K. Chahal, Václav Březina, Whitney A. Webre, Katsuhiko Ariga, Francis D’Souza, Jan Labuta, Jonathan P. Hill

Frontiers of Chemical Science and Engineering 2020, Volume 14, Issue 1,   Pages 28-40 doi: 10.1007/s11705-019-1869-1

Abstract: The host molecules could be used as multichannel probes of by using UV-vis, circular dichroism (CD)

Keywords： porphyrin dimer     chirality     enantiomeric excess     CD     fluorescence    

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Imposing Active Sources during High-Frequency Passive Surface-Wave Measurement Article

Feng Cheng,Jianghai Xia,Chao Shen,Yue Hu,Zongbo Xu,Binbin Mi

Engineering 2018, Volume 4, Issue 5,   Pages 685-693 doi: 10.1016/j.eng.2018.08.003

Abstract: The spatial autocorrelation (SPAC) method and the multichannel analysis of passive surface waves (MAPS

Keywords： Passive surface wave     Active surface wave     High frequency     Mixed-source surface wave     Spatial autocorrelation     Multichannel    

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Cooperative channel assignment for VANETs based on multiagent reinforcement learning Research Articles

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 1047-1058 doi: 10.1631/FITEE.1900308

Abstract: (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.

Keywords： Vehicular ad-hoc networks     Reinforcement learning     Dynamic channel assignment     Multichannel    

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Title Author Date Type Operation

Diporphyrin tweezer for multichannel spectroscopic analysis of enantiomeric excess

Daniel T. Payne, Mandeep K. Chahal, Václav Březina, Whitney A. Webre, Katsuhiko Ariga, Francis D’Souza, Jan Labuta, Jonathan P. Hill

Journal Article

Imposing Active Sources during High-Frequency Passive Surface-Wave Measurement

Feng Cheng,Jianghai Xia,Chao Shen,Yue Hu,Zongbo Xu,Binbin Mi

Journal Article

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Yun-peng Wang, Kun-xian Zheng, Da-xin Tian, Xu-ting Duan, Jian-shan Zhou,ypwang@buaa.edu.cn,zhengkunxian@buaa.edu.cn,dtian@buaa.edu.cn,duanxuting@buaa.edu.cn

Journal Article