SVM을 이용한 고속철도 궤도틀림 식별에 관한 연구

• 영문명: A Study on Identification of Track Irregularity of High Speed Railway Track Using an SVM
• 발행기관: 강원대학교 산업기술연구소
• 저자명: 김기동(Kim, Ki-Dong) 황순현(Hwang, Soon-Hyun)
• 간행물 정보: 『산업기술연구』vol.33, 31~40쪽, 전체 10쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2013.09.30

영문 초록

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

목차

1. 서론

2. 관련 연구현황

3. SVM을 이용한 궤도이상상태 분류

4. 실험 및 결과 분석

5. 결론 및 추후 연구 방향

키워드

인공지능 기계학습 지지 벡터 기계 artificial intelligence machine learning support vector machine(SVM)