| 제목 |
K겹 교차검증 및 심층신경망을 활용한 자동차 시트의 BSR 소음 지표 예측 연구 |
| 분야 |
진동ㆍ소음 |
| 언어 |
Korean |
| 저자 |
김석범(고등기술연구원), 남재현(고등기술연구원), 고동신(고등기술연구원) |
| Key Words |
BSR(자동차 이음), ANOVA(분산분석), Hold-out cross validation(홀드 아웃 교차 검증), K-fold cross validation(K겹 교차검증), Deep neural network(심층 신경망) |
| 초록 |
This study proposes a method for predicting Loudness N10, a quantitative indicator for evaluating BSR noise in automotive seats. The approach utilizes k-fold cross-validation and deep neural networks(DNNs) to predict the indicator without expensive equipment or specific software. Experimental data on acoustic and sound quality physical quantities were obtained, with significant factors such as sound pressure level and variation intensity identified. While linear and nonlinear regression equations using k-fold cross-validation resulted in large prediction errors, the DNN-based prediction model demonstrated lower errors. The integration of k-fold cross-validation helps maintain performance in limited environments. In summary, the proposed method enables accurate prediction of Loudness N10 based on acoustic and sound quality parameters, even in resource-constrained settings. |
| 원문(PDF) |
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