Time-Efficient Neural Architecture Search for Autonomous Underwater Vehicle Fault Diagnosis-Reference-Cited by-同舟云学术

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Time-Efficient Neural Architecture Search for Autonomous Underwater Vehicle Fault Diagnosis

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Pei Shicheng¹^ORCID,Wang Huan²^ORCID,Han Te³^ORCID

Affiliation:

1. Glasgow College, University of Electronic Science and Technology of China, Chengdu, China

2. Department of Industrial Engineering, Tsinghua University, Beijing, China

3. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China

Funder

Innovation Fund of Glasgow College, University of Electronic Science and Technology of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10295529.pdf?arnumber=10295529

Reference37 articles.

1. Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks

2. GASEL: Genetic algorithm-supported ensemble learning for fault detection in autonomous underwater vehicles

3. Observer-based fault detection for magnetic coupling underwater thrusters with applications in jiaolong HOV

4. An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes

5. A review of meta-reinforcement learning for deep neural networks architecture search;jaafra;arXiv 1812 07995,2018

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Fault Diagnostic Approach for Underwater Thrusters Based on Generative Adversarial Network;IEEE Transactions on Instrumentation and Measurement;2024

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