Fatih Emre Boran
Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey
Burak Efe
Department of Industrial Engineering, Faculty of Engineering and Architecture, Necmettin Erbakan University, Konya, Turkey
Diyar Akay
Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey
Brian Henson
School of Mechanical Engineering, University of Leeds, Leeds, UK
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Published in: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13
Linköping Electronic Conference Proceedings 100:103, p. 1235-1245
To increase the chance of launching a successful product into market; it is essential to satisfy customers’ affective needs during the product design stage. However; understanding customers’ affective needs is very difficult task and product designers might misunderstand the customers’ affective needs. In this study; linguistic summarization with fuzzy set is used to present customers’ affective needs with natural language statements which could be easily understood by human beings. The relations between customers’ affective needs and product design elements are represented by type-I and type-II fuzzy quantified sentences. To illustrate the applicability of the linguistic summarization with fuzzy set in translating customers’ affective needs to natural language statements; a case study is conducted on mobile phone design. The results indicate that the linguistic summarization with fuzzy set can be a useful tool to assist designers to create products satisfying affective needs of customers.
Akay; D.; & Kurt; M. (2009). A neuro-fuzzy based approach to affective design. International Journal
of Advanced Manufacturing Technology; 40(5-6); 425-437.
Bahn; S.; Lee; C.; Nam; C. S.; & Yun; M. H. (2009). Incorporating Affective Customer Needs for
Luxuriousness into Product Design Attributes. Human Factors and Ergonomics in
Manufacturing; 19(2); 105-127.
Barro; S.; Bugarin; A. J.; Carinena; P.; & Diaz-Hermida; F. (2003). A framework for fuzzy
quantification models analysis. Ieee Transactions on Fuzzy Systems; 11(1); 89-99.
Chan; K. Y.; Kwong; C. K.; Dillon; T. S.; & Fung; K. Y. (2011). An intelligent fuzzy regression
approach for affective product design that captures nonlinearity and fuzziness. Journal of
Engineering Design; 22(8); 523-542.
Chang; H. C.; Lai; H. H.; & Chang; Y. M. (2007). A measurement scale for evaluating the
attractiveness of a passenger car form aimed at young consumers. International Journal of Industrial Ergonomics; 37(1); 21-30.
Dahan; E.; & Srinivasan; V. (2000). The predictive power of Internet-based product concept testing
using visual depiction and animation. Journal of Product Innovation Management; 17(2);
99-109.
Delgado; M.; Sánchez; D.; & Miranda; M. A. V. (1999). A survey of methods for evaluating
quantified sentences. Paper presented at the EUSFLAT-ESTYLF Joint Conf.
Delgado; M.; Sanchez; D.; & Vila; M. A. (2000). Fuzzy cardinality based evaluation of quantified
sentences. International Journal of Approximate Reasoning; 23(1); 23-66.
Demirbilek; O.; & Sener; B. (2003). Product design; semantics and emotional response.
Ergonomics; 46(13-14); 1346-1360.
Diaz-Hermida; F.; & Bugarín; A. (2011). Semi-fuzzy quantifiers as a tool for building linguistic
summaries of data patterns. Paper presented at the Foundations of Computational
Intelligence (FOCI); 2011 IEEE Symposium on.
Diaz-Hermida; F.; Bugarin; A.; & Barro; S. (2003). Definition and classification of semi-fuzzy
quantifiers for the evaluation of fuzzy quantified sentences. International Journal of
Approximate Reasoning; 34(1); 49-88.
Diaz-Hermida; F.; Bugarin; A.; Carinena; P.; & Barro; S. (2004). Voting-model based evaluation of
fuzzy quantified sentences: a general framework. Fuzzy Sets and Systems; 146(1); 97-120.
Diaz-Hermida; F.; Losada; D. E.; Bugarin; A.; & Barro; S. (2005). A probabilistic quantifier
fuzzification mechanism: The model and its evaluation for information retrieval. Ieee
Transactions on Fuzzy Systems; 13(5); 688-700.
Dubois; D.; Hullermeier; E.; & Prade; H. (2006). A systematic approach to the assessment of fuzzy
association rules. Data Mining and Knowledge Discovery; 13(2); 167-192.
Dubois; D.; & Prade; H. (1996). What are fuzzy rules and how to use them. Fuzzy Sets and
Systems; 84(2); 169-185.
Dubois; D.; Prade; H.; & Sudkamp; T. (2005). On the representation; measurement; and discovery
of fuzzy associations. Ieee Transactions on Fuzzy Systems; 13(2); 250-262.
Han; S. H.; Hwan Yun; M.; Kim; K.-J.; & Kwahk; J. (2000). Evaluation of product usability:
development and validation of usability dimensions and design elements based on empirical
models. International Journal of Industrial Ergonomics; 26(4); 477-488.
Han; S. H.; Kim; K. J.; Yun; M. H.; Hong; S. W.; & Kim; J. (2004). Identifying mobile phone design
features critical to user satisfaction. Human Factors and Ergonomics in Manufacturing; 14(1);
15-29.
Han; S. H.; Yun; M. H.; Kwahk; J.; & Hong; S. W. (2001). Usability of consumer electronic products.
International Journal of Industrial Ergonomics; 28(3-4); 143-151.
Hong; S. W.; Han; S. H.; & Kim; K. J. (2008). Optimal balancing of multiple affective satisfaction
dimensions: A case study on mobile phones. International Journal of Industrial Ergonomics;
38(3-4); 272-279.
Hsiao; S.-W.; & Huang; H.-C. (2002). A neural network based approach for product form design.
Design studies; 23(1); 67-84.
Hsiao; S. W. (1994). Fuzzy Set-Theory Applied to Car Style Design. International Journal of Vehicle
Design; 15(3-5); 255-278.
Hsiao; S. W.; & Tsai; H. C. (2005). Applying a hybrid approach based on fuzzy neural network and
genetic algorithm to product form design. International Journal of Industrial Ergonomics;
35(5); 411-428.
Jiao; J. X.; Zhang; Y. Y.; & Helander; M. (2006). A Kansei mining system for affective design. Expert
Systems with Applications; 30(4); 658-673.
Katz; J. E.; & Sugiyama; S. (2005). Mobile phones as fashion statements: The co-creation of mobile
communication’s public meaning Mobile Communications (pp. 63-81): Springer.
Khalid; H. M.; & Helander; M. G. (2004). A framework for affective customer needs in product
design. Theoretical Issues in Ergonomics Science; 5(1); 27-42.
Kwon; K. S. (1999). Human sensibility ergonomics in product design. International Journal of
Cognitive Ergonomics; 3(1); 51-62.
Lai; H. H.; Lin; Y. C.; & Yeh; C. H. (2005). Form design of product image using grey relational
analysis and neural network models. Computers & Operations Research; 32(10);
2689-2711.
Lai; H. H.; Lin; Y. C.; Yeh; C. H.; & Wei; C. H. (2006). User-oriented design for the optimal
combination on product design. International Journal of Production Economics; 100(2);
253-267.
Lin; Y. C.; Lai; H. H.; & Yeh; C. H. (2007). Consumer-oriented product form design based on fuzzy
logic: A case study of mobile phones. International Journal of Industrial Ergonomics; 37(6);
531-543.
Martin; T.; & Shen; Y. (2009). Fuzzy Association Rules in Soft Conceptual Hierarchies. 2009 Annual
Meeting of the North American Fuzzy Information Processing Society; 247-252.
Martin; T.; Shen; Y.; & Majidian; A. (2010). Discovery of Time-Varying Relations Using Fuzzy
Formal Concept Analysis and Associations. International Journal of Intelligent Systems;
25(12); 1217-1248.
Nagamachi; M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for
product development. International Journal of Industrial Ergonomics; 15(1); 3-11.
Oztekin; A.; Iseri; A.; Zaim; S.; & Nikov; A. (2013). A Taguchi-based Kansei engineering study of
mobile phones at product design stage. Production Planning & Control; 24(6); 465-474.
Poirson; E.; Depince; P.; & Petiot; J. F. (2007). User-centered design by genetic algorithms:
Application to brass musical instrument optimization. Engineering Applications of Artificial
Intelligence; 20(4); 511-518.
Serrurier; M.; Dubois; D.; Prade; H.; & Sudkamp; T. (2007). Learning fuzzy rules with their
implication operators. Data & Knowledge Engineering; 60(1); 71-89.
Shi; F. Q.; Sun; S. Q.; & Xu; J. (2012). Employing rough sets and association rule mining in KANSEI
knowledge extraction. Information Sciences; 196; 118-128.
Shieh; M. D.; & Yang; C. C. (2008). Classification model for product form design using fuzzy support
vector machines. Computers & Industrial Engineering; 55(1); 150-164.
Tsai; H. C.; Hsiao; S. W.; & Hung; F. K. (2006). An image evaluation approach for parameter-based
product form and color design. Computer-Aided Design; 38(2); 157-171.
Wang; K. C. (2011). A hybrid Kansei engineering design expert system based on grey system
theory and support vector regression. Expert Systems with Applications; 38(7); 8738-8750.
Wu; D. R.; & Mendel; J. M. (2011). Linguistic Summarization Using IF-THEN Rules and Interval
Type-2 Fuzzy Sets. Ieee Transactions on Fuzzy Systems; 19(1); 136-151.
Yager; R. R. (1982). A New Approach to the Summarization of Data. Information Sciences; 28(1);
69-86.
Yager; R. R. (1991). On linguistic summaries of data. Knowledge discovery in databases; 347-363.
Yager; R. R. (1995). Linguistic summaries as a tool for database discovery. Paper presented at the
Proc. of the FUZZ-IEEE/IFES.
Yager; R. R. (1996). Database discovery using fuzzy sets. International Journal of Intelligent
Systems; 11(9); 691-712.
Yanagisawa; H.; & Fukuda; S. (2005). Interactive reduct evolutional computation for aesthetic
design. Journal of Computing and Information Science in Engineering; 5(1); 1-7.
Yang; C. C. (2011a). A classification-based Kansei engineering system for modeling consumers’
affective responses and analyzing product form features. Expert Systems with Applications;
38(9); 11382-11393.
Yang; C. C. (2011b). Constructing a hybrid Kansei engineering system based on multiple affective
responses: Application to product form design. Computers & Industrial Engineering; 60(4);
760-768.
Yang; C. C.; & Shieh; M. D. (2010). A support vector regression based prediction model of affective
responses for product form design. Computers & Industrial Engineering; 59(4); 682-689.
Zadeh; L. A. (1983). A Computational Approach to Fuzzy Quantifiers in Natural Languages.
Computers & Mathematics with Applications; 9(1); 149-184.
