Enhanced quantitative elemental analysis in XRF spectroscopy using deep learning fusion network
A multi-energy state attention fusion network is developed to enhance quantitative elemental analysis accuracy by effectively integrating multi-energy X-ray fluorescence spectral data.
Authors
- Lan Rao
- Fu‐Gen Wu (ORCID: https://orcid.org/0000-0003-1773-2868)
- Fu Wang (ORCID: https://orcid.org/0000-0002-4067-4435)
- Yun Teng (ORCID: https://orcid.org/0000-0002-0212-8839)
- Qi Zhang (ORCID: https://orcid.org/0000-0002-5203-8778)
- Qinghua Tian (ORCID: https://orcid.org/0000-0001-5695-4442)
- Xiangjun Xin (ORCID: https://orcid.org/0009-0007-7075-3699)
- Feng Tian (ORCID: https://orcid.org/0000-0001-8747-3959)
- Zhongfei Mu (ORCID: https://orcid.org/0000-0002-5273-7712)
- Ran Gao (ORCID: https://orcid.org/0000-0002-7430-5224)
- Jiajie Li (ORCID: https://orcid.org/0000-0002-5145-2934)
- Yuwen Qin
- Mohai Yue (ORCID: https://orcid.org/0009-0008-1053-8783)
- Yongjun Wang (ORCID: https://orcid.org/0000-0002-9112-816X)
Institutions
- Beijing Institute of Technology (CN)
- Guangdong University of Technology (CN)
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ja00208g
- Primary Topic
- Mineral Processing and Grinding
- Type
- article
- Field-Weighted Citation Impact
- 0.00