Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems-Reference-Cited by-同舟云学术

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Interval-Valued Reduced RNN for Fault Detection and Diagnosis for Wind Energy Conversion Systems

Published:2022-07-01 Issue:13 Volume:22 Page:13581-13588
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Mansouri Majdi¹^ORCID,Dhibi Khaled²^ORCID,Hajji Mansour³^ORCID,Bouzara Kais²^ORCID,Nounou Hazem¹^ORCID,Nounou Mohamed⁴^ORCID

Affiliation:

1. Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, Qatar

2. Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, Monastir, Tunisia

3. Higher Institute of Applied Sciences and Technology of Kasserine, University of Kairouan, Kairouan, Tunisia

4. Department of Chemical Engineering Program, Texas A&M University at Qatar, Doha, Qatar

Funder

Qatar National Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9812846/09775808.pdf?arnumber=9775808

Reference51 articles.

1. Convolutional Recurrent Neural Networks forHyperspectral Data Classification

2. A review of deep learning approaches in glove-based gesture classification

3. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks

4. Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review

5. Hybrid System of ART and RBF Neural Networks for Classification of Vibration Signals and Operational States of Wind Turbines

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