咨询与建议

限定检索结果

文献类型

  • 13 篇 期刊文献
  • 5 篇 会议

馆藏范围

  • 18 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 17 篇 工学
    • 10 篇 计算机科学与技术...
    • 8 篇 电气工程
    • 4 篇 信息与通信工程
    • 3 篇 石油与天然气工程
    • 2 篇 控制科学与工程
    • 1 篇 机械工程
    • 1 篇 仪器科学与技术
    • 1 篇 材料科学与工程(可...
    • 1 篇 动力工程及工程热...
    • 1 篇 电子科学与技术(可...
    • 1 篇 土木工程
    • 1 篇 交通运输工程
    • 1 篇 农业工程
    • 1 篇 城乡规划学
    • 1 篇 软件工程
    • 1 篇 网络空间安全
  • 1 篇 理学
    • 1 篇 物理学
    • 1 篇 化学
  • 1 篇 管理学
    • 1 篇 管理科学与工程(可...

主题

  • 18 篇 deep reinforceme...
  • 2 篇 reinforcement le...
  • 2 篇 path planning
  • 2 篇 digital twin syn...
  • 2 篇 mobile edge comp...
  • 2 篇 deep learning (a...
  • 2 篇 learning (artifi...
  • 1 篇 internet of thin...
  • 1 篇 action space
  • 1 篇 proton exchange ...
  • 1 篇 curriculum guida...
  • 1 篇 connected vehicl...
  • 1 篇 quantum process
  • 1 篇 adaptive cruise ...
  • 1 篇 mobility of supp...
  • 1 篇 energy managemen...
  • 1 篇 multiple-input m...
  • 1 篇 digital twin-ass...
  • 1 篇 privacy protecti...
  • 1 篇 deep learning

机构

  • 3 篇 city univ hong k...
  • 1 篇 southeast univ c...
  • 1 篇 univ fed piaui t...
  • 1 篇 shanxi univ coll...
  • 1 篇 sriram engn coll...
  • 1 篇 arunachala coll ...
  • 1 篇 shandong univ sc...
  • 1 篇 nantong univ sch...
  • 1 篇 china elect powe...
  • 1 篇 aalborg univ dep...
  • 1 篇 wenzhou business...
  • 1 篇 madanapalle inst...
  • 1 篇 tsinghua univ st...
  • 1 篇 politecn milan d...
  • 1 篇 univ paris 09 cn...
  • 1 篇 beijing univ civ...
  • 1 篇 south china univ...
  • 1 篇 state grid shanx...
  • 1 篇 soongsil univ 36...
  • 1 篇 beijing universi...

作者

  • 3 篇 liang weifa
  • 2 篇 zhang yuncan
  • 1 篇 lee dongsu
  • 1 篇 ma xiaoxuan
  • 1 篇 gupta nishu
  • 1 篇 yang luo
  • 1 篇 xie zhenzhen
  • 1 篇 rodrigues joel j...
  • 1 篇 xinwei zhang
  • 1 篇 zhao xianli
  • 1 篇 wang guixin
  • 1 篇 anvari-moghaddam...
  • 1 篇 langeroudi amir ...
  • 1 篇 al-durra ahmed
  • 1 篇 kwon minhae
  • 1 篇 yi liang
  • 1 篇 li keqiang
  • 1 篇 pal raghavendra
  • 1 篇 chen fei
  • 1 篇 zhanying li

语言

  • 18 篇 英文
检索条件"主题词=deep reinforcement learning algorithm"
18 条 记 录,以下是1-10 订阅
排序:
Model Predictive Adaptive Cruise Control of Intelligent Electric Vehicles Based on deep reinforcement learning algorithm FWOR Driver Characteristics
收藏 引用
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2023年 第4期24卷 1175-1187页
作者: Guo, Jinghua Li, WenChang Luo, Yugong Li, Keqiang Xiamen Univ Dept Mech & Elect Engn Xiamen 361005 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China
This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanis... 详细信息
来源: 评论
A Pareto-Efficient Task-Allocation Framework Based on deep reinforcement learning algorithm in MEC  18th
A Pareto-Efficient Task-Allocation Framework Based on Deep R...
收藏 引用
18th European-Alliance-for-Innovation (EAI) International Conference on Collaborative Computing - Networking, Applications and Worksharing (CollaborateCom)
作者: Liu, Wenwen Zhao, Sinong Yu, Zhaoyang Wang, Gang Liu, Xiaoguang Nankai Univ Coll Comp Sci TJ Key Lab NDST Tianjin Peoples R China
Mobile-edge computing (MEC) has emerged as a promising paradigm that moves tasks running in the cloud to edge servers. In MEC systems, there are various individual requirements, such as less user-perceived time and lo... 详细信息
来源: 评论
deep reinforcement learning for Mobility-Aware Digital Twin Migrations in Edge Computing
收藏 引用
IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年 第2期18卷 704-717页
作者: Zhang, Yuncan Wang, Luying Liang, Weifa City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410017 Peoples R China
The past decade witnessed an explosive growth on the number of IoT devices (objects/suppliers), including portable mobile devices, autonomous vehicles, sensors and intelligence appliances. To realize the digital repre... 详细信息
来源: 评论
Online topology-based voltage regulation: A computational performance enhanced algorithm based on deep reinforcement learning
收藏 引用
IET GENERATION TRANSMISSION & DISTRIBUTION 2022年 第24期16卷 4879-4892页
作者: Xu, Peng Wang, Beibei Zhang, Yue Zhu, Hong Southeast Univ Coll Elect Engn Nanjing 210096 Peoples R China State Grid Nanjing Power Supply Co Ltd Nanjing Peoples R China
The increasing use of distributed generation (DG) in power systems can result in frequent online voltage problems. In scenarios in which substantial DG prediction errors occur because of high DG accommodation levels, ... 详细信息
来源: 评论
deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad-hoc network
收藏 引用
IET COMMUNICATIONS 2020年 第19期14卷 3464-3471页
作者: Pal, Raghavendra Gupta, Nishu Prakash, Arun Tripathi, Rajeev Rodrigues, Joel J. P. C. Madanapalle Inst Technol & Sci Dept Elect & Commun Engn Madanapalle 517325 Andhra Pradesh India Vaagdevi Coll Engn Dept Elect & Commun Engn Warangal 506005 Telangana India Motilal Nehru Natl Inst Technol Allahabad Dept Elect & Commun Engn Prayagraj 211004 UP India Univ Fed Piaui Teresina PI Brazil Inst Telecomunicac Oes Aveiro Portugal
Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of ... 详细信息
来源: 评论
Data-driven optimal PEMFC temperature control via curriculum guidance strategy-based large-scale deep reinforcement learning
收藏 引用
IET RENEWABLE POWER GENERATION 2022年 第7期16卷 1283-1298页
作者: Li, Jiawen Yang, Shengchun Yu, Tao Zhang, Xiaoshun South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China China Elect Power Res Inst Nanjing Nanjing Peoples R China Shantou Univ Coll Engn Shantou Peoples R China
As the proton exchange membrane fuel cell (PEMFC) is a nonlinear, time-varying, multiple-input multiple-output system, an advanced controller with strong robustness and adaptability is required for controlling PEMFC s... 详细信息
来源: 评论
Cost-Aware Digital Twin Migration in Mobile Edge Computing via deep reinforcement learning  23
Cost-Aware Digital Twin Migration in Mobile Edge Computing v...
收藏 引用
23rd International-Federation-for-Information-Processing (IFIP) Networking Conference (IFIP Networking)
作者: Zhang, Yuncan Liang, Weifa City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China
The past decade experienced an explosive growth on the number of IoT devices connected to the Internet. Digital twins (DTs) emerge as key enablers to provide digital representations of objects for their monitoring, si... 详细信息
来源: 评论
deep reinforcement learning Based on Optical Neural Networks in Path Planning
Deep Reinforcement Learning Based on Optical Neural Networks...
收藏 引用
2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
作者: Yang, Zhiwei Lai, Yihang Dai, Jian Zhang, Tian Xu, Kun Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China
We propose the optical deep Q network (ODQN) algorithm based on optical neural networks (ONNs) to accelerate calculation in 2D grid path planning task. The calculated results demonstrate that the innovative algorithm ... 详细信息
来源: 评论
A High Reliable Computing Offloading Strategy Using deep reinforcement learning for IoVs in Edge Computing
收藏 引用
JOURNAL OF GRID COMPUTING 2021年 第2期19卷 15-15页
作者: Wang, Kun Wang, Xiaofeng Liu, Xuan Shanxi Univ Coll Phys & Elect Engn Taiyuan 030006 Shanxi Peoples R China State Grid Shanxi Elect Power Co Maintenance Branch Taiyuan 030000 Shanxi Peoples R China
In view of the serious problems of increasing delay energy consumption and decreasing service quality caused by complex network state and massive computing data in Internet of vehicles (IOT), a high reliable computing... 详细信息
来源: 评论
A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles
收藏 引用
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 第PartA期123卷
作者: Mei, Peng Karimi, Hamid Reza Xie, Hehui Chen, Fei Huang, Cong Yang, Shichun Beihang Univ Sch Transportat Sci & Engn Beijing Peoples R China Politecn Milan Dept Mech Engn Milan Italy Beijing Syst Design Inst Electro Mech Engn Beijing Peoples R China Nantong Univ Sch Transportat & Civil Engn Nantong Peoples R China
Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, thi... 详细信息
来源: 评论