| Issue |
EPJ Web of Conferences
Volume 167, 2018
Plasma Physics by Laser and Applications (PPLA 2017)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 5 | |
| Section | Laser Plasma theoretical aspect-Laser Plasma Effect | |
| DOI | https://doi.org/10.1051/epjconf/201816704002 | |
| Published online | 09 January 2018 | |
https://doi.org/10.1051/epjconf/201816704002
Prediction of strontium bromide laser efficiency using cluster and decision tree analysis
1
Technical University of Sofia - branch Plovdiv, Department of Physics, 25 Tzanko Dujsstabanov str., 4000 Plovdiv, Bulgaria
2
University of Plovdiv Paisii Hilendarski, Faculty of Mathematics and Informatics, 24 Tsar Asen str., 4000, Plovdiv, Bulgaria
* Corresponding author: iliev55@abv.bg
Published online: 9 January 2018
Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others) and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART) technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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