A Dual-input Fault Diagnosis Model Based on Convolutional Neural Networks and Gated Recurrent Unit Networks for Analog Circuits-Reference-Cited by-同舟云学术

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A Dual-input Fault Diagnosis Model Based on Convolutional Neural Networks and Gated Recurrent Unit Networks for Analog Circuits

Published:2021-05-17 Issue: Volume: Page:
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Container-title:2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
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Author:

Gao Tianyu,Yang Jingli,Jiang Shouda,Yang Cheng

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9459717/9459787/09459990.pdf?arnumber=9459990

Cited by 5 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. An incipient fault diagnosis method based on Att-GCN for analogue circuits;Measurement Science and Technology;2023-01-06

2. A Block PatchMatch-Based Energy-Resource Efficient Stereo Matching Processor on FPGA;IEEE Transactions on Circuits and Systems I: Regular Papers;2022-07

3. Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities;2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS);2022-06-13

4. Application of a Pattern-Recognition Neural Network for Detecting Analog Electronic Circuit Faults;Mathematics;2021-12-15

5. Dadu-Eye: A 5.3 TOPS/W, 30 fps/1080p High Accuracy Stereo Vision Accelerator;IEEE Transactions on Circuits and Systems I: Regular Papers;2021-10

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