Study on innovative design of urban intelligent lighting appliance (UILA) based on kansei engineering

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Abstract

Urban Intelligent Lighting Appliance (UILA) is a technological terminal device of smart city, which is a energy-saving lighting based on urban street LED lamp and achieves the functions of smart city in the environment of big data, including intelligent control, energy-saving lighting, charging of electrical vehicles, information distribution and exchange, intelligent transportation, WIFI, environmental monitoring, audio broadcasting, asking for help with one key and so on. Kansei engineering aims at the development or improvement of products and services by translating the customer’s psychological feelings and needs into the domain of product design, so It’s essential to research innovative design of UILA based on kansei engineering. The purpose of this research is to clarify a method to construct a support system capable of UILA modeling elements that fit the customer’s Kansei, and to verify the effectiveness of this method based on Kansei engineering, for the selection of recommended design samples. To accomplish this, this paper analyse the necessity of Kansei engineering applied in the innovative design of UILA and put forward the framework of UILA innovative design based on Kansei engineering of forward quantitative inference. Then it discussed how to build the relationship between emotional images and UILA modeling elements in detail by means of the innovative design of UILA shaping, which will provide a basis for guiding UILA styling innovation and design effect evaluation. In the end, it pointed out that the expansion of applications of Kansei engineering in the field UILA products design have broad prospects and can better meet the users’perceptual needs.

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APA

Cheng, J., Ye, J., Yang, C., Yao, L., Ma, Z., & Li, T. (2018). Study on innovative design of urban intelligent lighting appliance (UILA) based on kansei engineering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10921 LNCS, pp. 214–222). Springer Verlag. https://doi.org/10.1007/978-3-319-91125-0_18

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