机构

• 自动化研究所 [3]
• 长春光学精密机械与物... [1]
• 软件研究所 [1]

采集方式

• OAI收割 [5]

内容类型

• 期刊论文 [4]
• 会议论文 [1]

发表日期

• 2016 [2]
• 2013 [1]
• 2011 [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 A framework for diversifying recommendation lists by user interest expansion 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2016, 卷号: 105, 期号: 1, 页码: 83-95 作者:  Zhang, Zhu;  Zheng, Xiaolong;  Zeng, Daniel Dajun   |  收藏  |  浏览/下载：34/0  |  提交时间：2019/10/08 Recommender Systems  Interest Expansion  Social Tagging System  Collaborative Filtering  Diversity   A framework for diversifying recommendation lists by user interest expansion 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2016, 卷号: 105, 页码: 83-95 作者:  Zhang, Zhu;  Zheng, Xiaolong;  Zeng, Daniel Dajun 收藏  |  浏览/下载：25/0  |  提交时间：2016/10/20 Recommender systems  Collaborative filtering  Diversity  Interest expansion  Social tagging system   船舶信息系统数据分发服务研究 期刊论文  OAI收割 计算机工程, 2013, 卷号: 39, 期号: 9, 页码: 94-97,113 廖闯; 郑刚; 高骞   |  收藏  |  浏览/下载：104/0  |  提交时间：2014/12/16 船舶信息系统  数据分发服务  中间件  发布/订阅  兴趣过滤  系统集成  ship information system  Data Distribution Service(DDS)  middleware  publish/subscribe  interest filtering  system integration   A recommender system based on tag and time information for social tagging systems 期刊论文  OAI收割 EXPERT SYSTEMS WITH APPLICATIONS, 2011, 卷号: 38, 期号: 4, 页码: 4575-4587 作者:  Zheng, Nan;  Li, Qiudan 收藏  |  浏览/下载：31/0  |  提交时间：2015/08/12 Social tagging  Recommender system  Collaborative filtering  Interest drift   Real-time motive vehicle detection with adaptive background updating model and HSV colour space (EI CONFERENCE) 会议论文  OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China Rong-Hui Z.; Bai Y.; Hong-guang J.; Chen T. 收藏  |  浏览/下载：75/0  |  提交时间：2013/03/25 In the transportation monitor system  we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that  motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately  the arithmetic proposed in the paper can suppress shadow availably  we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter  detect motive vehicle accurately and satisfy real-time motive vehicle tracking. 2009 SPIE.  and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle  we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering