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Sample-efficient LIBS quantitative analysis of steel based on Bayesian convolutional neural networks and active learning

Yu, Haoyang, Fang, Xinyue, Huang, Qian, Jiang, Zhaohui, Pan, Dong, Ma, Qiuying, Gui, Weihua·2026·Spectrochimica Acta Part B: Atomic Spectroscopy
DOI 10.1016/j.sab.2026.107512
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Research works printed but not linked: 28

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Metadata

Authors
Yu, H., Fang, X., Huang, Q., Jiang, Z., Pan, D., Ma, Q., & Gui, W.
Venue
Spectrochimica Acta Part B: Atomic Spectroscopy
Publisher
Elsevier BV
Published
2026-06
Volume
240
Pages
107512
ISSN
0584-8547
DOI
10.1016/j.sab.2026.107512
Cited by
1
Language
en
Licence
Funders
  • Natural Science Foundation of Hunan Province (2025JJ10007)
  • Science and Technology Program of Hunan Province (2024RC1007)
  • National Science and Technology Major Project
  • National Major Science and Technology Projects of China (2025ZD1604602)

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CoChat. (2026). Citation check: Sample-efficient LIBS quantitative analysis of steel based on Bayesian convolutional neural networks and active learning. https://app.cochat.ai/papers/10.1016/j.sab.2026.107512
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Yu, H., Fang, X., Huang, Q., Jiang, Z., Pan, D., Ma, Q., & Gui, W. (2026). Sample-efficient LIBS quantitative analysis of steel based on Bayesian convolutional neural networks and active learning. Spectrochimica Acta Part B: Atomic Spectroscopy. https://doi.org/10.1016/j.sab.2026.107512

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