Evaluating practical adversarial robustness of fault diagnosis systems via spectrogram-aware ensemble method-Reference-Cited by-同舟云学术

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Evaluating practical adversarial robustness of fault diagnosis systems via spectrogram-aware ensemble method

Published:2024-04 Issue: Volume:130 Page:107980
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Kim Hoki^ORCID,Lee Sangho^ORCID,Lee Jaewook,Lee Woojin^ORCID,Son Youngdoo

Publisher

Elsevier BV

Reference45 articles.

1. Fault severity classification of ball bearing using SinGAN and deep convolutional neural network;Akhenia;Proc. Inst. Mech. Eng. C,2022

2. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling;Bai,2018

3. An improved fault diagnosis using 1d-convolutional neural network model;Chen;Electronics,2020

4. A mutually boosting dual sensor computational camera for high quality dark videography;Cheng;Inf. Fusion,2023

5. Exploring diverse feature extractions for adversarial audio detection;Choi;IEEE Access,2023

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