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
In developing or improving a product, gasping context of use is one of the first and important tasks. Accordingly increasing personal mobility accident rates, this study introduces Focused-Day Reconstruction Method (F-DRM) to gather unsafety episode of electric kick scooter users and extract risk factors from those episodes. The proposed F-DRM consist of episode description, perceived dangerous level, risk productenvironmental factor, and so on. 27 participants were recruited and reported 20-40 episodes per person in three weeks. General description of FDRM results is reported, and pair of product factors and environmental factors are clustered for extracting context of risky episodes. Extracting high-rank risk factor pairs, the factor pairs were divided into three clusters through K-means clustering. ANOVA shows significant differences among the clusters based on perceived dangerous level and frequent of factor pairs. In riskiest cluster contains pairs of ‘Wheel’-‘Obstacle’, ‘Wheel’-‘Road surface’, ‘Wheel’-‘Road type’, and ‘Brake’-‘Road type’. These factors are interpreted by detail risk factors to grasp context of risky episode. From the proposed F-DRM, risk factors derived out of the risky episodes are discovered. These results are expected to help further establish experiments to improve the current electric kick scooter design.
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.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2020-004-001167933
