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.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2012.10
- 수록면
- 1,118 - 1,122 (5page)
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초록· 키워드
오류제보하기
An urban road surface monitoring system is developed to monitor highway conditions. This paper uses principle component analysis (PCA) that combines 94 GHz dual-channel polarimetric radiometer and automatic weather station measurements. The goal of this paper is to combine these observations into a smaller number of variables through PCA. Each new variable has a unique physical interpretation. In particular, we are concerned with principal component variables whose weightings include brightness temperature and road surface temperature. Then, four different road surface classes (dry, wet, snowy, and icy) are classified by Bayesian classification. The novelty of our approach relies on using 94 GHz dual-channel polarimetric radiometer is used to investigate behavior of the brightness temperature of different road surface conditions in an open-air laboratory. On the other hand, the median absolute deviation algorithm is used to remove extreme values automatically from a given database of weather information (including brightness temperature, road surface temperature, wind speed, etc.) for robust classification. Finally, the road surface statuses were found to be well classified by the proposed method in real time.
목차
Abstract
1. INTRODUCTION
2. MEDIAN ABSOLUTE DEVIATION ALGORITHM
3. EXPERIMENTAL SETTINGS
4. EXPERIMENT RESULTS AND DISUCSSIONS
5. CONCLUSIONS
REFERENCES
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