机构

• 数学与系统科学研究院 [3]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2021 [1]
• 2020 [1]
• 2019 [1]
• 2011 [1]

学科主题

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 Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners 期刊论文  OAI收割 ENERGIES, 2021, 卷号: 14, 期号: 23, 页码: 19 作者:  Zeng, Isabella Yunfei;  Tan, Shiqi;  Xiong, Jianliang;  Ding, Xuesong;  Li, Yawen   |  收藏  |  浏览/下载：39/0  |  提交时间：2022/04/02 real-world fuel consumption rate  machine learning  big data  light-duty vehicle  China   Impact factors of the real-world fuel consumption rate of light duty vehicles in China 期刊论文  OAI收割 ENERGY, 2020, 卷号: 190, 页码: 12 作者:  Wu, Tian;  Han, Xiao;  Zheng, M. Mocarlo;  Ou, Xunmin;  Sun, Hongbo   |  收藏  |  浏览/下载：28/0  |  提交时间：2020/06/30 Real-world fuel consumption rate  Energy consumption  Private passenger vehicles  Big data  China   Multilayer Perceptron Method to Estimate Real-World Fuel Consumption Rate of Light Duty Vehicles 期刊论文  OAI收割 IEEE ACCESS, 2019, 卷号: 7, 页码: 63395-63402 作者:  Li, Yawen;  Tang, Guangcan;  Du, Jiameng;  Zhou, Nan;  Zhao, Yue   |  收藏  |  浏览/下载：35/0  |  提交时间：2020/01/10 Artificial intelligence  big data  multilayer perceptron  fuel consumption rate  light-duty vehicles   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：28/0  |  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.