scholarly journals Automatic classification of steel plates based on laser induced breakdown spectroscopy and support vector machines

2010 ◽  
Author(s):  
Francisco Anabitarte ◽  
Jesus Mirapeix ◽  
Olga M. Conde ◽  
Ana M. Cubillas ◽  
Luis Rodriguez-Cobo ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Automatic Classification ◽  
Steel Plates ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Vector Machines ◽  
Laser Induced Breakdown

Classification of steel materials by laser-induced breakdown spectroscopy coupled with support vector machines

2014 ◽  
Vol 53 (4) ◽  
pp. 544 ◽  
Author(s):  
Long Liang ◽  
Tianlong Zhang ◽  
Kang Wang ◽  
Hongsheng Tang ◽  
Xiaofeng Yang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Vector Machines ◽  
Laser Induced Breakdown

scholarly journals Laser-Induced Breakdown Spectroscopy for the Discrimination of Explosives Based on the ReliefF Algorithm and Support Vector Machines

2021 ◽  
Vol 9 ◽  
Author(s):  
Yu Zhao ◽  
Q. Q. Wang ◽  
Xutai Cui ◽  
Geer Teng ◽  
Kai Wei ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Classification Model ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Multivariate Statistical ◽  
Classification Rate ◽  
Breakdown Spectroscopy ◽  
Line Region ◽  
Vector Machines ◽  
Laser Induced Breakdown

Real-time explosive detectors must be developed to facilitate the rapid implementation of appropriate protective measures against terrorism. We report a simple yet efficient methodology to classify three explosives and three non-explosives by using laser-induced breakdown spectroscopy. However, the similarity existing among the spectral emissions collected from the explosives resulted in the difficulty of separating samples. We calculated the weights of lines by using the ReliefF algorithm and then selected six line regions that could be identified from the arrangement of weights to calculate the area of each line region. A multivariate statistical method involving support vector machines was followed for the construction of the classification model. Several models were constructed using full spectra, 13 lines, and 100 lines selected by the arrangement of weights and areas of the selected line regions. The highest correct classification rate of the model reached 100% by using the six line regions.

Interpreting support vector machines applied in laser-induced breakdown spectroscopy

2021 ◽  
pp. 339352
Author(s):  
Erik Képeš ◽  
Jakub Vrábel ◽  
Ondrej Adamovsky ◽  
Sára Střítežská ◽  
Pavlína Modlitbová ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Vector Machines ◽  
Laser Induced Breakdown

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.

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