Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness
Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition o...
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| Veröffentlicht in: | Journal of iron and steel research, international international, 2009-09, Vol.16 (5), p.50-55 |
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| creator | TANG, Cheng-long WANG, Shi-gang LIANG, Qin-hua XU, Wei |
| description | Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system. |
| doi_str_mv | 10.1016/S1006-706X(10)60010-6 |
| format | Article |
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Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.</description><identifier>ISSN: 1006-706X</identifier><identifier>EISSN: 2210-3988</identifier><identifier>DOI: 10.1016/S1006-706X(10)60010-6</identifier><language>eng</language><publisher>Singapore: Elsevier Ltd</publisher><subject>Algorithms ; Applied and Technical Physics ; Clustering ; Density ; density queue ; Engineering ; improved k-means algorithm ; Iron and steel industry ; Machines ; Manufacturing ; Materials Engineering ; Materials Science ; Metallic Materials ; On-line systems ; Pattern recognition ; Physical Chemistry ; Processes ; Steels ; Strip steel ; transversal thickness distribution ; 厚度分布 ; 厚度精度 ; 带钢厚度 ; 最终产品 ; 横向分布 ; 特征识别 ; 聚类算法 ; 质量控制体系</subject><ispartof>Journal of iron and steel research, international, 2009-09, Vol.16 (5), p.50-55</ispartof><rights>2009 Central Iron and Steel Research Institute</rights><rights>China Iron and Steel Research Institute Group 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-ad8ed015889d7a586682ada6cce167a32308d924b91b703a3ff17851b3da47cb3</citedby><cites>FETCH-LOGICAL-c415t-ad8ed015889d7a586682ada6cce167a32308d924b91b703a3ff17851b3da47cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/86787X/86787X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1016/S1006-706X(10)60010-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://dx.doi.org/10.1016/S1006-706X(10)60010-6$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,41488,42557,45995,51319</link.rule.ids></links><search><creatorcontrib>TANG, Cheng-long</creatorcontrib><creatorcontrib>WANG, Shi-gang</creatorcontrib><creatorcontrib>LIANG, Qin-hua</creatorcontrib><creatorcontrib>XU, Wei</creatorcontrib><title>Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness</title><title>Journal of iron and steel research, international</title><addtitle>J. Iron Steel Res. Int</addtitle><addtitle>Journal of Iron and Steel Research</addtitle><description>Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.</description><subject>Algorithms</subject><subject>Applied and Technical Physics</subject><subject>Clustering</subject><subject>Density</subject><subject>density queue</subject><subject>Engineering</subject><subject>improved k-means algorithm</subject><subject>Iron and steel industry</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Materials Engineering</subject><subject>Materials Science</subject><subject>Metallic Materials</subject><subject>On-line systems</subject><subject>Pattern recognition</subject><subject>Physical Chemistry</subject><subject>Processes</subject><subject>Steels</subject><subject>Strip steel</subject><subject>transversal thickness distribution</subject><subject>厚度分布</subject><subject>厚度精度</subject><subject>带钢厚度</subject><subject>最终产品</subject><subject>横向分布</subject><subject>特征识别</subject><subject>聚类算法</subject><subject>质量控制体系</subject><issn>1006-706X</issn><issn>2210-3988</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkVFPHCEUhUlTk26sP6EJ8UX7MC0MMwzzZMyqrYmJpq6Jb4SBO7PoLKzAbNL-ehnX9FUe4AbOuXA-EPpGyQ9KKP95TwnhRUP44ykl3zkhlBT8E1qUZS5YK8RntPgv-YKOYnwi82g5K8UCDdebbfA7MPhOpQTB4eU4xVxYN-DzcfDBpvUG9z7gP6D94Oy_-WQVlIs7CFGN-MLGFGw3Jesd9j2-TwBjnoPd4tXa6mcHMX5FB70aIxy9r4fo4epytfxd3Nz-ul6e3xS6onUqlBFgCK2FaE2jasG5KJVRXGugvFGsZESYtqy6lnYNYYr1PW1ETTtmVNXojh2ik33fnOplgpjkxkYN46gc-ClKwauKtKUgWVnvlTr4GAP0chvsRoW_khI5o5VvaOXMbd56Qyt59vG9L25nSBDkk5-Cy6E-NJ7tjZDz72w2Rm3BaTA2gE7SePthh-P3J6-9G17y7bJT-rm3I0iW-Yg6_-orQ7CeQA</recordid><startdate>20090901</startdate><enddate>20090901</enddate><creator>TANG, Cheng-long</creator><creator>WANG, Shi-gang</creator><creator>LIANG, Qin-hua</creator><creator>XU, Wei</creator><general>Elsevier Ltd</general><general>Springer Singapore</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20090901</creationdate><title>Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness</title><author>TANG, Cheng-long ; WANG, Shi-gang ; LIANG, Qin-hua ; XU, Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-ad8ed015889d7a586682ada6cce167a32308d924b91b703a3ff17851b3da47cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithms</topic><topic>Applied and Technical Physics</topic><topic>Clustering</topic><topic>Density</topic><topic>density queue</topic><topic>Engineering</topic><topic>improved k-means algorithm</topic><topic>Iron and steel industry</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Materials Engineering</topic><topic>Materials Science</topic><topic>Metallic Materials</topic><topic>On-line systems</topic><topic>Pattern recognition</topic><topic>Physical Chemistry</topic><topic>Processes</topic><topic>Steels</topic><topic>Strip steel</topic><topic>transversal thickness distribution</topic><topic>厚度分布</topic><topic>厚度精度</topic><topic>带钢厚度</topic><topic>最终产品</topic><topic>横向分布</topic><topic>特征识别</topic><topic>聚类算法</topic><topic>质量控制体系</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>TANG, Cheng-long</creatorcontrib><creatorcontrib>WANG, Shi-gang</creatorcontrib><creatorcontrib>LIANG, Qin-hua</creatorcontrib><creatorcontrib>XU, Wei</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Journal of iron and steel research, international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>TANG, Cheng-long</au><au>WANG, Shi-gang</au><au>LIANG, Qin-hua</au><au>XU, Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness</atitle><jtitle>Journal of iron and steel research, international</jtitle><stitle>J. Iron Steel Res. Int</stitle><addtitle>Journal of Iron and Steel Research</addtitle><date>2009-09-01</date><risdate>2009</risdate><volume>16</volume><issue>5</issue><spage>50</spage><epage>55</epage><pages>50-55</pages><issn>1006-706X</issn><eissn>2210-3988</eissn><abstract>Transversal distribution of the steel strip thickness in the entry section of the cold rolling mill seriously affects to the flatness and transversal thickness precision of the final products. Pattern clustering method is introduced into the steel rolling field and used in the patterns recognition of transversal distribution of the steel strip thickness. The well-known k-means clustering algorithm has the advantage of being easily completed, but still has some drawbacks. An improved k-means clustering algorithm is presented, and the main improvements include： （1） the initial clustering points are preselected according to the density queue of data objects; and （2） Mahalanobis distance is applied instead of Euclidean distance in the actual application. Compared to the patterns obtained from the common kmeans algorithm, the patterns identified by the improved algorithm show that the improved clustering algorithm is well suitable for the patterns＇ recognition of transversal distribution of steel strip thickness and it will be useful in online quality control system.</abstract><cop>Singapore</cop><pub>Elsevier Ltd</pub><doi>10.1016/S1006-706X(10)60010-6</doi><tpages>6</tpages></addata></record> |
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| source | ScienceDirect Journals (5 years ago - present); Alma/SFX Local Collection; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Applied and Technical Physics Clustering Density density queue Engineering improved k-means algorithm Iron and steel industry Machines Manufacturing Materials Engineering Materials Science Metallic Materials On-line systems Pattern recognition Physical Chemistry Processes Steels Strip steel transversal thickness distribution 厚度分布 厚度精度 带钢厚度 最终产品 横向分布 特征识别 聚类算法 质量控制体系 |
| title | Improved Pattern Clustering Algorithm for Recognizing Transversal Distribution of Steel Strip Thickness |
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