Y-Net: a dual-branch deep learning network for nonlinear absorption tomography with wavelength modulation spectroscopy
Y-Net: a dual-branch deep learning network for nonlinear absorption tomography with wavelength modulation spectroscopy
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DOI：
10.1364/oe.448916
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发表时间：
2022-01-17
期刊：
影响因子：
3.8
通讯作者：
Chao, Xing
中科院分区：
文献类型：
--
作者：
Wang, Zhenhai;Zhu, Ning;Chao, Xing
本文展示了一种解决非线性层析成像问题的新方法，将免校准波长调制光谱（CF-WMS）与双分支深度学习网络（Y-Net）相结合。研究了CF-WMS的原理，以及Y-Net的结构、训练和性能。随机生成20000个样本，每个温度或H2O浓度体模具有三个随机定位的高斯分布。非均匀性系数（NUC）方法提供了非均匀性的定量表征（即，复杂度）。假设四个投影，每个投影具有24个平行光束。对2000个样本的测试数据集，温度和H2O浓度的平均重建误差分别为1.55%和2.47%，标准偏差分别为0.46%和0.75%。温度和物种浓度分布的重建误差几乎线性增加，增加NUC从0.02到0.20。与现有的模拟退火算法相比，该算法具有更好的抗噪性和更高的计算效率。据我们所知，这是第一次将双分支深度学习网络（Y-Net）应用于基于WMS的非线性层析成像，它为实际燃烧环境的实时原位监测提供了机会。(C)2022 Optica出版集团根据Optica开放获取出版协议的条款
This paper demonstrates a new method tbr 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. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement