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Applied Sciences
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Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality
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Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 7081

Special Issue Editor

Prof. Dr. Sang Kwon Lee

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Inha University, Incheon 22212, Korea
Interests: automotive NVH; vibration and acoustic signal processing

Special Issue Information

Dear Colleagues,

This Special Issue focus on application of signal processing to automotive NVH and sound quality. NVH is one of important performances in car engineering. Signal processing tool has been applied to evaluation, analysis, and design of automotive NVH.

Prof. Dr. Sang Kwon Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • active noise control
  • deep neural network
  • blocked force transfer path analysis
  • sound quality
  • ride quality
  • condition monitoring
  • sound camera
  • sound absorption materials
  • electric vehicle
  • tire noise
  • ride and handling
  • active and passive dynamic damper

Published Papers (4 papers)

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Research

14 pages, 1540 KiB  
Article
Fuzzy Adaptive PSO-ELM Algorithm Applied to Vehicle Sound Quality Prediction
by Chenlin Wang, Gongzhuo Yang, Junyu Li and Qibai Huang
Appl. Sci. 2023, 13(17), 9561; https://0-doi-org.brum.beds.ac.uk/10.3390/app13179561 - 24 Aug 2023
Cited by 1 | Viewed by 871
Abstract
When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the [...] Read more.
When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy. Full article
(This article belongs to the Special Issue Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality)
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15 pages, 9211 KiB  
Article
Graph Convolutional Network Surrogate Model for Mesh-Based Structure-Borne Noise Simulation
by Sang-Yun Lee and Sang-Kwon Lee
Appl. Sci. 2023, 13(16), 9079; https://0-doi-org.brum.beds.ac.uk/10.3390/app13169079 - 9 Aug 2023
Viewed by 1085
Abstract
This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape panel vibration and sound pressure was measured in a closed-volume [...] Read more.
This study presents a unique method of building a surrogate model using a graph convolutional network (GCN) for mesh-based structure-borne noise analysis of a fluid–structure coupled system. Structure-borne noise generated from irregular shape panel vibration and sound pressure was measured in a closed-volume cavity coupled with the panel. The proposed network was trained to predict the sound pressure level with three steps. The first step is predicting the natural frequency of panels and cavities using the graph convolutional network, the second step is to predict the averaged vibration and acoustic response of the panel and cavity, respectively, in a given excitation condition using a triangular wave-type inference function based on the natural frequency predicted from the first step, and the third step is to predict the sound pressure in a cavity using a panel and cavity average response as an input to a 2D convolutional neural network (CNN). This method is an efficient way to build a surrogate model for predicting the response of a system which consisted of several sub-systems, like a full vehicle system model. We predicted the response of each sub-system and then combined this to obtain the response of the whole system. Using this method, an average 0.86 r-square value was achieved to predict the panel-induced structure-borne noise in a cavity from 10 to 500 Hz range in 1/12 octave band. This study is the first step towards creating a surrogate model of an engineering system with various sub-systems by changing it into a heterogeneous graph. Full article
(This article belongs to the Special Issue Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality)
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18 pages, 8337 KiB  
Article
A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
by Peilin Ruan, Xu Zheng, Yi Qiu and Zhiyong Hao
Appl. Sci. 2022, 12(23), 12151; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312151 - 28 Nov 2022
Cited by 7 | Viewed by 2154
Abstract
The high-speed train (HST) is one of the most important transport tools in China, and the sound quality of its interior noise affects passengers’ comfort. This paper proposes a HST sound quality model. The model combines Mel-scale frequency cepstral coefficients (MFCCs), the most [...] Read more.
The high-speed train (HST) is one of the most important transport tools in China, and the sound quality of its interior noise affects passengers’ comfort. This paper proposes a HST sound quality model. The model combines Mel-scale frequency cepstral coefficients (MFCCs), the most popular spectral-based input parameter in deep learning models, with convolutional neural networks (CNNs) to evaluate the sound quality of HSTs. Meanwhile, two input channels are applied to simulate binaural hearing so that the different sound signals can be processed separately. The binaural MFCC-CNN model achieves an accuracy of 96.2% and outperforms the traditional shallow neural network model because it considers the time-varying characteristics of noise. The MFCC features are capable of capturing the characteristics of noise and improving the accuracy of sound quality evaluations. Besides, the results suggest that the time and level differences in sound signals are important factors affecting sound quality at low annoyance levels. The proposed model is expected to optimize the comfort of the interior acoustic environment of HSTs. Full article
(This article belongs to the Special Issue Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality)
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13 pages, 5275 KiB  
Article
Classification of Road Surfaces Based on CNN Architecture and Tire Acoustical Signals
by Jinhwan Yoo, Chang-Hun Lee, Hae-Min Jea, Sang-Kwon Lee, Youngsam Yoon, Jaehun Lee, Kiho Yum and Seoung-Uk Hwang
Appl. Sci. 2022, 12(19), 9521; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199521 - 22 Sep 2022
Cited by 9 | Viewed by 1699
Abstract
This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. [...] Read more.
This paper presents a novel work for classification of road surfaces using deep learning method-based convolutional neural network (CNN) architecture. With the development of advanced driver assistance system (ADAS) and autonomous driving technologies, the need for research on vehicle state recognition has increased. However, research on road surface classification has not yet been conducted. If road surface classification and recognition are possible, the control system can make a more robust decision by validating the information from other sensors. Therefore, road surface classification is essential. To achieve this, tire-pavement interaction noise (TPIN) is adopted as a data source for road surface classification. Accelerometers and vision sensors have been used in conventional approaches. The disadvantage of acceleration signals is that they can only represent the surface profile properties and are masked by the resonance characteristics of the car structure. An image signal can be easily contaminated by factors such as illumination, obstacles, and blurring while driving. However, the TPIN signal reflects the surface profile properties of the road and its texture properties. The TPIN signal is also robust compared to those in which the image signal is affected. The measured TPIN signal is converted into a 2-dimensional image through time–frequency analysis. Converted images were used together with a CNN architecture to examine the feasibility of the road surface classification system. Full article
(This article belongs to the Special Issue Recent Automotive Noise Vibration Harshness (NVH) and Sound Quality)
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