Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Incheol Cho; Kichul Lee; Young Chul Sim; Jae-Seok Jeong; Minkyu Cho; Heechan Jung; Mingu Kang; Yong-Hoon Cho; Seung Chul Ha; Kuk-Jin Yoon; Inkyu Park

doi:10.1038/s41377-023-01120-7

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Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Original Articles

- Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor
- Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor
- 光：科学与应用 2023年12卷第5期页码：826-837
- Affiliations：
  
  1.Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
  2.Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
  3.SENKO Co., Ltd., 485, Oesammi-Dong, Osan-Si, Gyeonggil-Do, 18111, Republic of Korea
- Author bio：
  
  Kuk-Jin Yoon (kjyoon@kaist.ac.kr)
  Inkyu Park (inkyu@kaist.ac.kr)
- Funds：
- DOI：10.1038/s41377-023-01120-7
  中图分类号：
- 纸质出版日期：2023-5-31，
  
  网络出版日期：2023-4-18，
  
  收稿日期：2022-8-17，
  
  修回日期：2023-2-7，
  
  录用日期：2023-2-27
Scan for full text
Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor[J]. 光：科学与应用, 2023,12(5):826-837.

Cho, I. et al. Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor. Light: Science & Applications, 12, 826-837 (2023).
Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor[J]. 光：科学与应用, 2023,12(5):826-837. DOI： 10.1038/s41377-023-01120-7.

Cho, I. et al. Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor. Light: Science & Applications, 12, 826-837 (2023). DOI： 10.1038/s41377-023-01120-7.

摘要

Abstract

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications

such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species

herein

we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor

utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol

ethanol

acetone

and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost

space

and power consumption.

关键词

Keywords

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