Sensibility Extraction for Bicycle Design Using RFID Tag-Attached Crayons
Providing sensibility design using information convergence technology is an important factor in product service strategies. It is possible to ensure future competitiveness in bicycle industries by developing and specializing highly sensibility bicycle design. The necessity arises of creating a sensibility engineering approach to develop products that appeal to a wide variety of customers by stimulating their senses and creating emotional satisfaction. In this paper, we proposed a bicycle design recommendation using RFID tag-attached crayons. The proposed method obtains visual appeal using these RFID tag-attached crayons. Associative color patterns are analyzed using data mining, which extracts conceptual information from the collected data that is not easily exposed. The association can be determined as crayon colors are presented in a specific transaction, and different crayon colors are presented in the same transaction. The association rule represents a strong relationship between color sets. Designing frames, saddles, pedals, wheel sets, tires, cranks, and other parts of a bicycle based on visual sensibility represents a final shape by advancing through an application to a virtual model. By providing bicycle design adapted to one’s own design, it reduces cost and time and makes it possible to apply it to the desired styles.
KeywordsSensibility engineering Bicycle design Recommendation RFID tags
This work was supported by the Korea Foundation for the Advancement of Science & Creativity (KOFAC) grant funded by the Korean Government (MEST).
- 1.Lee H (2011) An research of a recently bicycle decoration design analysis and development. J Korea Digit Des 11(1):613–623Google Scholar
- 2.Jung HI, Jo SM, Rim KW, Lee JH, Chung KY (2012) Ergonomics automotive design recommendation using image based collaborative filtering. In: Proceeding of the international conference on information science and applications. IEEE Computer Society, pp 211–216Google Scholar
- 3.Shinohara A, Shimizu Y, Sakamoto K (1996) Introduction to Kansei engineering. Kumbuk Pub, Kyoto, pp 45–69Google Scholar
- 4.Kwan OK, Na YJ, Kim HE (2000) Fashion and sensibility science. Kyomunsa, Seoul, pp 25–30Google Scholar
- 5.Jung KY, Na YJ, Lee JH (2003) Creating user-adapted design recommender system through collaborative filtering and content based filtering. EPIA’03, LNAI 2902, Springer, pp 204–208Google Scholar
- 6.Skyetek, http://www.skyetek.com/
- 7.Chung KY (2008) Recommendation using context awareness based information filtering in smart home. J Korea Contents Assoc 8(7):17–25Google Scholar
- 8.Agrawal R, Srikant R (1994) Fast Algorithms for mining association rules, In: Proceeding of the 20th VLDB conference, Santiago, Chile, pp 487–499Google Scholar
- 9.Chung KY, Lee D, Kim KJ (2011) Categorization for grouping associative items data mining in item-based collaborative filtering. Multimedia Tools Appl, Published onlineGoogle Scholar
- 10.Jung KY (2006) Associative neighborhood according to representative attributes for performing collaborative filtering. ICIC’06, LNCIS 344, Springer, pp 839–844Google Scholar