A denoising autoencoder based on U-Net and bidirectional long short-term memory for multi-level random telegraph signal analysis-Reference-Cited by-同舟云学术

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A denoising autoencoder based on U-Net and bidirectional long short-term memory for multi-level random telegraph signal analysis

Published:2024-09 Issue: Volume:135 Page:108685
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Deng Bowen^ORCID,Yang HeeBong^ORCID,Kim Na Young^ORCID

Funder

Government of Canada

Industry Canada

Ontario Ministry of Research, Innovation and Science

Publisher

Elsevier BV

Reference59 articles.

1. Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications;Abrahart,2008

2. Deep learning using rectified linear units (relu);Agarap,2018

3. Deep convolutional neural networks: A survey of the foundations, selected improvements, and some current applications;Ankile,2020

4. Multi-trap RTN parameter extraction based on Bayesian inference;Awano,2013

5. The 1/f noise and random telegraph noise characteristics in floating-gate nand flash memories;Bae;IEEE Trans. Electron Devices,2009

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