Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra: Wavelet Decomposition and Support Vector Machine Classifier

Jia, Yongyi; Yu, Bin; Du, Min; Wang, Xiaoli

doi:10.2991/iceea-18.2018.28

Cited by 3 publications

(2 citation statements)

References 10 publications

(17 reference statements)

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“…Theoretical and practical applications of this phenomenon are available in the literature [ 250 , 251 , 252 , 253 , 254 , 255 ]. However, at least to the best of our knowledge, vibrational relaxation applied to CO

sensing never went further than the proof of concept presented by Petculescu et al [ 254 ].…”

Section: Review Of Co Sensing Techniquesmentioning

confidence: 99%

Carbon Dioxide Sensing—Biomedical Applications to Human Subjects

Dervieux¹,

Théron

Uhring

2021

Sensors

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Carbon dioxide (CO2) monitoring in human subjects is of crucial importance in medical practice. Transcutaneous monitors based on the Stow-Severinghaus electrode make a good alternative to the painful and risky arterial “blood gases” sampling. Yet, such monitors are not only expensive, but also bulky and continuously drifting, requiring frequent recalibrations by trained medical staff. Aiming at finding alternatives, the full panel of CO2 measurement techniques is thoroughly reviewed. The physicochemical working principle of each sensing technique is given, as well as some typical merit criteria, advantages, and drawbacks. An overview of the main CO2 monitoring methods and sites routinely used in clinical practice is also provided, revealing their constraints and specificities. The reviewed CO2 sensing techniques are then evaluated in view of the latter clinical constraints and transcutaneous sensing coupled to a dye-based fluorescence CO2 sensing seems to offer the best potential for the development of a future non-invasive clinical CO2 monitor.

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sensing never went further than the proof of concept presented by Petculescu et al [ 254 ].…”

Section: Review Of Co Sensing Techniquesmentioning

confidence: 99%

Carbon Dioxide Sensing—Biomedical Applications to Human Subjects

Dervieux¹,

Théron

Uhring

2021

Sensors

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show abstract

“…SVM [110,111,112,113,114,115] attracts considerable attention in gas classification due to its high performance towards small samples and nonlinearity problems of the dataset. Jia et al [111] present a method for gas-composition-unknown recognition by analyzing gas acoustic relaxation absorption spectrum based on wavelet multi-resolution analysis and multi-class SVM. Sujono et al [112] design an asthma identification system by gas sensors and SVM.…”

Section: Gas Sensing Pattern Recognitionmentioning

confidence: 99%

Review on Smart Gas Sensing Technology

Feng

Farha

et al. 2019

Sensors

225

122

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With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

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Multichannel convolution neural network for gas mixture classification

Lim

Lee

et al. 2022

Ann Oper Res

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Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra: Wavelet Decomposition and Support Vector Machine Classifier

Cited by 3 publications

References 10 publications

Carbon Dioxide Sensing—Biomedical Applications to Human Subjects

Carbon Dioxide Sensing—Biomedical Applications to Human Subjects

Review on Smart Gas Sensing Technology

Multichannel convolution neural network for gas mixture classification

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