An Algorithm Based on Transformer and Temporal Convolution Network for Early Identification of Ternary Gas Mixture-Reference-Cited by-同舟云学术

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An Algorithm Based on Transformer and Temporal Convolution Network for Early Identification of Ternary Gas Mixture

Published:2023-10-01 Issue:19 Volume:23 Page:23753-23764
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Yang Ge¹,Song Ruijie¹,Wu Yu¹,Yu Jun²,Zhang Jianwei¹^ORCID,Zhu Huichao¹

Affiliation:

1. Department of Electronic Information and Electrical Engineering, School of Artificial Intelligence, Dalian University of Technology, Dalian, China

2. Department of Medicine, School of Biomedical Engineering, Dalian University of Technology, Dalian, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10269339/10215320.pdf?arnumber=10215320

Reference48 articles.

1. Embedded Artificial Intelligence Approach for Gas Recognition in Smart Agriculture Applications Using Low Cost MOX Gas Sensors

2. The future of plant volatile organic compounds (pVOCs) research: Advances and applications for sustainable agriculture

3. Mobile Detection and Alarming Systems for Hazardous Gases and Volatile Chemicals in Laboratories and Industrial Locations

4. Evolution of breath analysis based on humidity and gas sensors: Potential and challenges

5. Application of an Array of Metal-Oxide Semiconductor Gas Sensors in an Assistant Personal Robot for Early Gas Leak Detection

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1. Methodology for estimating ethanol concentration with artificial intelligence in the presence of interfering gases and measurement delay;Sensors and Actuators B: Chemical;2024-12

2. Integrating Dynamic State Information to Enhance Gas Concentration Modeling Accuracy;IEEE Sensors Journal;2024-06-01

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