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.
피인용 0
초록·키워드
Pattern revision in apparel product development is a decision-making process in which practitioners interpret fitting problems and determine appropriate modification strategies. These decisions have nevertheless depended largely on practitioners’ tacit knowledge, and few studies have attempted to represent them as structured, analyzable data. This study proposes a Context-Problem-Decision-Outcome (CPDO) case data structure for recording pattern revision decision-making.
The proposed data structure, which draws on design rationale research and case-based reasoning, represents each pattern revision case through four components: contextual conditions, observed fitting problems, applied modification actions, and evaluation outcomes. To examine its representational capability, we constructed a rule-based synthetic dataset and used it to demonstrate how the data structure can represent pattern revision information and support its retrieval and extension.
The results show that the CPDO data structure represents the relationships among fitting problems, modification actions, and revision outcomes consistently. A case-based similarity search illustrates that similar revision cases can be retrieved using contextual and problem information, so that alternative modification strategies and their associated outcomes can be referenced together. The demonstrations also show that semantic revision information recorded in the CPDO data structure can be linked to a reference CAD pattern to represent the corresponding geometric pattern transformations.
This study conceptualizes pattern revision as a structured, data-driven decision-making process and proposes a foundational case data structure for recording and managing pattern revision knowledge systematically. The proposed structure offers a basis for future studies that use real pattern revision data to support case-based pattern revision and digital pattern management.
The proposed data structure, which draws on design rationale research and case-based reasoning, represents each pattern revision case through four components: contextual conditions, observed fitting problems, applied modification actions, and evaluation outcomes. To examine its representational capability, we constructed a rule-based synthetic dataset and used it to demonstrate how the data structure can represent pattern revision information and support its retrieval and extension.
The results show that the CPDO data structure represents the relationships among fitting problems, modification actions, and revision outcomes consistently. A case-based similarity search illustrates that similar revision cases can be retrieved using contextual and problem information, so that alternative modification strategies and their associated outcomes can be referenced together. The demonstrations also show that semantic revision information recorded in the CPDO data structure can be linked to a reference CAD pattern to represent the corresponding geometric pattern transformations.
This study conceptualizes pattern revision as a structured, data-driven decision-making process and proposes a foundational case data structure for recording and managing pattern revision knowledge systematically. The proposed structure offers a basis for future studies that use real pattern revision data to support case-based pattern revision and digital pattern management.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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