一种面向低碳设计的多属性相似实例检索方法
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摘要
低碳设计纳入产品全生命周期对环境的影响因素,是协调产品常规性能与低碳性能的回溯设计过程。设计知识重用技术在已有设计知识的基础上提高产品低碳设计效率,而知识重用的前提是相似实例的检索。提出一种基于关联函数的多属性相似实例检索方法,结合原有多维关联函数构造方法和侧距原理,将可拓一维关联函数拓展到多维,从最优点只在中点的应用条件推广到最优点在区间内任意点;推导出多维关联函数的降维计算规则,构建相似度函数。该方法应用于螺杆空压机多维低碳属性需求检索,计算结果与实例空间位置关系相一致;并与欧式几何距方法和KNN分类检索方法进行对比分析,分析表明基于关联函数的方法与欧式几何距方法检索结果一致,并且能够克服欧式距忽略属性物理含义造成检索不合理的缺陷;与KNN方法的检索分类结果一致,同时每维属性关联函数值可以作为对应属性修改难度的参考评价。
Environmental factors are taken into account for low-carbon design in product life cycle,which is a multiple backs design process in balancing conventional performance and low-carbon performance.Efficiency and effectiveness of low-carbon design are promoted with knowledge reuse technique;however,the premise is similar cases retrieval.A retrieval method based on the dependent function is proposed.Firstly,previous construction method of multi-dimensional dependent function and mathematical principle of side distance are combined,the one–dimensional dependent function is expanded to the multi-dimensional dependent function,and application condition is also improved by setting the optimal point only in geometric center to arbitrary point.Rule of dimensionality reduction for multi-dimensional dependent function is derived,and similarity function is constructed.In final,the proposed method is applied to attributes retrieval for screw compressor cases,and the computational result is consistent with the spatial position of each case.Comparisons are analyzed with Euclidean geometry distance method and the KNN classification method;it reveals that the weakness of unreasonable retrieval due to the neglected natural property of each attribute is overcome by proposed method,and the retrieval result is similar to that of the Euclidean geometry distance method;the classification result is equal to that of KNN method,and each one-dimensional dependent function value can be taken as the criteria for adaptation of attributes.
Environmental factors are taken into account for low-carbon design in product life cycle,which is a multiple backs design process in balancing conventional performance and low-carbon performance.Efficiency and effectiveness of low-carbon design are promoted with knowledge reuse technique;however,the premise is similar cases retrieval.A retrieval method based on the dependent function is proposed.Firstly,previous construction method of multi-dimensional dependent function and mathematical principle of side distance are combined,the one–dimensional dependent function is expanded to the multi-dimensional dependent function,and application condition is also improved by setting the optimal point only in geometric center to arbitrary point.Rule of dimensionality reduction for multi-dimensional dependent function is derived,and similarity function is constructed.In final,the proposed method is applied to attributes retrieval for screw compressor cases,and the computational result is consistent with the spatial position of each case.Comparisons are analyzed with Euclidean geometry distance method and the KNN classification method;it reveals that the weakness of unreasonable retrieval due to the neglected natural property of each attribute is overcome by proposed method,and the retrieval result is similar to that of the Euclidean geometry distance method;the classification result is equal to that of KNN method,and each one-dimensional dependent function value can be taken as the criteria for adaptation of attributes.
引文
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[23]赵燕伟,任设东,陈尉刚,等.基于改进BP神经网络的可拓分类器构建[J].计算机集成制造系统,2015,21(10):2807-2815.ZHAO Yanwei,REN Shedong,CHEN Weigang,et al.Extension classifier construction based on improved BPneural network[J].Computer Integrated Manufacturing Systems,2015,21(10):2807-2815.
[2]谢友柏.现代设计与知识获取[J].中国机械工程,1996,7(6):36-41.XIE Youbai.Modern design and knowledge acquisition[J].China Mechanical Engineering,1996,7(6):36-41.
[3]郭鑫,赵武,王杰,等.面向创新设计的工艺设计知识模型及检索方法研究[J].机械工程学报,2017,53(15):66-72.GUO Xin,ZHAO Wu,WANG Jie,et al.A study of knowledge modeling and retrieval methods oriented towards innovative design of manufacturing planning[J].Journal of Mechanical Engineering,2017,53(15):66-72.
[4]HE B,WANG J,HUANG S,et al.Low-carbon product design for product life cycle[J].Journal of Engineering Design,2015,26(10-12):321-339.
[5]刘飞,刘培基,李聪波,等.制造系统能量效率研究的现状及难点问题[J].机械工程学报,2017,53(5):1-11.LIU Fei,LIU Peiji,LI Congbo,et al.The statute and difficult problems of research on energy efficiency of manufacturing systems[J].Journal of Mechanical Engineering,2017,53(5):1-11.
[6]钟智,朱曼龙,张晨,等.最近邻分类方法的研究[J].计算机科学与探索,2011,5(5):467-473.ZHONG Zhi,ZHU Manlong,ZHANG Chen,et al.Research on nearest neighbors classification techniques[J].Journal of Frontiers of Computer Science and Technology,2011,5(5):467-473.
[7]董亭亭.大数据下空间数据索引和KNN查询技术的研究[D].大连:大连理工大学,2013.DONG Tingting.The research of spatial data indexing and KNN query on large data sets[D].Dalian:Dalian University of Technology,2013.
[8]HJALTASON G R,SAMET H.Index-driven similarity search in metric spaces[J].ACM Transactions on Database Systems,2003,28(4):517-580.
[9]STEPHANE N,HECTOR R,MARC L L J.Effective retrieval and new indexing method for case based reasoning:Application in chemical process design[J].Engineering Applications of Artificial Intelligence,2010,23(6):880-894.
[10]PAL S K,SHIU S C K.Foundations of soft case-based reasoning[M].Hoboken,New Jersey:John Willy and Sons,2004.
[11]BUENO R,TRAINA A J M,TRAINA C.Genetic algorithms for approximate similarity queries[J].Data&Knowledge Engineering,2007,62(3):459-482.
[12]NARIONI M C N B,RAZENTE H L,TRAINA A J M,et al.Accelerating k-medoid-based algorithms through metric access methods[J].Journal of Systems and Software,2008,81(3):343-355.
[13]GIACHETTI R E,YOUNG R E,ROGGATZ A,et al.Amethodology for the reduction of imprecision in the engineering process[J].European Journal of Operational Research,1997,100(2):277-292.
[14]AGARD B,BARAJAS M.The use of fuzzy logic in product family development:literature review and opportunities[J].Journal of Intelligent Manufacturing,2010,23(5):1445-1462.
[15]ZHU G N,HU J,QI J,et al.An integrated AHP and VIKOR for design concept evaluation based on rough number[J].Advanced Engineering Informatics,2015,29(3):408-418.
[16]SMYTH B,KEANE M T.Adaptation-guided retrieval:questioning the similarity assumption in reasoning[J].Artificial Intelligence,1998,102(2):249-293.
[17]CASTRO J L,NAVARRO M,SANCHEZ J M,et al.Loss and gain functions for CBR retrieval[J].Information Sciences,2009,179(11):1738-1750.
[18]NOUAOURIA N,BOUKADOUM M.Case retrieval with combined adaptability and similarity criteria:application to case retrieval nets[C]//Proceedings of the 18th International Confeerence on Case-Based Reasoning Research and Development,July 19-22,2010,Alessandria,Italy.Berlin:Springer,2010:242-256.
[19]蔡文.可拓论及其应用[J].科学通报,1999,44(7):673-682.CAI Wen.Extension theory and its application[J].Chinese Science Bulletin,1999,44(7):673-682.
[20]陈建,赵燕伟,李方义,等.基于转换桥方法的产品绿色设计冲突消解[J].机械工程学报,2010,46(9):132-142.CHEN Jian,ZHAO Yanwei,LI Fangyi,et al.Transforming bridge-based conflict resolution for product green design[J].Journal of Mechnical Engineering,2010,46(9):132-142.
[21]杨春燕,蔡文.可拓学[M].北京:科学出版社,2014.YANG Chunyan,CAI Wen.Extenics[M].Beijing:Science Press,2014.
[22]SMARANDACHE F.Extenics in higher dimensions[M].Columbus,USA:Education Publisher,2012.
[23]赵燕伟,任设东,陈尉刚,等.基于改进BP神经网络的可拓分类器构建[J].计算机集成制造系统,2015,21(10):2807-2815.ZHAO Yanwei,REN Shedong,CHEN Weigang,et al.Extension classifier construction based on improved BPneural network[J].Computer Integrated Manufacturing Systems,2015,21(10):2807-2815.
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