A priori information-guided generative adversarial network for data augmentation: application to pipeline fault diagnosis-Reference-Cited by-同舟云学术

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A priori information-guided generative adversarial network for data augmentation: application to pipeline fault diagnosis

Published:2024-04-30 Issue:1 Volume:12 Page:
ISSN:2164-2583
Container-title:Systems Science & Control Engineering
language:en
Short-container-title:Systems Science & Control Engineering

Author:

Guan Chuang¹²³⁴,Shang Rou¹²³⁴,Yang Fan¹²³⁴,Shao Ao-xiang¹²³⁴,Zhang Songyuan¹²³⁴

Affiliation:

1. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, People's Republic of China

2. The Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, People's Republic of China

3. The Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, People's Republic of China

4. National Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing, Heilongjiang, People's Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2024.2343311

Reference58 articles.

1. Infant cry classification by MFCC feature extraction with MLP and CNN structures

2. A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

3. Ali-Gombe A. Elyan E. & Jayne C. (2019). Multiple fake classes GAN for data augmentation in face image dataset. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.

4. Automatic spoken language identification using MFCC based time series features

5. Generative Adversarial Networks in Time Series: A Systematic Literature Review

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