Ongoing
2
Finished
19
Development of Intelligent Interaction Technology
based on Context Awareness and Human Intention Understanding
Publications
•
Lai, P. Y., Kim, M., Choi, M., Lee, C.-S., Porcellini, V., Yi, T. & Lee, J.-H. (2019). Framework of Judgement System for Smart Home Assistant utilizing Collective Intelligence Case-Based Reasoning
, In M.H. Haeusler, M.A. Schnabel & T. Fukuda (Eds.), Intelligent & Informed: Proceedings of the 24th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) , 15-18 April, 2019 , Victoria University of Wellington, New Zealand, pp. 695-704.
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Yi, T., Lee, I., Lee, C.-S., Lee, G., Kim, M. & Lee, J.-H. (2018). Interactive Data Acquisition for CBR System Based Smart Home Assistant
, In T. Fukuda, W. Huang, P. Jenssen, K. Crolla, and S. Alhadidi (Eds.), Learning, Ptorotyping and Adapting: Proceedings of the 23rd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) , 17-19 May, 2018, Tsinghua University, Beijing, China (Vol.2, pp. 525-534). Beijing: Jingcai Xueyun Printing House.
•
Yi, T., Rhim, J., Lee, I., Narangerel, A., & Lee, J.-H. (2017). Service Design of Intergeneration Home-Sharing System Using VR-Based Simulation Technology and Optimal Matching Algorithms
. In C. Stephanidis (Ed.), HCI International Posters 2017, Part II, CCIS 714 , 9-14 July 2017, Vancouver Convention Centre, Vancouver, Canada (pp. 95-100). Springer International Publishing, AG 2017.
AI Flagship: Smart Home Assistant
2016/12/01 → 2019/04/30
과학기술정보통신부 (MSIT)
IoT
Case-Based Reasoning
FBS
Recommender system
Project
Style synthesis and analysis of car designs for style quantification
Understanding how similar design appears is a key element to understanding companies’ design strategies. However, it is difficult to evaluate companies’ design strategies with conventional style measurement methods since they only taxonomically measure whether a specific characteristic is included in a specific style. This study numerically measured car design similarities to synthesize and analyze car brand styles, thereupon discovering the design trends among car brands for strategic design positioning. This paper aims to find methods for quantifying style differences and identifying unique design elements of car designs among 23 automobile manufacturers based on design similarities of a large quantity of car designs (N
= 119). To achieve this goal, a hybrid style quantification methodology – a mixture of Fourier decomposition, eye tracker, and shape grammar – was created to evaluate similarities, visual significance, and combinations of 19 car design elements. Fourier decomposition was incorporated to find the quantifiable values of design similarities of car design elements. Visual significance analysis was also conducted for each car design element through eye tracker to measure the importance of certain design elements for weighting factors. Then, each combination of design elements was compared with car design elements of other cars for similarity calculations. Finally, car design alternatives were synthesized, and transitions of design positioning were analyzed based on the similarity values weighed by the visual significance results. Using the suggested methods, alternate designs can be synthesized while preserving brands’ design styles, and design trends can be analyzed for strategic evaluation.
Publications
Style Synthesis and Analysis of Car Designs (Kyung-Hoon Hyun)
2012/03/01 → 2016/02/29
Design strategy
Product asethetics
Brand identity
Visual significance
Style analysis
Thesis/Dissertation
A quantitative approach for assessment of creativity in product design
Most of the assessment of creativity in product design is based on the outcome, not the design process from which the creative ideas are derived. In this paper, we revealed the correlation coefficient of 20 factors critical in the product design process and the quality of design creativity via investigation of the design processes and outcomes of 30 senior student designers. Six closely related factors were identified as variables to calculate the design creativity. An assessment formula was proposed: the corresponding correlation coefficient is the weight factor of each variable, and the sum represents the design creativity degree. Our quantitative approach can improve the validity and reliability of assessment of creativity in product design.
Publications
Assessment of creativity in product design (Xiaofang Yuan)
2009/03/01 → 2013/03/01
Creativity
Quantitative assessment
Design process
Product Service System Design (PSSD)
Thesis/Dissertation




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