[1] [1] HANSON R K. Applications of quantitative laser sensors to kinetics, propulsion and practical energy systems［J］. Proceedings of the Combustion Institute, 2011, 33(1): 1-40.

[2] [2] MA L, LI X S, SANDERS S T, et al. 50-kHz-rate 2D imaging of temperature and H2O concentration at the exhaust plane of a J85 engine using hyperspectral tomography［J］. Optics Express, 2013, 21(1): 1152.

[3] [3] LIU C, XU L J, CHEN J L, et al. Development of a fan-beam TDLAS-based tomographic sensor for rapid imaging of temperature and gas concentration［J］. Optics Express, 2015, 23(17): 22494.

[4] [4] BRKLE S, BIONDO L, DING C P, et al. In-cylinder temperature measurements in a motored IC engine using TDLAS［J］. Flow, Turbulence and Combustion, 2018, 101(1): 139-159.

[5] [5] YU T, CAI W W. Benchmark evaluation of inversion algorithms for tomographic absorption spectroscopy［J］. Applied Optics, 2017, 56(8): 2183-2194.

[6] [6] DAUN K J, WASLANDER S L, TULLOCH B B. Infrared species tomography of a transient flow field using Kalman filtering［J］. Applied Optics, 2011, 50(6): 891-900.

[7] [7] LIU C, XU L J, CAO Z, et al. Reconstruction of two-dimensional temperature distribution in swirling flames using TDLAS-based tomography［C］//2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). May 22-25, 2017, Turin, Italy. IEEE, 2017: 1-4.

[8] [8] MA L, CAI WW, CASWELL A W, et al. Tomographic imaging of temperature and chemical species based on hyperspectral absorption spectroscopy［J］. Optics Express, 2009, 17(10): 8602-8613.

[9] [9] ZHANG Z R, SUN P S, PANG T, et al. Reconstruction of combustion temperature and gas concentration distributions using line-of-sight tunable diode laser absorption spectroscopy［J］. Optical Engineering, 2016, 55: 076107.

[11] [11] CAI W W, EWING D J, MA L. Investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography［J］. Applied Mathematics and Computation, 2011, 217(12): 5754-5767.

[12] [12] HUANG J Q, LIU H C, DAI J H, et al. Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning［J］. Journal of Quantitative Spectroscopy and Radiative Transfer, 2018, 218: 187-193.

[13] [13] LECUN Y, BENGIO Y, HINTON G. Deep learning［J］. Nature, 2015, 521(7553): 436-444.

[14] [14] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［J］. Communications of the ACM, 2017, 60(6): 84-90.

[15] [15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［EB/OL］. （2015-04-10）［2020-02-12］.https://arxiv.org/pdf/1409.1556.pdf.2014.

[16] [16] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition［C］//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.

[17] [17] ZHANG H M, LI L, QIAO K, et al. Image prediction for limited-angle tomography via deep learning with convolutional neural network［EB/OL］. (2016-07-29)［2020-02-13］.https://arxiv.org/ftp/arxiv/papers/1607/1607.08707.pdf.2016.

[19] [19] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks［C］//Computer Vision-ECCV 2014, Berlin:Springer, 2014:818-833.