期刊文献+

基于“工业4.0”的智能船舶系统探讨 被引量:25

Study of Intelligent Ship System Based on Industrie 4.0
下载PDF
导出
摘要 以大数据为基础,预测技术为核心的"工业4.0"对船舶工业的发展必将产生深远的影响。介绍了基于"工业4.0"的智能船舶系统的基本概念及体系结构,将网络互联和船舶实体深度融合,构建了集系统化、网络化、智能化和服务化为一体的智能服务体系,分析了各模块的功能,探讨了传统船舶与智能船舶系统的区别。最后,具体阐述了智能船舶系统的构建步骤与过程。 The "lndustrie 4.0", based on big data technology, taking forecasting techniques as the core, will surely exert a far-reaching influence on the development of ship industry. In this paper, the conceptual framework and system architecture of intelligent system for ship is introduced Through deeply integrating the network interconnection with the ship entity, an intelligent service system integrating systematization, networking, intelligentization and servicization is built. The function of every module is analyzed The distinctions between the traditional ship and the intelligent ship system are discussed. In the end, the procedures and processes to build the intelligent system for ships are specifically stated
出处 《船舶工程》 北大核心 2015年第11期58-60,71,共4页 Ship Engineering
关键词 工业4.0 船舶工业 智能船舶 智能服务 Industrie 4. 0 shipbuilding industry intelligent ship intelligent service
  • 相关文献

参考文献6

  • 1乌尔里希.森德勒.工业4.0:即将来袭的第四次工业革命[M].邓敏,李现民,译.北京:机械工业出版社,2014.
  • 2国务院.国务院关于印发《中国制造2025》的通知[EB/OL].http-J/www.gov.crdzhengce/content/2015-05/19/content_9784.htm/2015.
  • 3DNV GL. The Future of Shipping[R]. 2014.
  • 4陈瑜.船舶工业4.0:-场造船业和航运业的工业智能化革命[N].科技日报,2014-10-20(3).
  • 5李矛阳.大数据:开启智能船舶时代[EB/OL].[2014-10-20] http://www.ce.cn/xwzx/gnsz/gdxw/ 201410/20/t20141020_3730494.shtml.
  • 6王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138. 被引量:711

二级参考文献68

  • 1Big data. Nature, 2008, 455(7209): 1-136.
  • 2Dealing with data. Science,2011,331(6018): 639-806.
  • 3Holland J. Emergence: From Chaos to Order. RedwoodCity,California: Addison-Wesley? 1997.
  • 4Anthony J G Hey. The Fourth Paradigm: Data-intensiveScientific Discovery. Microsoft Research, 2009.
  • 5Phan X H, Nguyen L M,Horiguchi S. Learning to classifyshort and sparse text Web with hidden topics from large-scale data collections//Proceedings of the 17th InternationalConference on World Wide Web. Beijing, China,2008:91-100.
  • 6Sahami M, Heilman T D. A web-based kernel function formeasuring the similarity of short text snippets//Proceedingsof the 15th International Conference on World Wide Web.Edinburgh, Scotland, 2006: 377-386.
  • 7Efron M, Organisciak P,Fenlon K. Improving retrieval ofshort texts through document expansion//Proceedings of the35th International ACM SIGIR Conference on Research andDevelopment in Information Retrieval. Portland, OR, USA,2012: 911-920.
  • 8Hong L,Ahmed A, Gurumurthy S,Smola A J, Tsioutsiou-liklis K. Discovering geographical topics in the twitterstream//Proceedings of the 21st International Conference onWorld Wide Web(WWW 2012). Lyon, France, 2012:769-778.
  • 9Pozdnoukhov A,Kaiser C. Space-time dynamics of topics instreaming text//Proceedings of the 3rd ACM SIGSPATIALInternational Workshop on Location-Based Social Networks.Chicago-IL,USA, 2011: 1-8.
  • 10Sun Yizhou,Norick Brandon, Han Jiawei, Yan Xifeng, YuPhilip S,Yu Xiao. Integrating meta-path selection with user-guided object clustering in heterogeneous information net-works/ /Proceedings of the 18th ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining.Beijing, China, 2012: 1348-1356.

共引文献722

同被引文献121

引证文献25

二级引证文献139

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部