[1] Wang Z J, Shuai S J, Li Z J et al. A review of energy loss reduction technologies for internal combustion engines to improve brake thermal efficiency[J]. Energies, 14, 6656(2021).

[2] Churchill S W. The reduction of air pollution by improved combustion[J]. Energy Conversion and Management, 38, 1335-1341(1997).

[3] Anghelescu L, Diaconu B M. Advances in detection and monitoring of coal spontaneous combustion: techniques, challenges, and future directions[J]. Fire, 7, 354(2024).

[4] Ge H, Yun S, Zhang Y et al. Femtosecond chirped-probe-pulse coherent anti-Stokes Raman scattering for thermometry in dynamic combustion fields[J]. Acta Optica Sinica, 44, 2132003(2024).

[5] Li H X, Chen W, Zhang C Y et al. Retrieval of multiple flame parameters based on physics-based neural network and emission spectrum measurement: model development and experimental validation[J]. Acta Optica Sinica, 45, 0130001(2025).

[6] Kaminski C F, Bai X S, Hult J et al. Flame growth and wrinkling in a turbulent flow[J]. Applied Physics B, 71, 711-716(2000).

[7] Ding H, Wang H Q, Wang K. Improved YOLOv3 flame detection algorithm based on dynamic shape feature extraction and enhancement[J]. Laser & Optoelectronics Progress, 59, 2410003(2022).

[8] Kashinath K, Waugh I C, Juniper M P. Nonlinear self-excited thermoacoustic oscillations of a ducted premixed flame: bifurcations and routes to chaos[J]. Journal of Fluid Mechanics, 761, 399-430(2014).

[9] Nori V, Seitzman J. Evaluation of chemiluminescence as a combustion diagnostic under varying operating conditions[C], 953-10(2008).

[10] Cao Z, Gao X, Lu F H et al. Laser absorption spectral tomography for dynamical combustion monitoring[J]. Chinese Journal of Lasers, 49, 1904002(2022).

[11] Ax H, Meier W. Experimental investigation of the response of laminar premixed flames to equivalence ratio oscillations[J]. Combustion and Flame, 167, 172-183(2016).

[12] Hossain A, Nakamura Y. A numerical study on the ability to predict the heat release rate using CH* chemiluminescence in non-sooting counterflow diffusion flames[J]. Combustion and Flame, 161, 162-172(2014).

[13] Sun D, Lu G, Zhou H et al. Quantitative assessment of flame stability through image processing and spectral analysis[J]. IEEE Transactions on Instrumentation and Measurement, 64, 3323-3333(2015).

[14] Floyd J, Kempf A M. Computed tomography of chemiluminescence (CTC): high resolution and instantaneous 3-D measurements of a matrix burner[J]. Proceedings of the Combustion Institute, 33, 751-758(2011).

[15] Floyd J, Geipel P, Kempf A M. Computed tomography of chemiluminescence (CTC): instantaneous 3D measurements and phantom studies of a turbulent opposed jet flame[J]. Combustion and Flame, 158, 376-391(2011).

[16] Worth N A, Dawson J R. Tomographic reconstruction of OH* chemiluminescence in two interacting turbulent flames[J]. Measurement Science and Technology, 24, 024013(2013).

[17] Shen L, Zhu D M, Nadeem S et al. Radiative transport based flame volume reconstruction from videos[EB/OL]. https:∥arxiv.org/abs/1809.06417v1

[18] Chorey D, Jagadale V, Prakash M et al. Simultaneous imaging of CH*, C2*, and temperature in flames using a DSLR camera and structured illumination[J]. Applied Optics, 62, 3737-3746(2023).

[19] Procacci A, Amaduzzi R, Coussement A et al. Computed tomography of chemiluminescence using a data-driven sparse sensing framework[J]. Applied Thermal Engineering, 255, 123918(2024).

[20] Shui Y H, Zhang F H, Gao C et al. FlameGS: reconstruct flame light field via Gaussian Splatting[EB/OL]. https:∥arxiv.org/abs/2412.19841v1

[21] Jin Y, Song Y, Qu X J et al. Three-dimensional dynamic measurements of CH* and C2* concentrations in flame using simultaneous chemiluminescence tomography[J]. Optics Express, 25, 4640-4654(2017).

[22] Jin Y, Zhang W Q, Song Y et al. Three-dimensional rapid flame chemiluminescence tomography via deep learning[J]. Optics Express, 27, 27308-27334(2019).

[23] Zhang F H, Zhang W X, Lei Q C et al. Voxel-free neural volume reconstruction technique for volumetric flame reconstructions[J]. Aerospace Science and Technology, 133, 108107(2023).

[24] Jin Y, Zhu S Y, Wang S Y et al. PENTAGON: physics-enhanced neural network for volumetric flame chemiluminescence tomography[J]. Optics Express, 32, 32732-32752(2024).

[25] Zhu S Y, Jin Y, Zhang X Y et al. Physically enhanced neural network based on projection map synthesis for chemiluminescence tomography reconstruction with limited views[J]. Optics Express, 33, 5840-5855(2025).

[26] Feng S X, Hao X J, Huang X D et al. Fire in SRRN: next-gen 3D temperature field reconstruction technology[EB/OL]. https:∥arxiv.org/abs/2405.06230v1

[27] Zhu S Y, Jin Y, Wu Q Y et al. Hybrid-convolution-based reconstruction for limited-view emission spectrum tomography[J]. Acta Optica Sinica, 42, 1315002(2022).

[28] Zhang F H, Pan H J, Zhang X et al. Three-dimensional reconstruction for flame chemiluminescence field using a calibration enhanced non-negative algebraic reconstruction technique[J]. Optics Communications, 520, 128530(2022).

[29] Wang J J, Tang R. Shape coordinate prediction of multi-core optical fiber based on CNN-LSTM network and attention mechanism[J]. Acta Optica Sinica, 45, 0106003(2025).

[30] Huang J Q, Liu H C, Cai W W. Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning[J]. Journal of Fluid Mechanics, 875, R2(2019).

[31] Niu Z T, Qi H, Sun A T et al. Efficient and robust CNN-LSTM prediction of flame temperature aided light field online tomography[J]. Science China Technological Sciences, 67, 271-284(2024).

[32] Azad R, Asadi-Aghbolaghi M, Fathy M et al. Bi-directional ConvLSTM U-Net with densley connected convolutions[C]. Republic of Korea, 406-415(2019).

[33] Jin Q G, Meng Z P, Sun C M et al. RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans[J]. Frontiers in Bioengineering and Biotechnology, 8, 605132(2020).

[34] Sun G Q, Pan Y Z, Kong W K et al. DA-TransUNet: integrating spatial and channel dual attention with transformer U-Net for medical image segmentation[J]. Frontiers in Bioengineering and Biotechnology, 12, 1398237(2024).

[35] Badea C, Gordon R. Experiments with the nonlinear and chaotic behaviour of the multiplicative algebraic reconstruction technique (MART) algorithm for computed tomography[J]. Physics in Medicine and Biology, 49, 1455-1474(2004).

[36] Jin Y. Research on combustion diagnosis and reconstruction method based on flame emission tomography (FET)[D](2020).

[37] Cai W W, Liu H C, Huang J Q et al. Reconstruction of kHz-rate 3-D flame image sequences from a low-rate 2-D recording via a data-driven approach[J]. Journal of the Optical Society of America B, 37, 3564-3571(2020).

[38] Liu G Y, Zhou Y, Duan Y X et al. Tomographic reconstruction of swirl flame front based on deep learning and chemiluminescence[J]. Journal of Engineering Thermophysics, 46, 467-474(2025).

[39] Gong Y H, Paul G, Sbalzarini I F. Coupled signed-distance functions for implicit surface reconstruction[C], 1000-1003(2012).

[40] Liu X J, Han X, Fang E X et al. Fundus microvascular image segmentation method based on parallel U‐net model[J]. Chinese Journal of Lasers, 51, 2107108(2024).

[41] Shan H L, Liu W X, Wang X T et al. Synthetic aperture radar ship detection method based on highly efficient aggregated feature enhancement network[J]. Acta Optica Sinica, 44, 1228002(2024).

[42] Xiao L, Hu P, Ma J J et al. Self-supervised monocular depth estimation model based on global information correlation under influence of local attention[J]. Laser & Optoelectronics Progress, 62, 0815010(2025).