PSCFormer: A lightweight hybrid network for gas identification in electronic nose system-Reference-Cited by-同舟云学术

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PSCFormer: A lightweight hybrid network for gas identification in electronic nose system

Published:2024-01 Issue: Volume:145 Page:109912
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Li Ziyang,Kang Siyuan,Feng Ninghui,Yin Chongbo,Shi Yan

Funder

Natural Science Foundation of Jilin Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. Portable food-freshness prediction platform based on colorimetric barcode combinatorics and deep convolutional neural networks;Guo;Adv. Mater.,2020

2. Lightweight residual convolutional neural network for soybean classification combined with electronic nose;Lin;IEEE Sens. J.,2022

3. Lightweight interleaved residual dense network for gas identification of industrial polypropylene coupled with an electronic nose;Shi;IEEE Trans. Instrum. Meas.,2021

4. Electronic noses in medical diagnostics;Wojnowski;Curr. Med. Chem.,2019

5. Evaluation of semanotus bifasciatus (Motschulsky) infestation in platycladus orientalis plants using E-nose and GC–MS;Wang;Trans. ASABE,2020

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3. Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders;ACS Sensors;2024-09-09

4. Advancing cross-subject olfactory EEG recognition: A novel framework for collaborative multimodal learning between human-machine;Expert Systems with Applications;2024-09

5. Identification of the quality for rice with different storage humidity: An electronic nose combined with multiblock feature integration method;Measurement;2024-09

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