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논문 기본 정보

자료유형
학술저널
저자정보
김보경 (University of Seoul) 이재관 (University of Seoul) 최호식 (University of Seoul) 장서일 (University of Seoul) 이수일 (University of Seoul)
저널정보
한국소음진동공학회 한국소음진동공학회논문집 한국소음진동공학회논문집 제31권 제4호(통권 261호)
발행연도
2021.8
수록면
450 - 458 (9page)
DOI
10.5050/KSNVE.2021.31.4.450

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초록· 키워드

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The investigation of tire-road noise according to the type of road pavement is time-consuming and expensive. In this study, an artificial neural network model was applied to address this problem. Models to classify road pavement types (for example, transverse-tined, longitudinal-tined, NGCS, DG, and SMA) were implemented and their performance were compared. Input data were constructed by combining the features extracted from tire-road noise and road surface images. The tire-road noise collected using the OBSI measurement method was analyzed for the sound pressure level, sound intensity, and sound quality indices. Road surface image data were analyzed using the image feature extraction algorithms of the Hough transformation and histogram of gradient(HOG). The top 10 important variables were selected by inputting each feature into a random forest model, and artificial neural network models were constructed by each feature. The classification accuracy of the model using only acoustic features was 90.8 % and that using only image features was 88.8 %. The accuracy of the model using both features was 97.3 %. The overall classification performance was improved by using the acoustic and image features.

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ABSTRACT
1. 서론
2. 연구방법
3. 연구결과
4. 결론
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