Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine

Qingdong ZENG; Guanghui CHEN; Wenxin LI; Zitao LI; Juhong TONG; Mengtian YUAN; Boyun WANG; Honghua MA; Yang LIU; Lianbo GUO; Huaqing YU

doi:10.1088/2058-6272/ac72e3

Plasma Science and Technology > 2022 > 24(8): 084009. > DOI: 10.1088/2058-6272/ac72e3

Qingdong ZENG, Guanghui CHEN, Wenxin LI, Zitao LI, Juhong TONG, Mengtian YUAN, Boyun WANG, Honghua MA, Yang LIU, Lianbo GUO, Huaqing YU. Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine[J]. Plasma Science and Technology, 2022, 24(8): 084009. DOI: 10.1088/2058-6272/ac72e3

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Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine

1.
School of Physics and Electronic-information Engineering, Hubei Engineering University, Xiaogan 432000, People's Republic of China
2.
Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China
3.
Faculty of Physics and Electronic Science, Hubei University, Wuhan 430062, People's Republic of China

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Corresponding author:
Huaqing YU, E-mail: yuhuaqing@hbeu.edu.cn
Received Date: January 10, 2022
Revised Date: May 22, 2022
Accepted Date: May 23, 2022
Available Online: December 10, 2023
Published Date: July 05, 2022

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Abstract

Abstract

In recent years, a laser-induced breakdown spectrometer (LIBS) combined with machine learning has been widely developed for steel classification. However, the much redundant information of LIBS spectra increases the computation complexity for classification. In this work, restricted Boltzmann machines (RBM) and principal component analysis (PCA) were used for dimension reduction of datasets, respectively. Then, a support vector machine (SVM) was adopted to process feature information. Two models (RBM-SVM and PCA-SVM) are compared in terms of performance. After optimization, the accuracy of the RBM-SVM model can achieve 100%, and the maximum dimension reduction time is 33.18 s, which is nearly half of that of the PCA model (53.19 s). These results preliminarily indicate that LIBS combined with RBM-SVM has great potential in the real-time classification of steel.
- laser-induced breakdown spectroscopy,
- restricted Boltzmann machines,
- classification,
- special steel

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61705064), the Natural Science Foundation of Hubei Province (No. 2021CFB607), theNatural Science Foundation of Xiaogan City (No. XGKJ202-1010003), and the Project of the Hubei Provincial Department of Education (No. T201617).

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Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine

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