Journal of Software Engineering and Applications, 2013, 6, 638-644
Published Online December 2013 (
Open Access JSEA
Prelude to Natphoric Kansei Engineering Framework
Anitawati Mohd Lokman1, Mohammad Bakri Che Haron1, Siti Zaleha Zainal Abidin1,
Noor Elaiza Abd Khalid1, Shigekazu Ishihara2
1Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia; 2Faculty of Psychological
Science, Hiroshima International University, Hiroshima, Japan.
Received April 18th, 2013; revised May 18th, 2013; accepted May 26th, 2013
Copyright © 2013 Anitawati Mohd Lokman et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In accordance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the
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Consumers’ emotion has become imperative in product design. In affective design field, Kansei Engineering (KE) has
been recognized as a technology that enables discovery of consumer’s emotion and formulation of guide to design
products that win consumers in the competitive market. Albeit powerful technology, there is no rule of thumb in its
analysis and interpretation process. KE expertise is required to determine sets of related Kansei and the sign ificant con-
cept of emotion. Many research endeavours become handicapped with the limited number of available and accessible
KE experts. This work is performed to simulate the role of experts with the use of Natphoric algorit hm an d thus provides
solution to the complexity and flexibility in KE. The algorithm is designed to learn the process by implementing train-
ing datasets taken from previous KE research works. A framework for automated KE is then designed to realize the
development of automated KE system.
Keywords: Kansei Engineering; Ant Colony Clustering; Natphoric Algorithm; Computer-Aided KE
1. Introduction
Advancement in production technology and market re-
search has flooded the market with many products with
similar design, functions as well as usability. For manu-
facturers, this means that they are competing in a highly
competitive market. They need to make their products
appealing to customers so that the production costs can
be turned into profits [1]. To compete in th is competitive
environment, it is very important for the product design-
ers to take into consideration the consumer’s impression
on their product.
Two methods of product development process are
“product-out” and “market-in” philosophies [2]. “Prod-
uct-out” philosophy or strategy takes place when a prod-
uct is developed for the market based on the needs of the
society. Designers mainly focus on the functional needs,
performance and usability. However, this rarely empow-
ers a competitive edge because competitors are quick to
catch up [3]. The other way of product development pro-
cess is “market-in” philosophy. This approach is based
on what consumers want, need and their emotional feel-
ings [4]. This kind of strategy attracts them to purchase
the product. In 1970’s, the “product-out” strategy is very
successful because the society hungers for the latest tech-
nology are sparse. However, in technology saturated
market, society has a variety of product selections, thus
shifting their needs to prefering products or goods that
have affective and exciting ele ments which provid e value
beyond the function al quality [4].
Nagamachi developed a technique called Kansei En-
gineering (KE) as a method to investigate consumers’
psychological feelings while interacting with a product
and identifying the relationship among these feelings
with product characteristics [5]. Research done by [6]
found out that many companies are skeptical about the
validity of KE results as its main process is hidden by KE
designers. Meanwhile, they often complained that they
do not have in-house expertise and have to employ ex-
perts for consultations. KE was founded in Japan and
many experts are from there. In Japan, many actual ex-
amples have been developed and have emerged world-
wide. The founder himself stated that companies all
Prelude to Natphoric Kansei Engineering Framework 639
over the world had come over all the way to Hiroshima
to discuss the actual implementations [7]. Due to this
problem, there are countless demands for computer-
aided KE. But, not much attempt has been made to de-
velop such system [8,9].
In one of the phases in the KE process, which is the
Factor Analysis process, experts are required to find sig-
nificant factor of emotion from the data obtained. This
information is required in order to determine the con cept
of emotion in the product design. In order to simulate
how the experts find the significant factor of emotions,
Natphoric algorithm will be used. The Natphoric algo-
rithm will be able to learn how the process is done by
training it with a set of training data collected from pre-
vious KE research works.
To realize the development of computer-aided KE
system, a framework for automated KE will be designed.
Suitable Natphoric algorithm will be incorporated into
the framework as a suitable development tool for the
complex and flexible requirement of KE. The framework
includes step-by-step technique of the use of KE Type 1
and will automate the word classification process that
normally requires KE expert.
2. Kansei Engineering
According to [10], Kansei is an individual subjective
impression from certain artifact, environment or situation
using all natural human senses such as sight, hearing,
smell, taste and balance as well as recognition. For ex-
ample, customers who want to purchase a product or
service will invoke emotional desire such as “elegant,
feminine and inexpensive” [11].
Kansei is an internal sensation, but at present, can only
be quantified using methods based on externalization.
Therefore, [12] developed a series of standard measure-
ment methods. The most common method of measuring
Kansei is through classifying and quantifying meaning in
words used to describe and differentiate each psycho-
logical and emotional need [13]. This reflects a person’s
mind and act as an external description for each elements
of Kansei [13].
KE is a product development methodology which
translates customers’ impressions, emotions, feelings and
demands of existing product or concepts into concrete
design parameters [14]. This methodology integrates
effective elements that are already presented during the
development process [10].
It is important for manufacturers to also satisfy cus-
tomers’ psychological needs in addition to the product
physical qualities which are defined objectively [15-18].
The KE objective is to develop products that satisfy the
individual physical, psychological and emotional needs
[2]. This will create a sense of endearment with the pro-
duct [19,20].
For example, before purchasing a car, an individual
will imagine a car with “beautiful and premium exterior”,
“powerful engine”, “easy operation”, “cool and relaxed
interior” and so forth. These words express the Kansei of
the consumers’ desire towards the kind of vehicle that
would satisfy their needs. These needs can be trans-
formed and realized by through product design and de-
velopment by manufacturers which in turn satisfy the
customers need. Presently, KE have been successfully
applied in areas such as home appliances, packaging de-
sign, work equipment or architecture [21,22].
3. Process of Kansei Engineering
KE methodology embeds tacit knowledge which has
been deeply rooted in Japanese culture making it unique
[4,23]. The objectives of KE are to translate Kansei into
product properties and used as a basis to build and vali-
date a prediction model [24].
Since KE is flexible in nature, variety of techniques
can be deployed depending on the type of KE. The proc-
ess of Kansei measurement and its underlying procedures
may differ accordingly. Nonetheless, the fundamental
structure contains basically the same standard procedure
There are at least eight types of KE [4,7,15-17] which
1) Type 1—Category Classification
2) Type 2—Kanse i Engineering System
3) Type 3—Hybrid Engineering System
4) Type 4—Kanse i Engineering Modelling
5) Type 5—Virtual Kansei Engineerin g
6) Type 6—Collaborative KE Design
7) Type 7—Concurrent KE
8) Type 8—RoughSet KE
However, this study will only concentrate on Type I
which is the simplest method of Kansei analysis and has
proven track record in industry [25]. A popular car maker,
Mazda, for example is using this type to design their cars
and one of their popular products as a result of this
method is Mazda Miata [7,14,20].
The most critical part that requires KE expert is the
“Interpretation of the analyzed data” phase. In this phase,
a set of Kansei Words that have most significant effect
on each emotion are selected based on statistical analysis
result. To date there are no hard and fast syntax rules.
Thus, the development of an automatic Kansei word
analysis system will significantly provide a faster way of
analysis and interpretation of the Kansei words.
4. Framework Design for Co mp u t er - Ai d ed
Computer-aided system is defined as advanced comput-
ing technologies that access various models to provide
Open Access JSEA
Prelude to Natphoric Kansei Engineering Framework
specific information when requested by user input [26].
The system has three primary elements which are:
1) An interface with the user.
2) A reasoning element that triggers system action.
3) A knowledge element in the form of databases, know-
ledge bases, and modeling modules that provides the in-
formation and analyses to be applied.
Various traditional and manual systems have migrated
to computer-aided system. Some examples of those mi-
grations are [27-29].
In these systems, the critical part of the process which
traditionally done manually is converted to a computer-
aided system. The process is thoroughly studied and a
computer system is built to simulate the process. The
advantage of a computer system is that it reduces human
workload, dependent on experts and hum an errors.
In KE, consumers’ Kanse i is mapped into design ele-
ments. Meanwhile, the requirement for seeking more
accurate mapping relationship never stops. Previous re-
search works proposes mapping schemes, such as grey
theory [30], neural network [31], fuzzy logic [9], and
linear regression [32]. However, these models have their
own deficits, limitations and are not accurate enough [33].
Therefore, this study only focuses on automating KE
expertise using intelligent approach. Evident from past
literature show no attempts have been made to employ
this technique in computer-aided KE.
5. Multivariate Statistical Analysis Phase in
In KE Type I, Kansei, data can be obtained from Kansei
survey. The data are analyzed using multivariate statisti-
cal method. The result of this process will be further
analyzed by experts in order to determine significant
Kansei words. Figure 1 shows the process of KE Type I
with the “Interpretation of the analyzed data” phase high-
There are a variety of statistical methods that are
commonly used in the multivariate statistical analysis
Figure 1. KE Type I process.
process. The statistical methods that are commonly used
are Correlation Coefficient Analysis (CCA), Principal
Component Analysis (PCA), Factor Analysis (FA), Con-
joint Analysis and Quantification Theory Type I (QT1).
KE researchers either use one or combinations of these
statistical methods. However, using th e methods requires
a certain level of expertise. This is one of the problems of
introducing KE in the industry. Experts in the area of
statistics, cognitive ergonomics and product d evelopment
are the main requirement [6]. For any of the statistical
methods, experts are required to analyze the result of the
statistical analysis to select significant elements. The
significant elements can be factors or set of Kansei wo r ds.
The selection is based on the score of the statistical
analysis and kansei expert knowledge.
[4] states that the most important results are derived
from Factor Analysis. The result of Factor Analysis eases
the process of identifying design elements. So, this study
will be focusing on Factor Analysis and identifying
methods to aid the analysis process. The objective of
Factor Analysis is to classify large number of variables
into groups, called factors. Mathematical relations be-
tween these factors can also be calculated. This analysis
can be used in order to understand the relationship be-
tween low level Kansei words and high level Kansei
words. The result of this process is a matrix where the
Kansei words are grouped into factors that reveal the
relationship between the word s. Table 1 show s a fraction
of Factor Analysis from a study on website design using
KE [20].
For instance in Table 1, the website emotion is struc-
tured into five factors. The first factor consists of “Mys-
tic”, “Futuristic”, “Masculine”, “Luxury”, “Sophisti-
cated”, “Surreal”, “Impressive”, “Gorgeous”, “Cool” and
“Professional”. The research classifies this factor of
emotion to represent the concept of “Exclusiveness”. In
classifying each factor group, the research followed the
common practice performed in KE, to select representa-
tive words that can effectively describe the factor group
[7]. The output of this Factor Analysis will be used in th e
next phase of Kansei Engineering to find the relation-
ships between those emotions with the product proper-
In order to enable users with only basic knowledge of
KE to execute the KE process, the system must act as the
expert to aid users during Kansei words selection process.
This project proposes using intelligent algorithm to
simulate the experts. A collection of Kansei words needs
to be collected and their similarities in term of meanings
need to be identified to ensure proper selection of Kansei
words can be programmed into the system using results
obtained from the Factor Analysis. Intelligence property
of Natphoric algorithm is suitable for simulating this
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Prelude to Natphoric Kansei Engineering Framework
Open Access JSEA
Table 1. Factor Analysis table example [20].
Creative 0.777329 Lively 0.67778 Comfortable 0.194254
Classic 0.794316 Appealing 0.682461 Refreshing 0.204197
Professional 0.805803 Pretty 0.689458 Sexy 0.272922
Cool 0.811333 Lovely 0.690027 Classic 0.275441
Gorgeous 0.812754 Elegant 0.703414 Boring 0.308598
Impressive 0.822734 Adorable 0.713039 Light 0.313839
Surreal 0.846445 Charming 0.763686 Neat 0.319281
Sophisticated 0.848426 Sexy 0.787619 Calm 0.339163
Luxury 0.878831 Cute 0.794058 Relaxing 0.348516
Masculine 0.899118 Beautiful 0.816958 Natural 0.474887
Futuristic 0.913165 Chic 0.93916 Plain 0.839005
Mystic 0.941857 Feminine 0.948707 Simple 0.9241
6. Natphoric Algorithm
Traditional artificial intelligence (AI) mainly concerned
with reproducing the abilities of human brain, but the
newer approaches is simulated based on inspiration from
biological structures and behavior that are capable of
autonomous self-organization. Natphoric algorithm is a
new approach of AI which comes from the idea that in-
telligence not only appears in evolution, development
and learning, but also appears as much in cells, bodies
and characterization of societies [34].
Algorithm that is needed in this project is the one that
can store and cluster a collection of Kansei words based
on their similarities. The algorithm also needs to be
flexible and dynamic, which can learn and adaptively
updates its databases when new words are added.
In this project, the collection and grouping of Kansei
words is a data clustering problem. The flexible, robust,
decentralized and self-organized property of Swarm In-
telligence (SI) is suitable for solving complex problems
such as data clustering [35].
[36] defines SI as any attempt to design algorithms or
distributed problem-solving devices based on the collec-
tive behavior of social insect colonies or other animals.
The characteristics of their behaviors such as social in-
teractions and attraction among similar groups (swarm)
have inspired the designing of several types of optimiza-
tion algorithms [34,37].
Natphoric algorithms have been applied and found to
be very successful in many applications such as business,
engineering, space exploration and many others [38]. In
this study, the ability of Natphoric algorithm in data clas-
sification and clustering will be used to collect and proc-
ess Kansei words. Successful applications of Natphoric
algorithm for similar purpose are [39-41]. Such success
stories prompt this study to formulate ant-based cluster-
ing Natphoric algorithms to develop an intelligent re-
pository of Kanse i words that can be used by automated
KE system in aiding the analysis and interpretation proc-
Ant Colony Optimization (ACO) algorithm has the
potential of simulating the process of interpreting Factor
Analysis result. The ACO algorithm can be used to store
the relationships between Kansei words by classification
and clustering which aid the analysis process in Factor
Analysis used to select Kans ei words in representing
each factor. Which Kansei words to choose and what
words to represent each group can then be identified
based on the rela t i on in AC O.
7. Automated KE Framework
To realize the development of computer-aided KE sys-
tem, a framework for automated KE will be designed.
Figure 2 depicts the automated Natphoric KE Frame-
The explanations of the phases in the framework are as
Phase 1: Identification of domain and Kansei words.
Step 1: User will need to specify the product domain.
Step 2: Automatic suggestion of Kansei words by the
system database based on the domain. The set of Kansei
Words are collected from previous research works based
on their domain and stored into a database.
Step 3: User will then finalize the suggested new
words, whether to add new Kansei words or remove any
from the list.
Phase 2: Specification of design elements.
Step 1: The design elements or specification of the
product need to be specified. For example color, shape,
size, etc.
Prelude to Natphoric Kansei Engineering Framework
Figure 2. Proposed Automated Natphoric KE framework.
Step 2: After specifying all these information, about
30 to 40 product specimens are uploaded into the system.
These specimens consist of products from the company
and other makers that will be used during the survey. Fo r
each specimen, user needs to define design elements ac-
cording to that have been specified earlier. For example,
for color element, user can define blue, red, white, or any
Phase 3: Web-based survey.
Step 1: Construction of web-based survey pages. The
system will be able to generate a link to the survey page
that can be used by test subjects to do the evaluation.
Step 2: Conduct the survey. The evaluation experi-
ment is done on a number of subjects. They will record
their feeling on the SD scale sheet on the website with
the Kansei words specified earlier as shown in Figure 3.
Phase 4: Factor Analysis.
Step 1: Data that is collected from the survey will th en
be analyzed using Factor Analysis (FA) to identify sig-
nificant factors of emotion. FA detailed out the structure
of emotion, where it determines significant factors of
emotion [20].
Step 2: Kanse i words that contribute to the factors that
have been selected will be identified from the Factor
Figure 3. Web-based survey.
Analysis result table. In the table, the Kansei words are
sorted in increasing order of factor value.
Phase 5: Evaluation done manually by expert and
automatic na tphori c evaluation system.
In a normal KE process, experts will select representa-
tive words which they could effectively describe the fac-
tor group [7]. There is no specific formula or rules to
make the selection.
Step 1: The Natph ori c algorithm is used to replicate
the expert knowledge in making the selection. The Kan-
sei words will be checked with the Kans ei words reposi-
tory that is built using Ant Colony Clustering algorithm.
The result of this process is a set of factors and group of
Kansei words that is contributing to each of the factor.
Step 2: Users are able to add more Kansei words or re-
move any of them from the suggestion that they think
appropriate. This information will be sent back to the
repository so that it can learn from this new relationship.
Phase 6: Analysis using PLS.
Partial Least Square (PLS) analysis will then be ap-
plied to the result from previous phase in order to iden-
tify the relationships between emotion and product de-
sign elements. It is used to rate the influence of the de-
sign elements in each emotion, the best and worst value
for each design elements, and the kind of emotion elic-
ited by each specimen.
Phase 7: Product design guidelines.
After all the analyses are done, the system will be able
to produce a guideline for designing the product. Results
of structure of emotion from FA with Natpho ri c were
used to conceptualize emotion, and result from PLS
scores were used to compose the design requirement. The
design requirements included in the guideline were from
the elements that have highest influence in eliciting tar-
get emotion.
8. Discussion
Evolutions in product design have led to many inventions
that allow high quality product being introduced to the
market. Consumers have vast choice of products as a re-
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Prelude to Natphoric Kansei Engineering Framework 643
sult of a highly competitive market. Hence, producers
strive to design products that can stand out and attract
consumers. Kansei Engineering (KE) was invented when
the founder realized that existing product design method
did not take into account the consumers’ feeling that en-
abled their needs to be satisfied, thus capture their atten-
tion. KE also helps producers in investigating how d esign
requirements influence consumer’s attention.
Even though KE has been proven to be successful in
designing a heart winning products in a variety of do-
mains, the process is not simple. Experts on KE and
product development are required in the process. Com-
panies are hesitating to adapt the technique because the
method is not transparent and the need to hire third party
In order to enable normal users with just a basic
knowledge of KE to apply the KE process, steps that
require expertise need to be automated. The most crucial
part in KE is the analysis phase. The process has a possi-
bility to be automated by applying Artificial Intelligence
to it. Studies show that An t Colony Optimization, one of
Natphoric algorithm seems promising to be formulated
and automate the Factor Analysis process.
9. Conclusion
This paper proposes an automated framework for Kanse i
Engineering by incorporating Natphoric algorithm in its
analysis process. The study focuses on Kansei Engineer-
ing Type I. The proposed framework will automate the
component in KE that requires experts and statistical
analysis. Therefore, the analysis process will be perfo rm-
ed at a centralized component as opposing to referring to
several different statistical tools and so ftware. As a result,
the KE process will be more accessible and convenient to
use. Furthermore, a person with basic knowledge of KE
is able to perform the KE process easily. Thus, the im-
plemented tool will help them design products that will
attract customers.
10. Acknowledgements
This work is supported by Research Management Insti-
tute of Universiti Teknologi MARA, Malaysia and Min-
istry of Higher Education Malaysia under the Explora-
tory Research Grant Scheme [Project Code: 600RMI/
ERGS 5/3 (32/20 1 2)] .
[1] J. Rajasekera and S. Dayal, “Using Kansei Engineering
with New JIT to Accomplish Cost Advantage,” Interna-
tional Journal of Biometrics, Vol. 2, No. 2, 2010, pp.
[2] N. Mitsuo, “Perspectives and New Trend of Kansei/Af-
fective Engineering,” 1st European Conference on Affec-
tive Design and Kansei Engineering & 10th QMOD Con-
ference, University of Linkoping and Lund University,
Helsingborg, 2007.
[3] H. M. Khalid, “Conceptualizing Affective Human Factors
Design,” Theoretical Issues in Ergonomics Science, Vol.
5, No. 1, 2004, pp. 1-3.
[4] M. Nagamachi, I. Ishihara, A. M. Lokman, T. Nishino,
M., Matsubara, T. Tsuchiya, et al., “Kansei/Affective En-
gineering,” Taylor & Francis Group, CRC Press, 2010.
[5] M. Nagamachi, “Kansei Engineering and Its Method,”
Management System, Vol. 2, No. 2, 1992, pp. 97-105.
[6] S. Schutte and J. Eklund, “Rating scales in Kansei Engi-
neering,” Internationcal Conference on Kansei Engi-
neering and Emotion Research 2010, KEER2011, Paris,
2010, pp. 23-25.
[7] M. Nagamachi and A. M. Lokman, “Innovations of Kan-
sei Engineering. Industrial Innovation Series,” Taylor &
Francis, Florida, 2010.
[8] L. Lin and C. Xue, “Review of Research and Develop-
ment Of Computer-Aided Kansei Engineering,” Frontiers
of Mechanical Engineering in China, Springer, 2009.
[9] J. Jiao, Y. Zhang and M. Helander, “A Kansei Minig
system for Affective Design,” Journal of Expert Systems
with Applications, Vol. 30, No. 4, 2006, pp. 658-673.
[10] M. Nagamachi, “Workshop 2 on Kansei Engineering,”
Proceedings of International Conference on Affective
Human Factors Design, Singapore, 2001.
[11] H. Yanagisawa and S. Fukuda, “Development of Interac-
tive Industrial Design Support System Considering Cus-
tomer’s Evaluation,” JSME International Journal, Vol.
47, No. 2, 2004, pp. 762-769.
[12] M. Nagamachi, “Kansei Engineering: A Powerful Ergo-
nomic Technology for Product Development,” In: Pro-
ceedings of the International Conference on Affective
Human Factors Design, Asean Academic Press, London,
2001, pp. 9-14.
[13] K. M. Nasser and T. Marjan, “Design with Emotional
Approach by Implementing Kansei Engineering—Case
Study: Design of Kettle,” International Conference on
Kansei Engineering and Emotion Research, KEER2010,
Paris, 2010, pp. 625-632.
[14] M. Nagamachi, “Kansei Engineering: The Implication
and Applications to Product Development,” IEEE Inter-
national Conference on SMC, 1999, pp. 273-278.
[15] A. M. Lokman, “Design & Emotion: The Kansei Engi-
neering Methodology,” Malaysian Journal of Computing,
Vol. 1, No. 1, 2010, pp. 1-11.
[16] A. M. Lokman, “Emotional User Experience in Web
Design: The Kansei Engineering Approach,” Univerisiti
Teknologi MARA, 2009.
[17] A. M. Lokman, N. L. M. Noor and M. Nagamachi, “Ex-
pert Kansei Web: A Tool to Design Kansei Website, En-
terprise Information Systems,” Springer, Berlin, Heidel-
berg, 2009.
[18] Y. Shimizu, T. Sadoyama, M. Kamijo, S. Hosaya, M.
Hashimoto, T. Otani, K. Yokoi, Y. Horiba, M. Taketera,
Open Access JSEA
Prelude to Natphoric Kansei Engineering Framework
Open Access JSEA
M. Honywood and S. Inui, “On-Demand Production Sys-
tems of Apparel on Basis on Kansei Engineering,” Inter-
national Journal of Clothing Science and Technology,
Vol. 16, No. 1/2, 2004, pp. 32-42.
[19] R. R. Seva, H. B. Duh and M. G. Helander, “The Mar-
keting Implications of Affective Product Design,” Jour-
nal of Applied Ergonomics, Vol. 38, No. 6, 2007, pp. 723-
[20] A. M. Lokman and M. Nagamachi, “Validation of Kansei
Engineering Adoption in e-Commerce Web Design,”
Kansei Engineering International, 2009.
[21] M. Nagamachi, “Kansei Engineering: An Ergonomic
technology for a Product Development,” Proceedings of
IEA’94, 1994, pp. 120-122.
[22] T. Childs, A. de Pennington, J. Rait, T. Robins, K. Jones,
C. Workman, S. Warren and J. Colwill, “Affective De-
sign (Kansei Engineering) in Japan, Faraday Packaging
Partnership,” University of Lees, UK, 2001.
[23] A. Lanzotti and P. Tarantino, “Kansei Engineering Ap-
proach for Total Quality Design and Continuous Innova-
tion,” The TQM Journal, Vol. 20, No. 4, 2008, pp.
[24] T. Murai, “Large Rough Sets and Modal Logics,” Journal
of Japan Society for Fuzzy Theory and Systems, Vol. 13,
No. 5, 2001, pp. 23-32.
[25] L. Hultman and S. Larsson, “Development of a Method
for Subjective Expert Evaluation of the Human Driving
Geometry,” Lulea University of Technology, Lulea, 2005.
[26] N. R. Council, et al., “Council Computer-Aided Materials
Selection during Structural Design,” The National
Academies Press, Washington, DC, 1995.
[27] D. E. Culler and W. Burd, “A Framework for Extending
Computer Aided Process Planning to Include Business
Activities and Computer Aided Design and Manufactur-
ing (CAD/CAM) Data Retrieval,” Robotics and Com-
puter-Integrated Manufacturing, Vol. 23, No. 3, 2007, pp.
[28] V. Raman and A. Palanissamy, “Computer Aided Legal
Support System: An Initial Framework for Retrieving
Legal Cases by Case Base Reasoning Approach,” Innova-
tions in Information Technology, Vol. 2, No. 48, 2008, pp.
[29] D. S. Kim, D. H. Baek and W. C. Yoon, “Development
and Evaluation of a Computer-Aided System for Analyz-
ing Human Error in Railway Operations,” Reliability En-
gineering and System Safety, Vol. 95, No. 2, 2010, pp.
[30] H. W. Hsiao and E. Liu, “A Neurofuzzy—Evolutionary
Approach for Product Design,” Integrated Computer-
Aided Engineering, Vol. 11, No. 4, 2004, pp. 323-338.
[31] H. W. Hsiao and H. C. Tsai, “Applying a Hybrid Ap-
proach Based on Fuzzy Neural Network and Genetic Al-
gorithm to Product Form Design,” International Journal
of Industrial Ergonomics, Vol. 35, No. 5, 2005, pp. 411-
[32] Y. Kinoshita, S. Ichinohe, Y. Sakakura, E. W. Cooper and
K. Kamei, “Kansei Product Design for the Active Senior
Generation—A Case Study of Mobile Phone Designs,”
International Conference on Kansei Engineering and
Emotion Research, 2007.
[33] K. C. Wang, “A Hybrid Kansei Engineering Design Ex-
pert System Based on Grey System Theory and Support
Vector Regression,” Expert Systems with Applications,
Vol. 38, No. 7, 2011, pp. 8738-8750.
[34] D. Floreano and C. Mattiussi, “Bio-Inspired Artificial
Intelligence Theories, Methods and Technologies,” MTI
Press, 2008.
[35] X. Cui Swarm, “Intelligence in Text Document Cluster-
ing,” Oak Ridge National Laboratory, 2009.
[36] E. Bonabeau, M. Dorigo and G. Theraulaz, “Swarm intel-
ligence: From natural to artificial intelligence,” Oxford
University Press, New York, 1999.
[37] G. Martens, C. L. Poppe and R. V. Walle, “Unsupervised
Texture Segmentation and Labeling Using Biologically
Inspired Features, Multimedia Signal Processing,” IEEE
10th Workshop, 2008.
[38] R. Chiong, “Nature-Inspired Informatics for Intelligent
Applications and Knowledge Discovery: Implications in
Business, Science, and Engineering,” 2009.
[39] N. Tsimboukakis and G. Tambouratzis, “Word Map Sys-
tems for Content-Based Document Classification,” IEEE
Transactions on Systems, Man & CyberneticsPart C,
2011, pp. 662-673.
[40] S. G. Khode and R. Bhatia, “Improving Retrieval Effec-
tiveness Using Ant Colony Optimization,” International
Conference on Advances in Computing, Control, & Tele -
communication Technologies, 2009, pp. 737-741.
[41] Z. S. Xu et al., “The Study on Electric Power System
Based on Swarm Intelligence,” Advanced Materials Re-
search, Vol. 442, 2012, pp. 424-429.