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딥 러닝을 이용한 노면 거칠기 분류 로직 개발Development of Road Roughness Classification Logic using Deep Learning
- Other Titles
- Development of Road Roughness Classification Logic using Deep Learning
- Issue Date
- Nov-2017
- Publisher
- 한국자동차공학회
- Keywords
- Convolutional Neural Network(컨벌루션 신경망); Feature selection(중요 피쳐 선정); Road roughness Classification(노면 상태 분류); Importance weight layer(중요 가중치 층)
- Citation
- 2017년 한국자동차공학회 추계학술대회 및 전시회, pp.489 - 491
- Indexed
- OTHER
- Journal Title
- 2017년 한국자동차공학회 추계학술대회 및 전시회
- Start Page
- 489
- End Page
- 491
- ISSN
- 2713-7171
- Abstract
- It is important to classify road condition to establish suspension control strategy. Today, using vertical acceleration sensor of sprung mass and unsprung mass is most commonly used method to classify road condition. However, when using only vertical acceleration sensor, it is hard to classify exact road condition. So, in this study, the logic which is used deep learning to classify road condition is proposed. Deep learning is a technology used to classify objects or data by learning artificial neural networks designed with multi-layer structure. It has advantage of increasing accuracy of road condition classification result by considering vehicle’s IMU sensors at once. IMU sensors are used as features which are deep learning input. To reduce network size and secure classification accuracy, feature selection method is proposed
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Collections - 서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles
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Related Researcher
- Huh, Kunsoo
- COLLEGE OF ENGINEERING (DEPARTMENT OF AUTOMOTIVE ENGINEERING)