2篇
您的检索式：作者名="Jingye Chi"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Construction Progress of Chinese Meridian Project PhaseⅡ显示文摘The Chinese Meridian Project(CMP)is the Space Environment Ground Based Comprehensive Monitoring Network of China,a national major science and technology infrastructure project.The CMP consists of the Space Environment Monitoring System,Data Communication System,and Science Application System.Its construction has been divided into two steps:the PhaseⅠwas from 2008 to 2012;the PhaseⅡstarted at the end of 2019,expected to be completed at the end of 2023.Beyond 2023,the CMP as a whole will be in operation to make observations.This report introduces the construction progress of CMP PhaseⅡin the past two years,covering the construction progress of both the Data Communication System and the Science Application System.As for the Space Environment Monitoring System,this report mainly gives an introduction to the construction progress of large-scale advanced monitoring equipment,such as,the solar radio telescope,interplanetary scintillation telescope,incoherent scatter radar,high frequency radar,MST radar,and large-aperture Helium Lidar.In addition,this paper presents the construction plan for the next two years and the future outlook as well. | WANG Chi XU Jiyao LÜDaren YUE Xinan XUE Xianghui CHEN Gang YAN Jingye YAN Yihua LAN Ailan WANG Jiangyan WANG Xin TIAN Yufang | 2022 | 空间科学学报2022，42，4： | 1 |
| 2 | Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles显示文摘With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate. | Xin Liu Siya Xu Chao Yang Zhili Wang Hao Zhang Jingye Chi Qinghan Li | 2022 | Computer Systems Science & Engineering2022，42，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员