机构

• 自动化研究所 [4]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [5]

内容类型

• 会议论文 [3]
• 期刊论文 [2]

发表日期

• 2016 [2]
• 2015 [1]
• 2013 [1]
• 2009 [1]

学科主题

筛选

浏览/检索结果: 共5条，第1-5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 Parallel Transportation Management and Control System and Its Applications in Building Smart Cities 期刊论文  OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 卷号: 17, 期号: 6, 页码: 1576-1585 作者:  Zhu, Fenghua;  Li, Zhenjiang;  Chen, Songhang;  Xiong, Gang 收藏  |  浏览/下载：53/0  |  提交时间：2016/10/20 Intelligent transportation systems (ITS)  artificial system  computational experiment and parallel execution (ACP) approach  parallel transportation management and control system (PTMS)  computational experiment  ITS cloud  social transportation  transportation knowledge automation   Exploring Optimal Frequency Caps in Real Time Bidding Advertising 会议论文  OAI收割 USA, 2016 作者:  Qin, Rui;  Yuan, Yong;  Wang, Fei-Yue   |  收藏  |  浏览/下载：31/0  |  提交时间：2017/09/21 Real Time Bidding  Frequency Capping  Computational Experiment Approach  Demand Side Platforms  Computational Advertising   Research on the Frequency Capping Issue in RTB Advertising: A Computational Experiment Approach 会议论文  OAI收割 Wuhan, China, Nov. 27-29, 2015 作者:  Qin, Rui;  Yuan, Yong;  Wang, Fei-Yue;  Li, Juanjuan;  Rui Qin   |  收藏  |  浏览/下载：26/0  |  提交时间：2016/06/20 Real Time Bidding  Computational Advertising  Frequency Capping  Computational Experiment Approach  Demand Side Platforms   Computational Traffic Experiments Based on Artificial Transportation Systems: An Application of ACP Approach 期刊论文  OAI收割 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 卷号: 14, 期号: 1, 页码: 189-198 作者:  Zhu, Fenghua;  Wen, Ding;  Chen, Songhang 收藏  |  浏览/下载：26/0  |  提交时间：2015/08/12 Agent  Artificial societies  artificial transportation systems (ATSs)  computational experiment  "simple is consistent"  Computational experiments  and Parallel execution (ACP) approach   Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文  OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者:  Wang D.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：24/0  |  提交时间：2013/03/25 In feature-level fusion recognition system  the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general  there are two main missions. One is improving the recognition correct rate as soon as possible  the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions  this paper presents a more rational and accurate optimization  Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition  we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last  we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points  while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.