Y-Net: a dual-branch deep learning network for nonlinear absorption tomography with wavelength modulation spectroscopy.

Abstract

This paper demonstrates a new method for solving nonlinear tomographic problems, combining calibration-free wavelength modulation spectroscopy (CF-WMS) with a dual-branch deep learning network (Y-Net). The principle of CF-WMS, as well as the architecture, training and performance of Y-Net have been investigated. 20000 samples are randomly generated, with each temperature or H2O concentration phantom featuring three randomly positioned Gaussian distributions. Non-uniformity coefficient (NUC) method provides quantitative characterizations of the non-uniformity (i.e., the complexity) of the reconstructed fields. Four projections, each with 24 parallel beams are assumed. The average reconstruction errors of temperature and H2O concentration for the testing dataset with 2000 samples are 1.55% and 2.47%, with standard deviations of 0.46% and 0.75%, respectively. The reconstruction errors for both temperature and species concentration distributions increase almost linearly with increasing NUC from 0.02 to 0.20. The proposed Y-Net shows great advantages over the state-of-the-art simulated annealing algorithm, such as better noise immunity and higher computational efficiency. This is the first time, to the best of our knowledge, that a dual-branch deep learning network (Y-Net) has been applied to WMS-based nonlinear tomography and it opens up opportunities for real-time, in situ monitoring of practical combustion environments.