Y-Net: a dual-branch deep learning network for nonlinear absorption tomography with wavelength modulation spectroscopy
Zhenhai Wang, Ning Zhu, Weitian Wang, Xing Chao
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 H 2 O 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 H 2 O 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.