Data Mining Approach for Ship Virtual Assembly based on Rough Set Theory

被引：0
作者：
Li, Cuiling [1 ]
Zhao, Rongyong [2 ]
Xiang, Yang [1 ]
Xu, Shanlin [1 ]
机构：
[1] Shanghai Maritime Univ, Elect Engn & Automat Dept, Shanghai 200135, Peoples R China
[2] Tongji Univ, CIMS Res Ctr, Shanghai 200092, Peoples R China
来源：
2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, VOLS 1 AND 2, PROCEEDINGS | 2008年
关键词：
data mining; rough set theory; shipbuilding; virtual assembly; Petri net;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To discover process planning knowledge for ship virtual assembly, a novel data mining approach is proposed based on rough set theory. The present achievements about data mining with rough set theory and virtual assembly technology for shipbuilding are studied respectively The current Petri net model for ship assembly is analyzed. A new transform? model from Petri net to rough set theory is presented. Thereby a systematical approach is proposed based on the present process planning platform for shipbuilding and three dimension virtual assembly software. Finally, a data mining example from engineering of ship engine assembly illustrates that this approach is feasible. Some meaningful assembly knowledge is discovered. The dependence on operational experience of human-being can be reduced and the efficiency of ship virtual assembly can be enhanced obviously
引用
收藏
页码：691 / +
页数：2
相关论文
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