Prelude to &amp;lt;i&amp;gt;Natphoric Kansei &amp;lt;/i&amp;gt; Engineering Framework

Lokman, Anitawati Mohd; Haron, Mohammad Bakri Che; Abidin, Siti Zaleha Zainal; Khalid, Norlin; Ishihara, Shigekazu

doi:10.4236/jsea.2013.612076

Cited by 21 publications

(7 citation statements)

References 27 publications

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Section: Kansei Engineeringmentioning

confidence: 99%

“…Finally, researchers use intelligent algorithms to train and optimize models to guide subsequent design. These methods include deep learning [31], swarm intelligence algorithms [32], genetic algorithms [33], and a combination of multiple optimization methods [34,35]. Therefore, the KE method is used to collect case samples and image vocabulary to form an initial sample set and an initial image set in this paper.…”

Section: Kansei Engineeringmentioning

confidence: 99%

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Evaluation and Balance of Cognitive Friction: Evaluation of Product Target Image Form Combining Entropy and Game Theory

Qiu

Zhang

et al. 2020

Symmetry

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With the great progress of product development technology, product forms have been greatly enriched by cognitive differences; users and designers have formed a “cognitive friction” phenomenon in the product evaluation process, which results in designers being unable to grasp user emotions accurately and risks of product development failure. This paper aims to balance the cognitive differences between cognitive subjects (users and designers) and evaluates the product image form. First, image entropy is used to evaluate and extract the weight of the product target image. Second, fuzzy Theil entropy is used to evaluate the cognitive friction between cognitive subjects, and its existence and size are visually presented. Then, a cognitive friction balance model is built by combining game theory, the comprehensive evaluation weight between cognitive subjects is obtained, and the product image form is ranked and optimized. Finally, all the research steps are described in the form of a household hair dryer. The results show that fuzzy Theil entropy and game theory have significant advantages in the evaluation and balance of cognitive friction in product design. Thus, the cognitive friction evaluation and balance model constructed from the fuzzy Theil entropy and game theory do not only enable different cognitive subjects to achieve cognitive symmetry, but also screen out product forms that meet the cognitive needs of users. This finding provides the theoretical basis and practical significance for the establishment of a closed-loop model in cognitive friction balance and the reduction of cognitive differences between cognitive subjects in the entire process of product design. It also introduces new ways of thinking and methods for cognitive science research.

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Section: Kansei Engineeringmentioning

confidence: 99%

Section: Kansei Engineeringmentioning

confidence: 99%

Evaluation and Balance of Cognitive Friction: Evaluation of Product Target Image Form Combining Entropy and Game Theory

Qiu

Zhang

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…This study adopts method of Kansei Engineering Type I (KEPack) due to its simplicity and wide use in many product developments [2,6,10,19,20,22,23]. Figure 3 shows the systematic processes of KEPack.…”

Section: Kepackmentioning

confidence: 99%

“…Other studies of Kansei engineering in e-Learning can be found in [19] which reports about Kansei semantic space in online database courseware systems and [20] which reports a theoretical framework of playful interaction in mobile learning system. Chen [21], Hussin and Lokman [22], and Lokman [23] are other studies of Kansei engineering to design an interface of web-based system development.…”

Section: Introductionmentioning

confidence: 99%

Analysis Learners’ Preference in E-Learning System Using Kansei Approach

Hadiana¹

2018

Trends in E-Learning

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Academic institutions have opportunity to provide better learning environment over the Internet using open source web-based e-Learning systems. One of the important issues is how to choose a suitable e-Learning system that can meet what learners need implicitly. It lacks guide to support academic institutions in determining a proper e-Learning system based on learners' needs; it becomes the reason of the implementation of e-Learning that cannot work for long time. This chapter gives an information of result observation regarding learners' psychological aspects toward e-Learning system using Kansei Engineering and its correlation with the e-Learning interface design. Analyzing learner's preference related to e-Learning system, it can provide the kind of desired e-Learning system to enhance learners' experience; finally, it can help the academic institutions to implement e-Learning system continually for a long time.

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“…Thus, this study used the theory of Kansei engineering and the multidimensional scaling algorithm to study subjects' "perception" cognition rules of products [6,7,8]. Furthermore, we discussed the perception quantification method of product form and affirmed the relation between "perception" and product design, with the establishment of the effective mapping relation between perceptual words and design elements.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Research on Product Form Based on Kansei Engineering

Li¹

2018

JESTR

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In mature market competition, the evaluations of users on product design are significantly dependent on the perception of product forms. In order to study the mapping relationship between product form and perceptual image, a quantification algorithm model was proposed. Initially, samples and perceptual image word pairs were collected and selected on basis of helmet products. A score was given to each sample image by subjects, and the initial data were obtained. Subsequently, the data were pre-processed using the Leida criterion for rejecting the abnormal perceptual image. The samples were grouped into 12 categories by clustering analysis. Finally, a multidimensional scaling and linear regression analysis were utilized to generate the joint space map of perceptual attribute vectors and sample scattered points. The attribute value of the sample was obtained by calculating the distances from the scattered points to the projection points of the perceptual vector and the origin. Thereafter, the correspondence between the sample and perceptual words was obtained. Results demonstrate that the perceptual characteristics of words are more evident for the absolute attribute value larger than 2, rather than the absolute value less than 0.1. The larger absolute attribute value shows that the sample form has more perceptual characteristics of the vector. The study provides an effective method for quantifying the perceptual characteristics of product form.

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Prelude to <i>Natphoric Kansei </i> Engineering Framework

Cited by 21 publications

References 27 publications

Evaluation and Balance of Cognitive Friction: Evaluation of Product Target Image Form Combining Entropy and Game Theory

Evaluation and Balance of Cognitive Friction: Evaluation of Product Target Image Form Combining Entropy and Game Theory

Analysis Learners’ Preference in E-Learning System Using Kansei Approach

Quantitative Research on Product Form Based on Kansei Engineering

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