A hybrid kernel function approach for acoustic reconstruction of temperature distribution

Yu, Yang; Xiong, Qingyu; Li, Qiude; Wu, Chao; Gao, Min; Wang, Kai

doi:10.1016/j.measurement.2020.108238

Cited by 13 publications

(2 citation statements)

References 49 publications

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“…Zhou et al 22 used reflected sigmoidal RBF, and solved the inverse problem by QR decomposition. Yu et al 23 used a hybrid RBF of the exponential kernel and cubic kernel and solved the inverse problem by QR factorization.…”

Section: Introductionmentioning

confidence: 99%

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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Acoustic tomography (AT) is one of a few non-contact measurement techniques that can present information about the temperature distribution. Its successful application greatly depends on the performance of the reconstruction algorithm. In this paper, a temperature distribution reconstruction method based on compressed sensing (CS) is proposed. Firstly, a measurement matrix of an AT system in a CS framework is established. Secondly, a sparse basis is selected based on the mutual coherence between the measurement matrix and sparse basis. Thirdly, an improvement of the orthogonal matching pursuit (OMP) algorithm, called the IMOMP algorithm, is proposed for pursuing efficiency in recovering sparse signals. Reconstruction experiments of Gaussian sparse signals showed that IMOMP was better than OMP in both success ratio and running time, and the selection method of sparse basis was effective. Finally, a temperature distribution reconstruction algorithm based on compressed sensing, that is, the CS-IMOMP algorithm, is proposed. Simulation and experiment results show that, compared with the least square algorithm and the Simultaneous Iterative Reconstruction Technique algorithm, the CS-IMOMP algorithm has smaller reconstruction errors and provides more accurate information about the temperature distribution.

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Section: Introductionmentioning

confidence: 99%

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

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“…Unlike the above-mentioned algorithm, some scholars did not assume that the sound slowness (temperature) distribution in each grid was uniform when developing the mathematical model. Instead, they used a linear combination of RBFs to estimate the sound slowness distribution; i.e., Markov RBF [ 10 , 20 ], inverse multiquadrics RBF [ 21 , 22 ], multiquadric RBF [ 23 ], reflected sigmoidal RBF [ 24 ], hybrid RBF of exponential kernel and cubic kernel [ 25 ] and Gaussian RBF [ 5 ]. This kind of algorithm allows N > M ; in order to address seriously ill-conditioned inverse problems, these algorithms employed methods such as singular value decomposition (SVD), Tikhonov regularization and so on.…”

Section: Introductionmentioning

confidence: 99%

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Wei

Yan

Zhou

2022

Sensors

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A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

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Precise temperature reconstruction in acoustic pyrometry: Impact of domain discretization and transceiver count

Barathula,

S.K.,

Alapati

et al. 2024

Applied Thermal Engineering

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A hybrid kernel function approach for acoustic reconstruction of temperature distribution

Cited by 13 publications

References 49 publications

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Precise temperature reconstruction in acoustic pyrometry: Impact of domain discretization and transceiver count

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