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第 46 卷 聂仲恒,等: 基于深度学习算法预测燃爆的临界淬熄直径 第 9 期
ZHANG L Z, YAN X Q, LV X S, et al. On quenching characteristics of combustible premixed gas through a crimped-ribbon
flame arrester at different initial pressures [J]. Explosion and Shock Waves, 2023, 43(1): 012202. DOI: 10.11883/bzycj-2021-
4058.
[32] ZHONG X T, GALLAGHER B, EVES K, et al. A study of real-world micrograph data quality and machine learning model
robustness [J]. NPJ Computational Materials, 2021, 7(1): 161. DOI: 10.1038/s41524-021-00616-3.
[33] PEI X L, ZHAO Y H, CHEN L W, et al. Robustness of machine learning to color, size change, normalization, and image
enhancement on micrograph datasets with large sample differences [J]. Materials & Design, 2023, 232: 112086. DOI:
10.1016/j.matdes.2023.112086.
[34] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]//Proceedings of the 31st International
Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc. , 2017: 6000–6010.
[35] WAIBEL A, HANAZAWA T, HINTON G, et al. Phoneme recognition using time-delay neural networks [J]. IEEE
Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(3): 328–339. DOI: 10.1109/29.21701.
[36] 卢祖文. 基于 X 光图像与深度学习的耐张线夹缺陷识别 [D]. 南昌: 南昌大学, 2022. DOI: 10.27232/d.cnki.gnchu.
2022.002699.
LU Z W. Recognition of tension clamp defects based on X-ray image and deep learning [D]. Nanchang: Nanchang University,
2022. DOI: 10.27232/d.cnki.gnchu.2022.002699.
[37] BI Y B, WANG Y B, WANG S L, et al. A hybrid expert neural network for predicting hydrogen concentration under the
ceiling in underground garage [J]. Building and Environment, 2025, 268: 112325. DOI: 10.1016/j.buildenv.2024.112325.
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