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Density Peaks Clustering Based on Multiple Distance Measures for Manufacturing Process
Li S(李帅); Gao SY(高诗莹); Zhou XF(周晓锋)
作者部门数字工厂研究室
会议名称2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
会议日期July 31 - August 4, 2016
会议地点Ningbo, China
会议录名称Proceedings of the IEEE International Conference on Information and Automation
出版者IEEE
出版地New York
2016
页码1143-1148
收录类别EI ; CPCI(ISTP)
EI收录号20171203484703
WOS记录号WOS:000405519500204
产权排序1
ISBN号978-1-5090-4102-2
关键词Density Peaks Clustering Distance Measure Mode Division Manufacturing Process
摘要Data analysis and processing of manufacturing process is significant to ensure the stable production safety, maintain quality stabilization, and optimize production profit. Practical manufacturing process often has complex characteristics, such as multimode, nonlinearity, etc. Mode division can divide manufacturing process into multiple modes and is useful for subsequent process monitoring and scheduling optimizing. In this paper, density peaks clustering (DPC) based on multiple distance measures is used for mode division in manufacturing process. Multiple distance measures for computing the local density and minimum distance between the point and any other point with higher density in DPC are compared and analyzed. To illustrate the effectiveness of the clustering method for mode division in manufacturing process, experiments are developed based on penicillin fermentation process and practical foods industrial production process. Experimental results verify the feasibility and efficiency of the clustering method for mode division in manufacturing process.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19484
专题数字工厂研究室
通讯作者Li S(李帅)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, 110016, China
3.College of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China
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Li S,Gao SY,Zhou XF. Density Peaks Clustering Based on Multiple Distance Measures for Manufacturing Process[C]. New York:IEEE,2016:1143-1148.
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