DocumentCode :
263399
Title :
Ontology-Based Feature Modeling and Combination for Kansei Engineering
Author :
Qianru Qiu ; Hongming Cai ; Lihong Jiang ; Omura, K.
Author_Institution :
Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2014
fDate :
12-14 July 2014
Firstpage :
26
Lastpage :
32
Abstract :
Nowadays consumers´ affective responses have become more and more helpful for developing successful products. Kansei Engineering (KE) is a challenging customer-oriented technology for product development in the intelligent age. It should not only translate the humans´ feeling into design specifications, but also share classified design knowledge and integrate design features to get a new design scheme automatically. In this paper, an ontology-based systematic approach is proposed to establish a Kansei Engineering System (KES). The approach is based on a hierarchical ontology model and a feature combination mechanism. In our ontology model, design elements and items are divided into several hierarchies according to the expertise and the results of statistical analysis. The source data is from Kansei Evaluation Experiment. The classification of design features is used to decide the priority during feature selection and combination. In the present study, the hierarchical model is applied to establish an intelligent system for card design to demonstrate the effectiveness of the proposed approach.
Keywords :
consumer behaviour; design engineering; emotion recognition; feature selection; human factors; ontologies (artificial intelligence); product design; product development; production engineering computing; statistical analysis; KES; Kansei engineering system; Kansei evaluation experiment; card design; customer-oriented technology; design elements; design feature classification; feature combination mechanism; feature selection; hierarchical ontology model; intelligent system; ontology-based feature modeling; ontology-based systematic approach; product development; source data; statistical analysis; Cognition; Databases; Intelligent systems; Knowledge acquisition; Ontologies; Semantics; affective design; feature combination; kansei engineering; ontology modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Ubi-Media Computing and Workshops (UMEDIA), 2014 7th International Conference on
Conference_Location :
Ulaanbaatar
Print_ISBN :
978-1-4799-4267-1
Type :
conf
DOI :
10.1109/U-MEDIA.2014.50
Filename :
6916320
Link To Document :
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