Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data

2012 ◽  
Vol 26 (5) ◽  
pp. 143-149 ◽  
Author(s):  
Jessi Cisewski ◽  
Emily Snyder ◽  
Jan Hannig ◽  
Lukas Oudejans
Keyword(s):  
Support Vector Machine ◽  
Spectral Data ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Support Vector Machine Classification ◽  
Laser Induced Breakdown

Identification accuracy improvement for steel species using a least squares support vector machine and laser-induced breakdown spectroscopy

2018 ◽  
Vol 33 (9) ◽  
pp. 1545-1551 ◽  
Author(s):  
Jingjun Lin ◽  
Xiaomei Lin ◽  
Lianbo Guo ◽  
Yangmin Guo ◽  
Yun Tang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Identification Accuracy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Classification Methods ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Two typical classification methods, partial least squares discriminant analysis (PLS-DA) and a support vector machine (SVM), were used to study the classification of steels with similar constituents.

scholarly journals Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1393 ◽  
Author(s):  
Yanwei Yang ◽  
Xiaojian Hao ◽  
Lili Zhang ◽  
Long Ren
Keyword(s):  
Machine Learning ◽  
Iron Ore ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Iron Ores ◽  
K Nearest Neighbor ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.

Quantitative Analysis of Laser-Induced Breakdown Spectroscopy of Heavy Metals in Water Based on Support-Vector-Machine Regression

2013 ◽  
Vol 33 (3) ◽  
pp. 0330002 ◽  
Author(s):  
王春龙 Wang Chunlong ◽  
刘建国 Liu Jianguo ◽  
赵南京 Zhao Nanjing ◽  
马明俊 Ma Mingjun ◽  
王寅 Wang Yin ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Support Vector Machine ◽  
Quantitative Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Breakdown Spectroscopy ◽  
Water Based ◽  
Laser Induced Breakdown

Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

2015 ◽  
Vol 17 (11) ◽  
pp. 964-970 ◽  
Author(s):  
Haiyang Kong ◽  
Lanxiang Sun ◽  
Jingtao Hu ◽  
Yong Xin ◽  
Zhibo Cong
Keyword(s):  
Spectral Data ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Selection Of
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