인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.2
- 수록면
- 19 - 28 (10page)
- DOI
- 10.15187/adr.2016.02.29.1.19
이용수
초록· 키워드
Background : Emotions are complex conscious states that play an essential role in our daily lives. They have a great influence on human perception, particularly concerning our thinking, learning and decisions. In design, product emotion plays an important role in encouraging product quality and sustainability. Several methods and tools exist and are used to in the process of evaluating users-emotional experiences with products. Emotion is important to design because virtually all the decisions we make are based on how we feel or how we anticipate we will feel. Kansei engineering is a consumer-oriented technique for new product development. In product design, for instance, clothing and fabric design can be applied to translate consumers’ favorite feelings or images into physical design elements.
Methods : This paper aims to develop the process of evaluating users’ emotional experiences in a more accurate semantic dimension through Kansei. Image feature extraction is a key issue for concept recognition in images and particularly emotions. In analyzing the product images, KJ methods based on the affective model was used to develop the full range of emotional keywords. This method shows the potentiality, limitation and building of a new framework for understanding the semantic structure used by designers in the visual analysis process for fabric analysis of clothing. Another affective model from SD methods also needed to support all the data.
Result : The results show that various methods of the affective model will be a good reference for design studies that are involved with users’ subjective emotional experience with product design. The emotional expression showed that it can be well engaged in the process of satisfied and dissatisfied or desired or undesired in product choices.
Conclusions : The process of identifying consumer needs is an integral part of the larger product development process and most closely related to the conceptual design, design selection, competitive benchmarking and establishment of product specification. Kansei methods will lead to better perceptions and understanding of product images and forms.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지Methods : This paper aims to develop the process of evaluating users’ emotional experiences in a more accurate semantic dimension through Kansei. Image feature extraction is a key issue for concept recognition in images and particularly emotions. In analyzing the product images, KJ methods based on the affective model was used to develop the full range of emotional keywords. This method shows the potentiality, limitation and building of a new framework for understanding the semantic structure used by designers in the visual analysis process for fabric analysis of clothing. Another affective model from SD methods also needed to support all the data.
Result : The results show that various methods of the affective model will be a good reference for design studies that are involved with users’ subjective emotional experience with product design. The emotional expression showed that it can be well engaged in the process of satisfied and dissatisfied or desired or undesired in product choices.
Conclusions : The process of identifying consumer needs is an integral part of the larger product development process and most closely related to the conceptual design, design selection, competitive benchmarking and establishment of product specification. Kansei methods will lead to better perceptions and understanding of product images and forms.
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목차
- Abstract
- 1. Introduction
- 2. The Kansei Mind
- 3. Pilot study: Fabric for Clothing
- 4. Semantic Based Images Retrieval
- 5. Semantic Indexing Using Semantic Differential Method (SD)
- 6. Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2016-658-002393412
