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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
Mechanical Engineering     
User needs matching based on latent semantic analysis and kansei engineering
LIU Zheng hong1, XIE Qing sheng1, LI Shao bo1,2, LIN Li2
1. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou 550003, China; 2. School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China
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Abstract  

A new LSA KE approach was presental based on latent semantic analysis (LSA) in order to meet user needs more accurate and improve product design efficiency. LSA was used match terms to documents for document indexing, and kansei engineering (KE) was used to match user needs to designs. A RKE model was created to obtain the KE weights for all design elements and all Kansei word pairs. The KE model was used to establish a LSA KE semantic space and calculate Kansei values for new designs, determine Kansei values for user needs through survey users simultaneously. The KE and the LSA KE semantic space were used to match Kansei values for user needs to Kansei values for new designs. Then the two results were compared. Application process and procedure of the proposed method were described by a case of numerical control machine tools form design. The LSA KE approach was used to match Kansei values for user needs to numerical control machine tools designs for each user. The case study results reveal that the LSA KE approach can significantly improve matching accuracy.

Published: 01 February 2016
CLC:  TH 166  
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LIU Zheng hong, XIE Qing sheng, LI Shao bo, LIN Li. User needs matching based on latent semantic analysis and kansei engineering. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2016, 50(2): 224-233.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2016.02.005     OR     http://www.zjujournals.com/eng/Y2016/V50/I2/224


基于潜在语义分析和感性工学的用户需求匹配

为了更加精确地满足用户需求并提高设计精度,结合潜在语义分析（LSA）和感性工学（KE）,提出基于潜在语义分析和感性工学（LSA KE）的用户需求匹配方法.通过构建RKE模型,获得感性词汇对和设计元素权重；使用RKE模型构建LSA KE语义空间和计算设计方案的感性值,通过用户调查获得用户需求感性值；使用感性工学和LSA KE语义空间匹配用户需求和设计方案,并与匹配结果进行对比.以机床造型设计为案例对该方法进行描述,结果表明该方法能明显提高用户匹配精度.

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