Hybrid kansei engineering system and design support

Symbiosis of Human and Artifact Y. Anzai, K. Ogawa and H. Mori (Editors) © 1995 Elsevier Science B.V. All rights reserved.

161

Hybrid Kansei Engineering System and Design Support Yukihiro MATSUBARA a and Mitsuo NAGAMACHI a a Faculty of Engineering, Hiroshima University 1-4-1, Kagamiyama, Higashi-Hiroshima 724, JAPAN Kansei Engineering is defined as "translating technology of a consumer's feeling and image for a product into design elements" (Nagamachi, 1989). There are two type of the Kansei Engineering System (KES), one for the consumer decision supporting system called the Forward KES, and another for designer supporting system called the Backward KES. The combined computerized system of the Forward KES and Backward KES must be the powerful supporting tools for both users. This paper introduces the structure of the combined system, and proposes the Hybrid KES as the new general framework of Kansei Engineering System.

1. INTRODUCTION Kansei Engineering (KE) is effective technique to translate the human Kansei (consumer's feeling and desire for the domain product) into the product design element (Nagamachi, 1989). Recently, this technique is noticed in many fields of product development department. In actually, the concept of KE is referred in many phase of product designing process. Furthermore, Kansei Engineering System (KES) takes up the role of function for interface between the product designer and consumer. On the other hands, there are two kinds of technique for Kansei Engineering. Those are (1) forward inference type of KE (from Kansei to design element) and (2) backward inference type of KE (from candidate design to diagnosed Kansei) (Nagamachi, 1995). Implementing these inference algorithm on the computer system, we can get the KES as the expert system based on the Artificial Intelligence technology (Matsubara, et.al, 1994a). In general, the forward inference type of KES (called Forward KES) is utilized to support the consumer's decision of selecting the desired product. On the other hands, the backward inference type of KES (called Backward KES) is utilized to support the designer's creative work diagnosing the Kansei about the designer's rough sketch. In this view, we proposed the intelligent image processing mechanism which utilized on the KES, the system can recognize the designer's idea as the combination of design element (Matsubara, et.al, 1994b). Furthermore, the combined computerized system of the Forward and Backward KES would be more powerful supporting tool both consumer and designer. In this paper, we propose the Hybrid KES as the new general framework of KES, which is combined Forward KES and Backward KES. At first, we describe the function of user supporting and show the concept of Hybrid KES. Secondly, we propose the Kansei inference model which is based on linear regression model. Thirdly, we show the detail description of the Hybrid KES structure and design recognition function. Finally, we

162 construct the prototype system for the Hybrid KES as the domain for "housing front door", and conclude by estimating the system. 2. K A N S E I

ENGINEERING

AND

DECISION SUPPORTING

Hybrid KES consists of Forward KES and Backward KES. Figure 1 shows the diagram of the Hybrid KES. The former is the KES in which a designer obtains the demanded design through an input of the Kansci word. In the Backward KES, the designer is able to draw a rough sketch in the computer and the computer system recognizes the pattern of the design inputted by the designer. Then the system estimates the Kansei or image of the design inputted through the backward infercnce cnginc and shows the cstimated level of Kansei about the design.

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Figure 1. The diagram of Hybrid KES 3.KANSEI

INFERENCE

MODEL

We proposed some Kansci model which are based on both linear and non-linear model to make the relationship between the human Kansci and product design element (Ishihara, ct.al., 1995) (Tsuchiya, ct.al., 1994). In this paper, we assume the linear regression model as the Kansci inference model (s¢¢ Figure 2, 3) and formalized as following equation. This model is the typical linear regression model, and can be analyzed and identified using the Hayashi's Quantification Theory Type I (Hayashi, 1976). This method is one of the categorical multiple regression analysis method, which is the case for as criterion variable is quantitatively and explanatory variable is qualitatively (which means as categorical parameter). At first, we define the dummy variable 6 ,(jk) as follows. 5i(jk) = i • when a sample i corresponds to item j and category k

(1)

5i(jk) = 0 • otherwise where; i=l,...,n (n: number of samples), j=I,-..,R (R: number of item),

(1')

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k=I,...,Cj(Cj: number of categoryfor item j), RR= j~ cj Using this dummy variables, suppose the linear regression model for the categorical explanatory variable as equation (2). R q

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(2)

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(3)

Now, doing the partial differentiation for {Q} by each ajk, We can get the RR case of

163 linear equations. Solving these simultaneous equations, we can get the category score a~k shown in equation (4).

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(4)

j=l k=l Now, we assume the Yi°) as the evaluated value of the specific Kansei I (I=1,..-,m; number of Kansei words), we can say that a jk0) indicates the relationship level between the specific Kansei I and the design element corresponded to item j, and category k. ,

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4. HYBRID KES 4.1 Overview Figure 4 shows the system structure of Hybrid KES. This system is consist of four main modules (design processing module, inference module, Kansei word processing module and system controller), and five kinds of DB (design DB, graphic DB, knowledge base, image DB and Kansei word DB). When the user (ether consumer or designer) inputs the Kansei word by natural language, the system tries to identify the Kansei meanings through the Kansei word processing unit referring the Kansei word DB. Then the system infers the candidate design through the forward inference engine referring the knowledge base and image DB. Finally the system outputs and displays the candidate design using CG through the picture drawing module referring the design DB and graphic DB (see Figure

5(a)). On the other hands, when user inputs the combination sets of design element, the system performs the design element recognition module and identifies the design elements as the item and category. If the user inputs the free drawing picture, the system uses the image processing and recognition technique, and can get the identified results (Matsubara, et.al., 1994b). Then the system outputs the diagnosis results which is the Kansei or image concerned with the inputted design through the backward inference engine and explanation processing unit (see Figure 5(b)). In following, we give a explanation for each forward and backward inference engine mechanism in detail.

4.2 Forward Inference Engine Suppose that the user input and request the Kansei 1". On the other hands, we have already gotten the equation (4) concerned with the Kansei 1" and identified each a jk, which are stored in image DB. Then the system can infer the adequate category K* concerned with Kansei 1"performing the following steps for each item.

stEP1. START, j= STEP2" a*jk • = maxk [* a jk]

164 STEP3" item j" select the category k" STEP4" j=R ~ STEP5 otherwise ---, j=j+ 1, STEP2 S T E P 5 : referring the knowledge base (expert knowledge) if needs ; modify the design element adequately (if there are conflict condition between each item and category) S T E P 6 : decide the candidate design, END i :

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Figure 4. The system structure of Hybrid KES 4.3 Backward

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Set the following equation based on the equation (4). R

q

S(1) = j~l e ~ l ) ~ a .(1) jk ~i(jk)+ C (1) =

(5)

k=l

where; ej°) • certainty factor, c °) • constant term If the user inputs the specific candidate design i', the system can identify the each item and category corresponded to input design. As the results, identified 6 "i(jk), then the system can get the S O). =

i

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(6)

When put the e.]0) as equation (6), the system actualize the modell shown in Figure 3(a). If we assume the adequate the certainty factor ej°) (e.g. the specific value which is introduced for referring the Partial Correlation Coefficient (PCC) calculated by Quantification Theory Type 1), we can actualize the model2 shown in Figure 3(b). Furthermore, we can get the S 0r as following equation (7).

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sO). minj, [SO)]

,,_ rna~)j,k [S(1)] - minj,k [S(I)] (7) We define me ~ as the goodness of fit for Kansei 1 corresponded to input candidate design i'. Iterating the above procedure for all 1, we can get the diagnosis result for backward Kansei inference. In this article, the system has four type of inference procedure as the combination of establishment method for e.j 0) and a'.jk (1) as follows. e.0) ~-- 1, a'., (1) ~--normal [1] [2] ~0)~_ 1, a*~.~°)~- standardized . [3] . ~ referring the PCC, a .k°) ~- normal [4] ~0)~_ referdng the PCC, a*]k(l)~-standardized

5.APPLICATION:

Housing Front Door

This article focuses on the housing front door as the domain of prototype system for Hybrid KES. Especially, the housing environment is one of the most important field to express the personal favor and feeling. On the other hands, each product unit is so expensive that it is difficult to change it frequently. Therefore, the customer is requested careful decision to buy the product unit and desires decision support tool to realize and translate the own demand. In this view, the Hybrid KES is very useful tool to support consumer's decision as well as designer's use, and housing front door is good example as the domain. Furthermore, it is easy to carry out recognition because the shape is constituted by a simple part of square shape and circle one.

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(b) Output Figure 6. An example: Forward

5.1 Forward Inference Figure 6 shows a sample run of the expert system for front door designing, which we constructed in this study. This system is implemented on a Macintosh using the programming language C. Running the system, at first a window for adjective selection is displayed as in Figure 6(a) and the user chooses an adjective or more, and the system does the reasoning. Then the system draws a picture as an output, where Figure 6(b) shows. Further after that, the user can change the detail designs of the outputted designs. 5.2 Backward Inference The output example of backward Kansei evaluation result is shown in Figure 7. The recognition result (two kind of pictures and the list of recognized item) is indicated in the upper part of the figure. The reasoning result for the Kansei is shown in the fight lower part, and a menu button to carry out details change is indicated in the left lower part.

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1 2 3 4 5 6 7 8 9 10 11 Design Element / No. of Item Figure 8. The recognition rate for each item

5.3 Design Recognition We collect the 82 patterns design samples which exist in the marketing catalog and estimate the recognition rate for the design element recognition sub-system to try recognizing those samples (Matsubara, et.al., 1994b). The system divides the picture into the door frame level as Hierarchy-i, the shape of crosspiece level as Hierarchy-2, and the texture of lattice level as Hierarchy-3. The each hierarchy's recognition rate are shown in Figure 8. In this result, high hierarchy has good performance to recognize the element. This is the reason why the lower hierarchy received the influence for higher one's recognition error. Therefore, it is able to improve the rate to increase the sufficient recognition rules. 6.CONCLUSION In this paper, we proposed the new concept of Kansei Engineering system, and constructed the Hybrid KES. At first, we described the function of user supporting and showed the concept of Hybrid KES, then proposed the Kansei inference model which is based on linear regression model. Finally, we showed the detail description of the Hybrid KES structure and design recognition function, and constructed the prototype system for the Hybrid KES as the domain for "housing front door". This work is supported in part by the Grand.in-Aid for Scientific Research 05220104 from the Ministry of Education, Science and Culture. We wish to thank Mr. Kouji MIYAZAKI and Kouichi KASI-IIWAGI for their contribution to this study.

References 1 S. Ishihara, K. Ishihara, M. Nagamachi and Y. Matsubara, 1995: An Automatic Builder for a Kansei Engineering Expert System using Self- Organizing Neural Networks, International Journal of Industrial Ergonomics, Vol.15, No.l, 13/24. 2 Y. Matsubara, M. Nagamachi, T. Jindo, 1994a: Kansei Engineering as an Artificial Intelligent System, Proc. of 4th International Conference of the Human Factors in Organizational Design and Management, 473/478. 3 Y. Matsubara and M. Nagamachi, 1994b: An Application of Image Processing Technology in Kansei Engineering, Proc. of 12th Triennial Congress of the International Ergonomics Association, Vol.4, 123/126. 4 M. Nagamachi, 1989 ' Kansei Engineering, Kaibundo Publisher, Tokyo (in Japanese). 5 M. Nagamachi, 1995: Kansei Engineering: A New Ergonomic Consumer-OrientedTechnology for Product Development, International Journal of Industrial Ergonomics, Vo1.15, No.l, 3/11. 6 T. Tsuchiya, Y. Matsubara and M. Nagamachi, 1994: A Fuzzy Rule Induction Method with Genetic Algorithm, Proc. of the Third Pan-Pacific Conference on Occupational Ergonomics, 77/81.