Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification-Reference-Cited by-同舟云学术

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Disentangled Prototypical Convolutional Network for Few-Shot Learning in In-Vehicle Noise Classification

Published:2024 Issue: Volume:12 Page:66801-66808
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Inho Kee Robin¹^ORCID,Nam Dahyun²^ORCID,Buu Seok-Jun³^ORCID,Cho Sung-Bae⁴^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

2. Department of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea

3. Department of Computer Science, Gyeongsang National University, Jinju, Gyeongsangnam-do, Republic of Korea

4. Department of Computer Science, Yonsei University, Seoul, Republic of Korea

Funder

Yonsei University Research Fund (Postdoctoral Researcher Supporting Program) of 2023

Yonsei Fellow Program funded by Lee Youn Jae

Institute for Information and Communication Technology Planning and Evaluation (IITP) Grant funded by Korea Government [Ministry of Science and ICT (MSIT)]

Air Force Defense Research Sciences Program funded by the Air Force Office of Scientific Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10521661.pdf?arnumber=10521661

Reference30 articles.

1. Recent research on vehicle noise and vibration

2. Impacts of pavement types on in-vehicle noise and human health

3. Interior Noise Simulation for Improved Vehicle Sound

4. The application of the simulation techniques to reduce the noise and vibration in vehicle development;Sang-Hyun

5. A novel approach to optimizing and stabilizing interior noise quality in vehicles

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