Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning. (October 2018)

• Record Type: Journal Article
• Authors: Huang, Jianqing; Liu, Hecong; Dai, Jinghang; Cai, Weiwei
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 218(2018)
• Page Start: 187
• Page End: 193
• Publication Date: 2018-10
• Subjects: Absorption spectroscopy -- Nonlinear tomography -- Convolutional neural networks -- Combustion diagnostics; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2018.07.011
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: l A new inversion method based on convolutional neural networks for nonlinear tomographic absorption spectroscopy is demonstrated. l The effects of network parameters on the performance of neural networks are investigated. l Comparison between CNN and SA is investigated and CNN shows better noise immunity and higher computational efficiency. Abstract: Nonlinear tomographic absorption spectroscopy (NTAS) is an emerging gas sensing technique for reactive flows that has been proven to be capable of simultaneously imaging temperature and concentration of absorbing gas. However, the nonlinear tomographic problems are typically solved with an optimization algorithm such as simulated annealing which suffers from high computational cost. This problem becomes more severe when thousands of tomographic data needs to be processed for the temporal resolution of turbulent flames. To overcome this limitation, in this work we propose a reconstruction method based on convolutional neural networks (CNN) which can take full advantage of the large amount tomographic data to build an efficient neural networks to rapidly predict the reconstruction by feeding the sinograms to it. Simulative studies were performed to investigate how the parameters will affect the performance of neural networks. The results show that CNN can effectively reduce the computational cost and at the same time achieve a similar accuracy level as SA. The successful demonstration CNN in this work indicates possible applications of other sophisticated deep neural networks such as deep belief networks (DBN) and generative adversarial networks (GAN) to nonlinear tomography. © 2018 Elsevier Ltd.