Gas identification using electronic nose via gramian-angular-field-based image conversion and convolutional neural networks architecture search-Reference-Cited by-同舟云学术

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Gas identification using electronic nose via gramian-angular-field-based image conversion and convolutional neural networks architecture search

Published:2024-10 Issue: Volume:417 Page:136198
ISSN:0925-4005
Container-title:Sensors and Actuators B: Chemical
language:en
Short-container-title:Sensors and Actuators B: Chemical

Author:

Zhu Yudi,Wang Tao,Li Zhuoheng,Ni Wangze,Zhang Kai,He Tong,Fu Michelle,Zeng Min,Yang Jianhua,Hu Nantao,Cai Wei,Yang Zhi

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Center for High Performance Computing, Shanghai Jiao Tong University

Shanghai Jiao Tong University

China Postdoctoral Science Foundation

Natural Science Foundation of Shanghai Municipality

Publisher

Elsevier BV

Reference33 articles.

1. Portable electronic nose system with elastic architecture and fault tolerance based on edge computing, ensemble learning, and sensor swarm;Wang;Sens. Actuators B Chem.,2023

2. Target discrimination, concentration prediction, and status judgment of electronic nose system based on large-scale measurement and multi-task deep learning;Wang;Sens. Actuators B Chem.,2022

3. A new method of mixed gas identification based on a convolutional neural network for time series classification;Han;Sensors,2019

4. Study on an improved LeNet-5 gas identification structure for electronic noses;Wei;IEEE Sens.,2018

5. GaCNN: Composing CNNs and GAs to Build an Optimized Hybrid Classification Architecture;de Lima Mendes,2021

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