机构

• 自动化研究所 [5]
• 地理科学与资源研究所 [3]
• 长春光学精密机械与物... [3]
• 沈阳自动化研究所 [1]

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• OAI收割 [12]

内容类型

• 会议论文 [4]
• 期刊论文 [4]
• SCI/SSCI论文 [2]
• 学位论文 [2]

发表日期

• 2024 [1]
• 2023 [1]
• 2016 [3]
• 2014 [1]
• 2013 [1]
• 2011 [3]
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学科主题

筛选

浏览/检索结果: 共12条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system 期刊论文  OAI收割 CHINESE JOURNAL OF AERONAUTICS, 2024, 卷号: 37, 期号: 7, 页码: 301-316 作者:  Cai, Kaiquan;  Tang, Shuo;  Qian, Shengsheng;  Shen, Zhiqi;  Yang, Yang   |  收藏  |  浏览/下载：14/0  |  提交时间：2024/09/09 Air traffic control  Graph neural network  Multi-faceted information  Air traffic flow prediction  Multi-airport system   Preliminary Concept of Urban Air Mobility Traffic Rules 期刊论文  OAI收割 DRONES, 2023, 卷号: 7, 期号: 1, 页码: 22 作者:  Qu, Wenqiu;  Xu, Chenchen;  Tan, Xiang;  Tang, Anqi;  He, Hongbo   |  收藏  |  浏览/下载：32/0  |  提交时间：2023/03/03 urban air mobility  traffic rules  traffic flow control  eVTOL aircraft   A model-based demand-balancing control for dynamically divided multiple urban subnetworks 期刊论文  OAI收割 JOURNAL OF ADVANCED TRANSPORTATION, 2016, 卷号: 50, 期号: 6, 页码: 1046-1060 作者:  Lin, Shu;  Kong, Qing-Jie;  Huang, Qingming 收藏  |  浏览/下载：32/0  |  提交时间：2017/02/14 traffic network control  urban road network  macroscopic fundamental diagram  traffic flow equilibrium   REINFORCEMENT LEARNING FOR RAMP CONTROL: AN ANALYSIS OF LEARNING PARAMETERS SCI/SSCI论文  OAI收割 2016 作者:  Lu C.;  Huang, J.;  Gong, J. W.;  Deng, Y. 收藏  |  浏览/下载：31/0  |  提交时间：2016/12/16 reinforcement learning  Q-learning  ramp control  agent  macroscopic  traffic flow model  freeway  model   REINFORCEMENT LEARNING FOR RAMP CONTROL: AN ANALYSIS OF LEARNING PARAMETERS SCI/SSCI论文  OAI收割 2016 作者:  Lu C.;  Huang, J.;  Gong, J. W.   |  收藏  |  浏览/下载：25/0  |  提交时间：2017/11/09 reinforcement learning  Q-learning  ramp control  agent  macroscopic  traffic flow model  freeway  model   A Simulation Analysis on the Existence of Network Traffic Flow Equilibria 期刊论文  OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 卷号: 15, 期号: 4, 页码: 1706-1713 作者:  Lin, Shu;  Kong, Qing-Jie;  Huang, Qingming 收藏  |  浏览/下载：31/0  |  提交时间：2015/08/12 Macroscopic fundamental diagram (MFD)  traffic flow equilibrium  traffic network control   基于ACP的智能交通系统云服务平台研究 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2013 作者:  李双双 收藏  |  浏览/下载：101/0  |  提交时间：2015/09/02 智能交通系统  平行系统理论  ACP  云计算  Hadoop  交通流预测  控制代理  Intelligent Transportation Systems  Parallel System Theory  ACP Method  Cloud Computing  Hadoop  Traffic Flow Forecasting  Control Agent   Energy-consumption-related robust optimization scheduling strategy for elevator group control system (EI CONFERENCE) 会议论文  OAI收割 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011, September 17, 2011 - September 19, 2011, Qingdao, China 作者:  Wang F.;  Wang F. 收藏  |  浏览/下载：31/0  |  提交时间：2013/03/25 Group elevator scheduling (GES) problem is an optimization problem. In this work  a robust optimization scheduling method is proposed to solve elevator group control system problem with passenger traffic flow uncertainty. An uncertain optimization model of elevator group scheduling is set up based on considering both energy consumption and waiting time. When considering the uncertainty of passenger traffic flow  the model is an uncertain optimization problem. It is tuned into its robust counterpart (RC) based on robust optimization theory. The simulation experiment presents the proposed scheduling method's scheduling performance. 2011 IEEE.   Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文  OAI收割 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states Dai Y.; Hu J.; Zhao D.; Zhu F. 收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25 Traffic congestion leads to problems like delays  decreasing flow rate  and higher fuel consumption. Consequently  keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus  computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper  a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.   Pricing Scheme based Nash Q-learning Flow Control for Multi-user Network 会议论文  OAI收割 International Conference on Materials, Mechatronics and Automation (ICMMA 2011), Melbourne, AUSTRALIA, JAN 15-16, 2011 作者:  Li X(李鑫);  Yu HB(于海斌) 收藏  |  浏览/下载：22/0  |  提交时间：2012/05/29 Computer simulation  Controllers  Costs  Flow control  HIgh speed networks  Mechatronics  Quality of service  Time varying networks  Traffic congestion