Kansei Analysis Research Articles

The Importance of Users’ Emotional Factors Related to Design of E-Learning Interface Using Kansei Analysis

E-Learning is a popular information system used to facilitate learning activities without borders of time and place. Students, as users, play an important role in determining the design of an e-Learning system; incorporating the users’ emotional requirements into e-Learning development is therefore crucial. A user interface is a critical software component that acts as a bridge to facilitate interaction between users and the system. The design of the user interface must consider the users’ emotions in order to create a positive experience during the running of a software system. Kansei engineering is a methodology utilized to analyze users’ feelings towards the software user interface. The goals of this research are to implement Kansei engineering with coefficient correlation analysis in order to analyze and explore students’ emotional experiences in higher learning institutions and to identify the emotional factors related to the appearance of an e-Learning interface based on students’ learning experiences. Most e-Learning is considered based on its functional aspect, but this paper attempts to explore e-Learning based on its emotional aspect by discovering the relationship between the user interface and students’ emotional experience in e-Learning. The results found that Kansei was “excellent” for exploring students’ most critical emotional factors, which could have a significant impact on designing and implementing an e-Learning system using open-source software. The result could also be used as a recommendation for educational institutions when selecting an open-source learning management system as a platform for e-Learning.

Kansei Analysis about 3DCG Design of Car Front Grill for Thais

Kansei Analysis about 3DCG Design of Car Front Grill for Thais

Kansei Analysis of Car Front Grill to Design for Thai People

Kansei Analysis of Car Front Grill to Design for Thai People

Product Innovation Design Based on Deep Learning and Kansei Engineering

Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei engineering with back-propagation neural networks to establish a mapping model between product properties and styles. To address the inspiration issue in product innovation, the convolutional neural network-based neural style transfer is adopted to reconstruct and merge color and pattern features of the style image, which are then migrated to the target product. The generated new product image can not only preserve the shape of the target product but also have the features of the style image. The Kansei analysis shows that the semantics of the new product have been enhanced on the basis of the target product, which means that the new product design can better meet the needs of users. Finally, implementation of this proposed method is demonstrated in detail through a case study of female coat design.

Tensor SOMによる感性の評価者・評価対象・評価語の同時分析

Tensor SOMによる感性の評価者・評価対象・評価語の同時分析

「大きい－小さい」「速い－遅い」に基づいたペット型ロボットの動作とその感性分析

「大きい－小さい」「速い－遅い」に基づいたペット型ロボットの動作とその感性分析

Study on the Application of Kansei Engineering in Product Form Design

Design mode of product form is constructed combining Kansei Engineering (KE) with Ergonomics. KE is used as main technique to transform consumers' feelings and images of shape, size, material, operability of product into design form futures. The main factors and problems of Kansei experiment and Kansei analysis are also identified.Ergonomics acts as the assisting technique for meeting physiological criteria of consumer group. An implement of a product form design was conducted, and then the feasibility of proposed design mode based on KE is discussed which provides related products’ reference to designers.

Kansei Analysis of the Japanese Residential Garden and Development of a Low-Cost Virtual Reality Kansei Engineering System for Gardens

Residential garden design using Kansei engineering is a challenging problem. Landscaping components, such as rocks, trees, and ponds, are widely diversified and have a large number of possible arrangements. This large number of design alternatives makes conventional analyses, such as linear regression and its variations like Quantification Theory Type I (QT1), inapplicable for analyzing the relationships between design elements and the Kansei evaluation. We applied a partial least squares (PLS) model that effectively deals with a large number of predictor variables. The multiple correlation coefficient of the PLS analysis was much higher than that of the QT1 analysis. The results of the analyses were used to create a low-cost virtual reality Kansei engineering system that permits visualization of garden designs corresponding to selected Kansei words. To render complex garden scenes, we developed an original 3D computation and rendering library built on Java. The garden is shown in public-view style with stereo 3D graphic projection. The rendering is scalable from low to high resolution and enables drop object shadowing, which is indispensable for considering the effect of daytime changes in insolation. Visualizing the garden design based on Kansei analysis could facilitate collaboration between the designer and customer in the design process.

An e‐commerce site for gift flower arrangements that fit kansei and social manners

PurposeGift flowers should be chosen to depict a message with the sender's kansei and are bound by nature of flowers and social manners, to maintain social relationship between the sender and the recipient. Few buyers, but most florists, have expert knowledge of the flowering time, scent, price, and nature of each flower, and are experts in arranging flowers that meet a given purpose. The purpose of this paper is to incorporate handling constraints into the inference process of a kansei engineering system.Design/methodology/approachThe paper collected the expert knowledge concerning nature of flowers, composing flower arrangements and social manners on gifts from specialists of flower arrangements including a florist and special books. At the same time, kansei evaluation experiments on the kinds of flowers and colors were conducted. The expertise and the results of kansei experiments were organized into a flower database and inference rules for choice of a main flower, arrangement shapes and combination flowers. The rules were implemented as server‐side programs. Users input information about the recipient, purpose of the arrangement and purchase information using a web browser. The system outputs a solution; a list of main flowers, combination flowers, greens and the shape of arrangement.FindingsTraditional kansei engineering studies revealed the relationships between design elements and kansei with developing new analyzing methods. Different constraints come into the actual product design and manufacturing should be integrated with findings obtained from the kansei evaluation to successfully utilize kansei engineering for product development.Practical implicationsThe inference rules will be able to tell the reasons for choosing the main‐ and combination flowers and arrangement shapes to satisfy the customers.Originality/valueThe proposed system suggests the original arrangement of flowers unlike most online florists selling ready‐made arrangements. The paper shows a solution to incorporate different constraints underlying in a real production process into the inference process based on the result of kansei analysis.

Kansei and human experience analysis for mobile robot navigation in a ubiquitous environment

Researchers are trying to construct an intelligent system with the help of advance in technology. Many successful milestones have been passed in achieving “humanoid robots” that are little closer to humans, but still not very good. Although the expectation level for the intelligence of these new systems is still unknown, it can be fairly assumed that reaching a goal at least up to the intelligence level of a human being may be a big success. In order to be closer to a human, it is necessary to analyze the behavioral patterns of humans in order to apply those to other artificial intelligence machines. Humans can be described as creatures of emotion. They have feelings toward themselves as well as towards others. These affect all the dealings they perform. Is there anything these feelings can do to robots? Will there be any improvements to robot by having them? This research project discusses the possibility of applying “feelings” to mobile robots in ubiquitous environments.

Reduction of Attribute Values for Kansei Representation

In this paper, we discuss attribute-value reduction for Kansei analysis using information granulation. In the traditional “reduction” sense, the goal is to find the smallest number of attributes enabling us to discern each tuple or each decision class. Once we focus on the number of attribute values, i.e., the size and/or resolution of each attribute domain, another criterion is needed. We must ask ourselves which is better: 1) discerning them with a single attribute described in detail, or 2) discerning them with a few attributes described roughly. Our study answers this question. If we evaluate this difference in the light of understandability or of Kansei representation, we may prefer working with a few attributes described roughly because they yield simpler descriptions. To do this, we propose a criterion and an algorithm to find near-optimal solutions for the criterion, detailing results for databases in the UCI Machine Learning Repository.

Special Issue on Rough Sets and Granular Computing

Rough sets and granular computing, known as new methodologies for computing technology, are now attracting great interest of researchers. This special issue presents 12 articles, and most of them were presented at the second Japanese workshop on Rough Sets held at Kyushu Institute of Technology in Tobata, Kitakyushu, Japan, on August 17-18, 2005. The first article studies the relation between rough set theory and formal concept analysis. These two frameworks are analyzed and connected by using the method of morphism. The second article introduces object-oriented paradigm into rough set theory, and object-oriented rough set models are proposed. Theoretical aspects of these new models are also examined. The third article considers relations between generalized rough sets, topologies and modal logics, and some topological properties of rough sets induced by equivalence relations are presented. The fourth article focuses on a family of polymodal systems, and theoretical aspects of these systems, like the completeness, are investigated. By means of combining polymodal logic concept and rough set theory, a new framework named multi-rough sets is established. The fifth article focuses on the information incompleteness in fuzzy relational models, and a generalized possibility-based fuzzy relational model is proposed. The sixth article presents a developed software EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) and the application of this software to pipeline valve control. The seventh article presents the properties of attribute reduction in variable precision rough set models. Ten kinds of meaningful reducts are newly proposed, and hierarchical relations in these reducts are examined. The eighth article proposes attribute-value reduction for Kansei analysis using information granulation, and illustrative results for some databases in UCI Machine Learning Repository are presented. The ninth article investigates cluster analysis for data with errors tolerance. Two new clustering algorithms, which are based on the entropy regularized fuzzy c-means, are proposed. The tenth article applies binary decision trees to handwritten Japanese Kanji recognition. The consideration to the experimental results of real Kanji data is also presented. The eleventh article applies a rough sets based method to analysing the character of the screen-design in every web site. The obtained character gives us good knowledge to generate a new web site. The last article focuses on rule generation in non-deterministic information systems. For generating minimal certain rules, discernibility functions are introduced. A new algorithm is also proposed for handling every discernibility function. Finally, we would like to acknowledge all the authors for their efforts and contributions. We are very grateful to reviewers for their thorough and on-time reviews, too. We are also grateful to Prof. Toshio Fukuda and Prof. Kaoru Hirota, Editors-in-Chief of JACIII, for inviting us to serve as Guest Editors of this Journal, and to Mr. Uchino and Mr. Ohmori of Fuji Technology Press for their kind assistance in publication of this special issue.

1/fn Fluctuating Phenomena in the Hue of Lignt Emission Pattern of Firefly and Its Healing Effect

The fantastic light of firefly, which keeps fascinating the heart of Japanese from ancient time, and the ecosystem are taken up as one of cure fields being benefit from the nature. In this study, from a viewpoint of Kansei engineering, we focused on the hue of light emission pattern of firefly, and we examined whether it causes the human spirit any effects or not by using various statistical analysis and Kansei analysis. It was revealed that there is a high possibility that the sufficient cure effect exists in the hue of light emission pattern of firefly and the ecosystem. This research is the first basic trial turned to the creation of cure spaces for hospice and welfare facility, which utilize the firefly and the mini ecosystem artificially modeled in an enclosure.

Kansei Estimation on Luminescence of Aurora

The beautiful and magnificent light of aurora is one of the most wonderful phenomena among natural resources that we can, observe. In this study, from the viewpoints of Kansei engineering, we focused on the hue of light of aurora, and we examined whether it causes the human spirit any effects or not by using various statistical analysis and Kansei analysis. It was revealed that there is a high possibility that the sufficient cure effect exists in the light of aurora. This research is the first basic trial turned to the creation of cure spaces for hospice and welfare facility. We also aim at developing a cure equipment by applying the light of aurora to hospice and medical facilities in future.

NON-LINEAR DATA ANALYSIS ON KANSEI ENGINEERING AND DESIGN EVALUATION BY GENETIC ALGORITHM

This paper discusses non-linearity in KANSEI evaluation data and attempts to better understand non-linear relationships between design attributes. Conventionally, the basis of KANSEI analysis has been multivariate analyses and linear modeling. These methods have proven unreliable and problematic. Non-linearity may be typically represented by the interaction between design elements, which often is represented as multicollinearity. To better understand such interactions, a method is proposed that expresses the effect of combining multiple design elements, and uses 'f-then' rules in conjunction with decision trees. The method is also described for constructing these decision trees by using genetic algorithms (GA). Our concern is to consider the adequateness of the proposed method for analyses of non-linear KANSEI data. Two actual examples are shown in order to investigate this problem. An example of KANSEI experiment on interior automobile space is used to examine divided partitions as tree representations. Another illustration of canned coffee design shows a comparison between the results of our method and quantification type I. We can conclude from these examples such that the proposed method indicates in more detailed rules and a deeper understanding of information extracted from KANSEI evaluation data.

Detecting Non-Linear Relations between Variables of Kansei Evaluation Data

Kansei Engineering is advanced method for analyzing the relationship between design elements and human images at the designing production design. Nonlinear relationships among evaluation data used by Kansei Engineering system are discussed in this paper for the purpose to consider the distribution of Kansei data. The analysis of real products extract the nonlinearity in the data of SD scaled questionnaire. In especially, treating combination effects among design elements are very difficult problem to solve in kansei analysis. Then we discuss the analysis the combination effects at kansei evaluation data.

Rule-based inference model for the Kansei Engineering System

Kansei Engineering has been applied to product development for customer satisfaction based on ergonomic technology. The system is composed of three parts such as Kansei analysis, inference mechanism, and presentation technologies. The inference mechanism by which human Kansei is translated into design elements plays an important role in Kansei Engineering. The reasoning logic in the system must satisfy several conditions. First, the whole aspects of design elements must be considered in the reasoning processes. Second, the reasoning logic can make the discrimination between Kansei words selected by customers and other words. Third, the reasoned results must have reliability high enough to reduce the difference between customer's image and reasoned design elements. In this paper, we propose a rule-based inference model which will cover the above-mentioned conditions. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected Kansei words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model. Relevance to industry Rule-based inference model in useful in product design development having complex relationship with human Kansei of any industry. This model can also be made available to evaluation method for the analysis of reasoned results.