H
Hongsheng Tang
Researcher at Northwest University (China)
Publications - 39
Citations - 926
Hongsheng Tang is an academic researcher from Northwest University (China). The author has contributed to research in topics: Laser-induced breakdown spectroscopy & Chemistry. The author has an hindex of 15, co-authored 26 publications receiving 668 citations.
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Journal ArticleDOI
Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods
TL;DR: In this paper, a support vector machine (SVM) and partial least square (PLS) methods were used to perform quantitative and classification analysis of 20 slag samples, and the performance of the SVM calibration model was investigated by 5-fold cross-validation.
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A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR)
TL;DR: In this article, a novel method based on laser induced breakdown spectroscopy (LIBS) and random forest regression (RFR) was proposed for the quantitative analysis of multiple elements in fourteen steel samples.
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Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)
TL;DR: The study presented here demonstrates that LIBS–RF is a useful technique for the identification and discrimination of iron ore samples, and is promising for automatic real-time, fast, reliable, and robust measurements.
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Chemometrics in laser‐induced breakdown spectroscopy
TL;DR: In this article, the authors reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012•2016).
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Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines.
Long Liang,Tianlong Zhang,Kang Wang,Hongsheng Tang,Xiaofeng Yang,Xiaoqin Zhu,Yixiang Duan,Hua Li +7 more
TL;DR: The studies presented here demonstrate that LIBS-SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry.