Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detec...

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Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 11; s. 3601
Hlavní autori: Jang, Hee-Deok, Kwon, Seokjoon, Nam, Hyunwoo, Chang, Dong Eui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 03.06.2024
MDPI
Predmet:
chemical gas classification
chemical warfare agent
Chemical weapons
Classification
Communication
Datasets
Deep learning
deep neural network
Fourier transform infrared
Fourier transforms
Gases
Lee, J.H
Machine learning
Neural networks
semi-supervised autoencoder
Spectrum analysis
semi-supervised autoencoder
chemical gas classification
Fourier transform infrared
chemical warfare agent
deep neural network
ISSN:1424-8220, 1424-8220
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Shrnutí:Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24113601