| Project/Area Number |
09838028
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
感性工学
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
ICHIHASHI Hidetomo Osaka Prefecture University, Department of Industrial Engineering, Professor, 工学部, 教授 (30151476)
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| Co-Investigator(Kenkyū-buntansha) |
MIYOSHI Tetsuya Osaka Prefecture University, Department of Industrial Engineering, Associate Researcher, 工学部, 助手 (10254434)
NAGASAKA Kazunori Osaka Prefecture University, Department of Industrial Engineering, Assistant Professor, 工学部, 講師 (90081405)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,600,000)
Fiscal Year 1999: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1998: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1997: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Kansei Engineering / Kansei word / Quantification analysis / Projection pursuit regression / Decision tree / Spline function / Pairwise comparison / 数量化理論一類 |
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| Research Abstract |
Image Technology or Kansei Engineering is defined as a translation system of a consumer's image or feeling into real design components. In this research we proposed some methods to analyze consumer's image or feeling for objects or products with respect to "KANSEI word". We developed the collecting. System of data through the Internet and evaluated the efficiency of the proposed methods. Our proposed analyzing methods and their properties are as follows.
(1) We proposed projection pursuit regression using pairwise comparison data in the similar way to Guttman's Quantifying method of pairwise comparisons. We collected the data about feeling and emotion, and analyzed the relation between design components of objects with respect to the adjective words "conspicuous" and "quiet" using proposed method.
(2) We proposed the convenient fuzzy clustering algorithm using pseudo Mahalanobis distances and maximizing entropy approach, since discreminant analysis with Mahalanobis distances has been eff
… More
iciently applied to various pattern recognition problems.
(3) We proposed an approach to analyze the psychological feeling of consumer in which the Fuzzy c-Means clustering algorithm and correspondence analysis are simultaneously applied. In our clustering algorithm, membership to clusters are determined by considering not only the minimization of distances from cluster centers but also the maximization of correlation ratio between numerical values which are assigned to each category and individual.
(4) Strongly nonlinear multi-variate functions hardly bring about insights about the local relationships between inputs and outputs. For the analysis of psychological feelings a weakly nonlinear version of Fuzzy c-Regression Models is applied. The data in each fuzzy cluster is projected in a single dimensional space and latent local weakly nonlinear relationships between independent observations and their corresponding dependent observations can be found.
(5) As an empirical experiment, we dealt with "up-to-date" feeling of CI (Corporate Identity) symbol marks, which has similar pattern to Japanese national flag whose design components are the locations of circles relieved in white, and analyzed the impression for the design using the proposed method. Less
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