Multi-relaxation time model and its application in construction of gas acoustic attenuation spectrum

Jia, Yanping; Wang, S.; Zhu, Ming; Yuan, Fengling

doi:10.1007/s12648-013-0247-y

Cited by 2 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EXPERIMENTAL DATA the experimental data from [10] (circles and squares), the GARAS curves calculated from Dain's theory [2,3] (the dash lines) and our previous work [11,12] (the solid lines). The experimental data shows the gas acoustic relaxation absorption coefficient varying with frequency, and it can be represented by GARAS curves obtained from several acoustic absorption theories as shown in Figure 1.…”

Section: Garas From Theoretical Models Andmentioning

confidence: 99%

See 1 more Smart Citation

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

Jia¹,

Yu²,

Du³

et al. 2018

Proceedings of the 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)

View full text Add to dashboard Cite

Abstract-Gas acoustic spectrum represents properties of acoustic propagation, which can distinguish gas compositions. However in few existing methods of gas-composition-unknown recognition, the approaches of programmatically processing the acoustic spectrum curves have not yet been presented. We propose a method for gas-composition-unknown recognition by analyzing gas acoustic relaxation absorption spectrum (GARAS) based on wavelet multi-resolution analysis (MRA) and multi-class support vector machine (SVM). Features of GARAS are extracted by wavelet MRA, and then selected to obtain a few feature coefficients which are utilized to train and test multi-class SVM. Simulation results show that the proposed method completely classifies four examples of gas mixtures, and that it recognizes the mixtures with the same and similar concentration or temperature. This method realizes the numerically extracting and programmatically processing the information of GARAS, and implements gas-composition-unknown sensing based on acoustic spectrums.

show abstract

Section: Garas From Theoretical Models Andmentioning

confidence: 99%

“…The GARAS curves are obtained from our theoretical model [11,12]. In this paper, four sorts of gas mixtures, air-CO2, air-CO, air-CH4 and air are taken as examples to validate the proposed method, in which the composition proportion of air is given as 03 …”

Section: Garas From Theoretical Models Andmentioning

confidence: 99%

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

Jia¹,

Yu²,

Du³

et al. 2018

Proceedings of the 2018 2nd International Conference on Electrical Engineering and Automation (ICEEA 2018)

View full text Add to dashboard Cite

show abstract

“…Petculescu [2,3] proposed future trend of gas sensing based on ultrasonic spectrum, which indicated that contaminant-unknown gas can be distinguished by comparing acoustic properties of candidates and the contaminant gas. Then Jia [4] successfully implemented programmatically recognizing gas mixtures by analyzing gas acoustic relaxation absorption and perfectly reached the recognition rate of 100%.…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet decomposition is a powerful tool to extract characteristics of curves and support vector machine (SVM) is a popular way in solving classification problems [5,6]. Jia firstly applied wavelet decomposition to extract the detail coefficients of the curves [4]. Secondly they extracted feature coefficients (for training and testing [7] ) from the detail coefficients and approximation coefficients, which is implemented by calculating mean, standard deviation, energy and entropy values at each decomposition level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Wavelet Decomposition on Acoustic Spectrums for Gas Composition Sensing

Zhu

Liu

2014

AMM

View full text Add to dashboard Cite

Wavelet decomposition on acoustic spectrums is a novel method in the field of gas composition sensing. However, the existing method doesn’t explain the relationship between the characteristics of acoustic gas spectrums and the results of wavelet decomposition, and it has high computational complexity. In this paper, we firstly find it can basically keep the recognition rate if the detail coefficients from wavelet decomposition are used to construct samples instead of characteristic coefficients extracted from the detail ones. Nevertheless, the detail coefficients can represent the properties of acoustic gas spectrums, which can’t be represented by characteristic coefficients. Then, we find points of detail coefficients with relatively high recognition rate are mainly distributed in the first three layers. Therefore, it can reduce the computational complexity when three-level decomposition is directly utilized in the method of gas composition sensing.

show abstract

Multi-relaxation time model and its application in construction of gas acoustic attenuation spectrum

Cited by 2 publications

References 18 publications

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

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

Analysis of Wavelet Decomposition on Acoustic Spectrums for Gas Composition Sensing

Contact Info

Product

Resources

About