为提高船舶设计质量管理水平,以某满足《协调共同结构规范》(Harmonized Common Structure Rule,HCSR)的巴拿马型散货船为研究对象,利用计划、执行、检查、处理(Plan,Do,Check,Act,PDCA)循环法基本原则对设计质量进行管控,并建立同一船...为提高船舶设计质量管理水平,以某满足《协调共同结构规范》(Harmonized Common Structure Rule,HCSR)的巴拿马型散货船为研究对象,利用计划、执行、检查、处理(Plan,Do,Check,Act,PDCA)循环法基本原则对设计质量进行管控,并建立同一船型设计质量管理体系。此外,对船舶能效指数(Energy Efficiency Design Index,EEDI)提升和结构轻量化设计的研讨方法进行分析。研究成果可为船舶设计质量管理提供一定参考。展开更多
In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Associ...In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.展开更多
Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are ...Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are redundant or minor variations of others.Their existence may simply be due to chance rather than truecorrelation. Thus, those spurious and insignificant rules should beremoved. In this paper, we propose a novel technique to over- Comethis problem. The technique firstly introduces the newconcept-structure rule cover, and then present a Quantitative methodto prune redundant correlation patterns. The user can now obtain acomplete picture of the do- Main without being overwhelmed by a hugenumber of rules.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.50539010)the Special Fund for Public Welfare Industry of the Ministry of Water Resources of China(Grant No.200801019)
文摘In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.
文摘Efficient methods exist for discovering association rules fromlarge collections of data. The number of discovered rules can,however, be so large. At the same time it is well known that manydiscovered associations are redundant or minor variations of others.Their existence may simply be due to chance rather than truecorrelation. Thus, those spurious and insignificant rules should beremoved. In this paper, we propose a novel technique to over- Comethis problem. The technique firstly introduces the newconcept-structure rule cover, and then present a Quantitative methodto prune redundant correlation patterns. The user can now obtain acomplete picture of the do- Main without being overwhelmed by a hugenumber of rules.
