Kansei Quantitative Analysis and its Application to Drum Washing Machine Apperence Design

Article Preview

Abstract:

Kansei Engineering (KE) refers to the translation of consumers' emotional requirements about a product into perceptual design elements. The Kansei Engineering is applied to the drum washing machine aided by a variety of engineering mean.Semantic differential (SD) is applied to extract the kansei tags.Multivariate statistical analysis method is also used for data mining.The data of design elements is processed by principal component analysis (PCA) and SPSS.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

193-196

Citation:

Online since:

September 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Hung-Yuan Chen, Yu-Ming Chang. Extraction of product form features critical to determining consumers' perceptions of product image using a numerical definition-based systematic approach[J]. International Journal of Industrial Ergonomics39(1): 133-145.

DOI: 10.1016/j.ergon.2008.04.007

Google Scholar

[2] Nagamachi,M. (1989). Kansei engineering. Tokyo: Kaibundo Publisher.

Google Scholar

[3] Nagamachi,M. (1996). Introduction of Kansei engineering. Tokyo: JapanStandard Association.

Google Scholar

[4] Osgood, C.E., Suci, C.J., Tannenbaum, P.H., 1957. The Measurement of Meaning. University of Illinois Press, Urbana, pp.76-124.

Google Scholar

[5] Yuexiang Huang, Chun-Hsien Chen, Li Pheng Khoo. Products classification in emotional design using a basic-emotion based semantic differential method[J]. International Journal of IndustrialErgonomics42(6): 569-580.

DOI: 10.1016/j.ergon.2012.09.002

Google Scholar

[6] Huang Y., Chen C.H., Khoo, L.P., 2012. Kansei clustering for emotional design using a combined design structure matrix. International Journal of Industrial Ergonomics 42 (5), 416-427.

DOI: 10.1016/j.ergon.2012.05.003

Google Scholar

[7] Shang H Hsua, Ming C Chuangb, Chien C Changa. A semantic differential study of designers' and users' product form perception[J]. International Journal of Industrial Ergonomics 25(4): 375-391.

DOI: 10.1016/s0169-8141(99)00026-8

Google Scholar

[8] Torralba, Antonio B, LIS-INPG, Grenoble, France Oliva, Aude. Semantic organization of scenes using discriminant structural templates[J]. IEEE International Conference 2(1999)(1253-1258).

DOI: 10.1109/iccv.1999.790424

Google Scholar

[9] Huiwen Wang, Rong Guan, Junjie Wu. CIPCA: Complete-Information-based Principal Component Analysis for interval-valued data[J]. Neuro computing86(1): 158-169.

DOI: 10.1016/j.neucom.2012.01.018

Google Scholar

[10] Bian, Wei Tao, Da-Cheng. Max-min distance analysis by using sequential SDP relaxation for dimension reduction[J]. IEEE Transactions Pattern Analysis and Machine Intelligence: 33 (5)(2011) 1037–1050.

DOI: 10.1109/tpami.2010.189

Google Scholar

[11] I.T. Jolliffe. Principal Component Analysis. new york: springer-verlag, (2002)1-5.

Google Scholar

[12] Dr. Nancy L. Leech, Dr. Karen Caplovitz Barrett, George A. Morgan, Jr. Spss For Intermediate Statistics: Use And Interpretation. New Jersey, LAWRENCE ERLBAUM ASSOCIATES, (2009)88-92.

Google Scholar