机构

• 地理科学与资源研究所 [1]
• 成都山地灾害与环境研... [1]
• 长春光学精密机械与物... [1]
• 遥感与数字地球研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [2]
• SCI/SSCI论文 [1]
• 会议论文 [1]

发表日期

• 2020 [1]
• 2016 [1]
• 2015 [1]
• 2011 [1]

学科主题

• Physical G... [1]

筛选

浏览/检索结果: 共4条，第1-4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 中巴经济走廊DMSP/OLS与NPP/VIIRS夜光数据辐射一致性校正 期刊论文  OAI收割 遥感学报, 2020, 卷号: 24, 期号: 2, 页码: 149-160 作者:  梁丽;  边金虎;  李爱农;  冯文兰;  雷光斌   |  收藏  |  浏览/下载：97/0  |  提交时间：2020/04/29 DMSP/OLS  Intercalibration  Invariant regions  Night  time lights  NPP/VIIRS  Pakistan   Urban expansion in China and its spatial-temporal differences over the past four decades 期刊论文  OAI收割 JOURNAL OF GEOGRAPHICAL SCIENCES, 2016, 卷号: 26, 期号: 10, 页码: 1477-1496 作者:  Liu, Fang;  Zhang, Zengxiang;  Shi, Lifeng;  Zhao, Xiaoli;  Xu, Jinyong 收藏  |  浏览/下载：24/0  |  提交时间：2017/04/24 LAND-COVER CHANGE  URBANIZATION DYNAMICS  IMPERVIOUS SURFACES  SATELLITE IMAGERY  HUMAN-SETTLEMENTS  SPATIAL-PATTERNS  DRIVING FORCES  CITY LIGHTS  TIME-SERIES  EXPANSION   Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data SCI/SSCI论文  OAI收割 2015 作者:  Ma T.;  Zhou, Y. K.;  Zhou, C. H. 收藏  |  浏览/下载：60/0  |  提交时间：2015/12/09 Night-time light  Urbanization  DMSP/OLS  Quadratic relationship  China's cities  urban sprawl  city lights  imagery  gas  world  proxy   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.