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.
초록·키워드
The identification and classification of railway turnout faults are essential for guaranteeing train safety. Traditional diagnostic methods for these faults face challenges due to limited accuracy, stemming from the scarcity of fault samples, and often fail to provide detailed fault classification. In response to these issues, we introduce an advanced two‐stage model for the classification of railway turnout faults, utilizing the FastDTW algorithm, known for its efficient approximation of DTW (dynamic time warping) with linear time and space complexity. In the first stage, we employ a Shapelets feature extraction algorithm, based on a greedy strategy, to efficiently identify the most representative segments from long sequence action curves. Progressing to the second stage, the model tackles the inherent singularities in the FastDTW algorithm by incorporating a novel curve segmentation technique, also rooted in a greedy strategy. This technique fine‐tunes the fault classification process, leading to more accurate outcomes. The effectiveness and precision of our proposed model were validated empirically using a dataset of 540 faulty curves from a specific high‐speed railway station, achieving an impressive classification accuracy of 97%. This substantial accuracy in fault curve classification underscores the potential of our model to significantly enhance the safety and efficiency of railway operations, marking a notable advancement in the field of railway turnout fault classification.
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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