Inspection of thin-film solar cell processing by laser-induced breakdown spectroscopy and neural networks

2012 ◽  
Author(s):  
David Diego-Vallejo ◽  
Hans Joachim Eichler ◽  
David Ashkenasi
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Solar Cell ◽  
Thin Film Solar Cell ◽  
Laser Induced Breakdown Spectroscopy ◽  
Cell Processing ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

Talanta ◽  
2011 ◽  
Vol 84 (3) ◽  
pp. 730-737 ◽  
Author(s):  
D. Marcos-Martinez ◽  
J.A. Ayala ◽  
R.C. Izquierdo-Hornillos ◽  
F.J. Manuel de Villena ◽  
J.O. Caceres
Keyword(s):  
Neural Networks ◽  
Laser Induced Breakdown Spectroscopy ◽  
Bacterial Strains ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks

2020 ◽  
Vol 12 (10) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yawen Yang ◽  
Chen Li ◽  
Shu Liu ◽  
Hong Min ◽  
Chenglin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Ann Models

In this work, PCA-ANN models of LIBS spectra were developed to classify and identify iron ores according to the production countries and brands.

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

2014 ◽  
Vol 644-650 ◽  
pp. 4722-4725 ◽  
Author(s):  
Hai Yang Kong ◽  
Lan Xiang Sun ◽  
Jing Tao Hu ◽  
Yong Xin ◽  
Zhi Bo Cong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Laser Induced Breakdown ◽  
Reduction Methods ◽  
Artificial Neural ◽  
Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

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