Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks-Reference-Cited by-同舟云学术

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Robust Classification of Largely Corrupted Electronic Nose Data Using Deep Neural Networks

Published:2021-02-15 Issue:4 Volume:21 Page:5052-5059
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Yoo YoungJoon,Kim Hyun-Il,Choi Sang-Il^ORCID

Funder

Dankook University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9328368/09240952.pdf?arnumber=9240952

Cited by 22 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. RLCSA-Net: a new deep learning method combined with an electronic nose to identify the quality of tea;Journal of Food Measurement and Characterization;2024-07

2. AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture;ACS Sensors;2024-06-05

3. Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-06

4. An Adaptive Deep Learning Method Combined With an Electronic Nose System for Quality Identification of Soybeans Storage Period;IEEE Sensors Journal;2024-05-01

5. FTM-GCN: A novel technique for gas concentration predicting in space with sensor nodes;Sensors and Actuators B: Chemical;2024-01

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