| INTERIOR WIND NOISE PREDICTION AND VISUAL EXPLANATION SYSTEM FOR EXTERIOR VEHICLE DESIGN USING COMBINED CONVOLUTION NEURAL NETWORKS
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| HaEun Park 1, Hoichan Jung 1, Min Seok Lee 1, Doohyung Kim 1, Jongwon Lee 2, Sung Won Han 1 |
1School of Industrial and Management Engineering, Korea University
2NVH Research Lab, Hyundai Motor Company |
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| ABSTRACT |
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An analytical model configuration, in addition to air pressure analysis and post-processing, was conducted to
measure the interior wind noise by changing the exterior vehicular design. Although wind noise can be calculated accurately
through the current process, it requires three to five days for each design. In this study, a convolutional neural network (CNN),
which is a class of deep neural networks designed for processing image data, was applied to predict the wind noise with vehicle
design images from four different views. Feature maps were extracted from the CNN models trained with images of each view
and concatenated to flow through a sequence of fully connected (FC) layers to predict the wind noise. Moreover, visualization
of the significant vehicle parts for wind noise prediction was provided using a gradient-weighted class activation map (Grad-
CAM). Finally, we compared the performance of various CNN-based models, such as ResNet, DenseNet, and EfficientNet, in
addition to the architecture of the FC layers. The proposed method can predict the wind noise using vehicle images from
different views with a root-mean-square error (RMSE) value of 0.206, substantially reducing the time and cost required for
interior wind noise estimation. |
| Key Words:
Wind noise prediction, Image regression, Convolutional neural networks (CNN), Gradient-weighted class activation map (Grad-CAM)
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Corresponding Author.
Sung Won Han , Email. 
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