Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Interior Wind Noise Prediction and Visual Explanation System for Exterior Vehicle Design Using Combined Convolution Neural Networks

Authors
Park, HaEunJung, HoichanLee, Min SeokKim, DoohyungLee, JongwonHan, Sung Won
Issue Date
8월-2022
Publisher
KOREAN SOC AUTOMOTIVE ENGINEERS-KSAE
Keywords
Convolutional neural networks (CNN); Gradient-weighted class activation map (Grad-CAM); Image regression; Wind noise prediction
Citation
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, v.23, no.4, pp.1013 - 1021
Indexed
SCIE
SCOPUS
KCI
Journal Title
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY
Volume
23
Number
4
Start Page
1013
End Page
1021
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/144107
DOI
10.1007/s12239-022-0088-9
ISSN
1229-9138
Abstract
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 (GradCAM). 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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Han, Sung Won photo

Han, Sung Won
공과대학 (산업경영공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE