Tunable Diode Laser Absorption Tomography Based on Hierarchical Discretization and Residual Network

SI Jingjing; FU Gengchen; CHENG Yinbo; LIU Chang

doi:10.11999/JEIT210160

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2539-2546

Jiang Yu-Wen, Tan Le-Yi, Wang Shou-Jue. Saliency Detected Model Based on Selective Edges Prior[J]. Journal of Electronics & Information Technology, 2015, 37(1): 130-136. doi: 10.11999/JEIT140119

Citation:

SI Jingjing, FU Gengchen, CHENG Yinbo, LIU Chang. Tunable Diode Laser Absorption Tomography Based on Hierarchical Discretization and Residual Network[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2539-2546. doi: 10.11999/JEIT210160

Jiang Yu-Wen, Tan Le-Yi, Wang Shou-Jue. Saliency Detected Model Based on Selective Edges Prior[J]. Journal of Electronics & Information Technology, 2015, 37(1): 130-136. doi: 10.11999/JEIT140119

Citation:

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Tunable Diode Laser Absorption Tomography Based on Hierarchical Discretization and Residual Network

doi: 10.11999/JEIT210160

SI Jingjing^{1, 4},
FU Gengchen¹,
CHENG Yinbo^{2
,
,},
LIU Chang³

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Ocean College, Hebei Agricultural University, Qinhuangdao 066003, China
3.
School of Engineering, The University of Edinburgh, Edinburgh EH93JL, UK
4.
Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61701429), The Natural Science Foundation of Hebei Province (F2021203027), The Science and Technology Program of Universities and Colleges in Hebei Province (QN2019133)

Received Date: 2021-02-25
Rev Recd Date: 2021-07-19

Available Online: 2021-07-28

Publish Date: 2022-07-25

Abstract

Abstract

Implementation of fast, accurate and adaptable reconstruction is one of the core topics in Tunable Diode Laser Absorption Tomography (TDLAT). In existing algorithms, a certain region at the center of combustion field is usually set as the Region of Interest (RoI). Temperature image of RoI is reconstructed from the absorbance for laser beams passing through the whole tomographic field. It will cause deviations in the reconstructed image. To address this issue, a spatial hierarchical discretization and a Hierarchical Temperature Tomography scheme based on Residual Network (HTT-ResNet) are proposed for TDLAT. It reconstructs the temperature image of the entire combustion field from limited amount of absorbance measurements, and configures optimally computational resources and imaging resolution to describe the temperature distribution in RoI with better spatial resolution. Experiments using random multimodal Gaussian flame models and the measured data of the actual TDLAT system both show that temperature images reconstructed by HTT-ResNet can accurately locate the flame and clearly describe the temperature profile in the combustion field.
- Tunable Diode Laser Absorption Tomography (TDLAT),
- Deep learning,
- Residual Network (ResNet),
- Hierarchical discretization

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References(14)

References

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