Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

2018 ◽  
Vol 33 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Partial Least Squares ◽  
Iron Ore ◽  
Support Vector Machine Model ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

A hybrid sparse partial least squares and least-squares support vector machine model was proposed to improve the accuracy of iron ore analysis using LIBS.

scholarly journals Correction: Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

2020 ◽  
Vol 35 (7) ◽  
pp. 1487-1487
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Iron Ore ◽  
Support Vector Machine Model ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model’ by Y. M. Guo et al., J. Anal. At. Spectrom., 2018, 33, 1330–1335, DOI: 10.1039/C8JA00119G.

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 Correction: Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy

2020 ◽  
Vol 35 (7) ◽  
pp. 1498-1498
Author(s):  
Zhihao Zhu ◽  
Jiaming Li ◽  
Yangmin Guo ◽  
Xiao Cheng ◽  
Yun Tang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Laser Induced Breakdown Spectroscopy ◽  
Least Squares Regression ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy’ by Zhihao Zhu et al., J. Anal. At. Spectrom., 2018, 33, 205–209, DOI: 10.1039/C7JA00356K.

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.

scholarly journals A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Optimization Methods ◽  
Parameters Optimization ◽  
Least Square ◽  
Movement Direction ◽  
Support Vector ◽  
Machine Model ◽  
Mode Decomposition

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

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