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Basic Study of Psychological Analysis about "KANSEI Word"

“感性词”心理分析基础研究

基本信息

批准号：
09838028
负责人：
ICHIHASHI Hidetomo
金额：
$ 1.66万
依托单位：
Osaka Prefecture University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1999
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-09838028/
关键词：
Kansei Engineering Kansei word Quantification analysis Projection pursuit regression Decision tree Spline function Pairwise comparison 数量化理論一類

项目摘要

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

形象技术或感性工程学被定义为将消费者的形象或感觉转化为实际设计组件的翻译系统。在这项研究中，我们提出了一些方法来分析消费者对“感性词”的形象或对物体或产品的感觉。我们发展了收集。系统的数据通过Internet进行评价，提出了效率评价的方法。我们提出的分析方法及其性质如下。(1)采用与Guttman的两两比较量化方法类似的方法，提出了利用两两比较数据的投影寻踪回归。我们收集了关于感觉和情绪的数据，并利用所提出的方法分析了物体的设计成分与形容词“显眼”和“安静”之间的关系。(2)由于基于马氏距离的判别分析已经被有效地应用于各种模式识别问题，我们提出了基于伪马氏距离和最大熵方法的方便的模糊聚类算法。(3)提出了一种将模糊c均值聚类算法与对应分析相结合的消费者心理感受分析方法。在我们的聚类算法中，不仅要考虑到到聚类中心的距离的最小化，而且要考虑到分配给每个类别和个体的数值之间的相关比率的最大化，从而确定聚类的隶属度。(4)强非线性多变量函数难以揭示输入与输出之间的局部关系。对于心理感受的分析，采用了弱非线性模糊c回归模型。每个模糊聚类中的数据被投影到一个单维空间中，可以发现独立观测值与其对应的依赖观测值之间潜在的局部弱非线性关系。(5)作为实证实验，我们处理了与日本国旗图案相似的CI （Corporate Identity）符号标志的“最新”感觉，其设计成分为白色浮雕圆的位置，并使用所提出的方法分析了对设计的印象。少