An Acoustic Hotspot Tracking Algorithm for Highly Centralized Gas Temperature Distribution

Pal, Soma; Lin, Fu-Sung; Hsieh, Ching-Chuan; Liu, Ya-Han; Lu, Chen-Yuan; Du, Shan‐Wen; Huang, Chih-Hsien

doi:10.1109/tuffc.2020.3030541

Cited by 10 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transceiver layout and the sound paths formed are shown in Figure 1. Five typical temperature distributions, that is, single-peak (T1), double-peak (T2), triple-peak (T3), quadruple-peak (T4), and uniform distribution (T5), which are expressed in (15) to (19), were reconstructed using CS-IMOMP, CS-OMP, and the traditional LSA and SIRT algorithms. Kelvin temperature is used in this paper.…”

Section: Reconstruction Of Typical Temperature Distributionsmentioning

confidence: 99%

“…By shifting the distribution of grid cells, more acoustic paths can be involved in the calculation of the slowness function of each grid cell. 19 Acoustic tomography allows the reconstruction of the air temperature distribution, but it is mainly used outdoors due to the challenge of travel-time estimation indoors. To apply AT in buildings, Dokhanchi et al proposed a numerical method to determine an optimal position of transducers, which simultaneously achieves good sound ray coverage of the room and makes the arrival time of the sound rays distinguishable.…”

Section: Introductionmentioning

confidence: 99%

“…By shifting the distribution of grid cells, more acoustic paths can be involved in the calculation of the slowness function of each grid cell. 19…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Reconstruction Of Typical Temperature Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The algorithm adopts the sampling method of spatial movement. More acoustic paths were able to be involved in the calculation of the slowness function of each grid cell, through shifting the distribution of grid cells [ 15 ]. Dokhanchi et al proposed a method to optimize the layout of acoustic transceivers.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Yan

Zhou

2022

Sensors

View full text Add to dashboard Cite

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).

show abstract

Indoor air temperature measurements using ultrasonic travel-time tomography

Dokhanchi,

Othmani,

Arnold

et al. 2024

Applied Acoustics

View full text Add to dashboard Cite

An Acoustic Hotspot Tracking Algorithm for Highly Centralized Gas Temperature Distribution

Cited by 10 publications

References 20 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

Indoor air temperature measurements using ultrasonic travel-time tomography

Contact Info

Product

Resources

About