Paper Info
Title
Determining The Composition Of Bronze Alloys By Means Of High-Dimensional Feature Selection And Artificial Neural Networks
Abstract
In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.
Year
DOI
Venue
2015
10.1109/I2MTC.2015.7151598
2015 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC)
Keywords
Field
DocType
artificial neural networks, feature selection, high-dimensional data, laser-induced breakdown spectroscopy
Bronze,Feature selection,Electronic engineering,Artificial intelligence,Artificial neural network,Reference standards,Laser-induced breakdown spectroscopy,Clustering high-dimensional data,Pattern recognition,Engineering,Calibration curve,Machine learning,Calibration
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Eleonora D'Andrea1516.19
Beatrice Lazzerini271545.56
vincenzo palleschi301.01
Stefano Maria Pagnotta461.28