[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Random forests for statistical modeling of experimental data for CuBr vapor lasers used as brightness amplifiers

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This study demonstrates the high capabilities of data mining and the random forest (RF) machine learning technique for processing experimental data in the field of laser equipment and technology and extracting significant information from these. The subject of study is the copper bromide vapor laser, used as a brightness amplifier and as an active medium in active optical systems actively developed in recent years. Published data from 465 experiments on this type of laser are statistically examined. RF regression models are built to predict the output power as a basic laser characteristic. The dependence of the output power on the input electric power, the pulse repetition frequency, the pressure of the additional gases in the discharge, and other operating and geometric parameters of the laser is determined. The models fit up to 98% of the experimentally measured laser output power data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Webb, C., Jones, J. (eds.): Handbook of Laser Technology and Applications, vol. 2. Institute of Physics Publishing, New York (2004)

    Google Scholar 

  2. Little, C.E.: Metal Vapour Lasers: Physics, Engineering and Applications. Wiley, Chichester (1999)

    Google Scholar 

  3. Sabotinov, N.V.: Metal vapor lasers. In: Endo, M., Walter, R.F. (eds.) Gas lasers, pp. 449–494. CRC Press, Boca Raton (2006)

    Google Scholar 

  4. Tanzi, E.L., Lupton, J.R., Alster, T.S.: Lasers in dermatology: four decades of progress. J. Am. Acad. Dermatol. 49(1), 1–34 (2003). https://doi.org/10.1067/mjd.2003.582

    Article  Google Scholar 

  5. Eimpunth, S., Wanitphakdeedecha, R., Triwongwaranat, D., Varothai, S., Manuskiatti, W.: Therapeutic outcome of melasma treatment by dual-wavelength (511 and 578 nm) laser in patients with skin phototypes III–V. Clin. Exp. Dermatol. 39(3), 292–297 (2014). https://doi.org/10.1111/ced.12267

    Article  Google Scholar 

  6. Steen, W.M., Mazumder, J.: Laser Material Processing. Springer, London (2010)

    Book  Google Scholar 

  7. Evtushenko, G.S. (ed.): Methods and Instruments for Visual and Optical Diagnostics of Objects and Fast Processes. Nova Science, New York (2018)

    Google Scholar 

  8. Cheng, C., Sun, W.: Study on the kinetic mechanisms of copper vapor lasers with hydrogen-neon admixtures. Opt. Commun. 144(1–3), 109–117 (1997). https://doi.org/10.1016/S0030-4018(97)00328-3

    Article  Google Scholar 

  9. Boichenko, A.M., Evtushenko, G.S., Nekhoroshev, V.O., Shiyanov, D.V., Torgaev, S.N.: CuBr−Ne−HBr laser with a high repetition frequency of the lasing pulses at a reduced energy deposition in the discharge. Phys. Wave Phenomena 23(1), 1–13 (2015). https://doi.org/10.3103/S1541308X1501001X

    Article  Google Scholar 

  10. Torgaev, S.N., Boichenko, A.M., Evtushenko, G.S., Shiyanov, D.V.: Simulation of a CuBr–Ne–HBr laser with high pump pulse repetition frequencies. Russ. Phys. J. 55(9), 1039–1045 (2013). https://doi.org/10.1007/s11182-013-9919-5

    Article  Google Scholar 

  11. Iliev, I.P., Gocheva-Ilieva, S.G.: Model of the radial gas temperature distribution in a copper bromide vapour laser. Quantum Electron. 40(6), 479–483 (2010). https://doi.org/10.1070/QE2010v040n06ABEH014201

    Article  Google Scholar 

  12. Iliev, I.P., Gocheva-Ilieva, S.G.: Study on the maximum electric power supplied to copper bromide vapor lasers. J. Comput. Electron. 19, 1187–1191 (2020). https://doi.org/10.1007/s10825-020-01490-w

    Article  Google Scholar 

  13. Denev, N.P., Iliev, I.P.: Second degree model of laser efficiency of a copper bromide laser. AIP Conf. Proc. 1561, 92–99 (2013). https://doi.org/10.1063/1.4827218

    Article  Google Scholar 

  14. Gocheva-Ilieva, S.G., Iliev, I.P.: Statistical Models of Characteristics of Metal Vapor Lasers. Nova Science, New York (2011)

    Google Scholar 

  15. Iliev, I.P., Voynikova, D.S., Gocheva-Ilieva, S.G.: Simulation of the output power of copper bromide lasers by the MARS method. Quantum Electron. 42(4), 298–303 (2012). https://doi.org/10.1070/QE2012v042n04ABEH014808

    Article  Google Scholar 

  16. Iliev, I.P., Voynikova, D.S., Gocheva-Ilieva, S.G.: Application of the classification and regression trees for modeling the laser output power of a copper bromide vapor laser. Math. Probl. Eng. 2013, 654845 (2013). https://doi.org/10.1155/2013/654845

    Article  Google Scholar 

  17. Evtushenko, G.S., Trigub, M.V., Gubarev, F.A., Evtushenko, T.G., Torgaev, S.N., Shiyanov, D.V.: Laser monitor for non-destructive testing of materials and processes shielded by intensive background lighting. Rev. Sci. Instrum. 85(3), 033111 (2014). https://doi.org/10.1063/1.4869155

    Article  Google Scholar 

  18. Trigub, M.V., Torgaev, S.N., Evtushenko, G.S., Drobchik, V.V.: Laser monitors for high speed imaging of plasma, beam and discharge processes. Key Eng. Mater. 712, 303–307 (2016). https://doi.org/10.4028/www.scientific.net/KEM.712.303

    Article  Google Scholar 

  19. Li, L., Ilyin, A.P., Gubarev, F.A., Mostovshchikov, A.V., Klenovskii, M.S.: Study of self-propagating high-temperature synthesis of aluminium nitride using a laser monitor. Ceram. Int. 44(16), 19800–19808 (2018). https://doi.org/10.1016/j.ceramint.2018.07.237

    Article  Google Scholar 

  20. Li, L., Mostovshchikov, A.V., Ilyin, A.P., Antipov, P.A., Shiyanov, D.V., Gubarev, F.A.: Imaging system with brightness amplification for a metal-nanopowder-combustion study. J. Appl. Phys. 127(19), 194503 (2020). https://doi.org/10.1063/1.5139508

    Article  Google Scholar 

  21. Shiyanov, D.V., Evtushenko, G.S., Sukhanov, V.B., Fedorov, V.F.: Effect of gas mixture composition and pump conditions on the parameters of the CuBr–Ne–H2 (HBr) laser. Quantum Electron. 37(1), 49–52 (2007). https://doi.org/10.1070/QE2007v037n01ABEH013217

    Article  Google Scholar 

  22. Gubarev, F.A., Sukhanov, V.B., Evtushenko, G.S., Fedorov, V.F., Shiyanov, D.V.: CuBr laser excited by a capacitively coupled longitudinal discharge. IEEE J. Quantum Electron. 45(2), 171–177 (2009). https://doi.org/10.1109/JQE.2008.2002502

    Article  Google Scholar 

  23. Gubarev, F.A., Evtushenko, G.S., Vuchkov, N.K., Sukhanov, V.B., Shiyanov, D.V.: Modeling technique of capacitive discharge pumping of metal vapor lasers for electrode capacitance optimization. Rev. Sci. Instrum. 83(5), 055111 (2012). https://doi.org/10.1063/1.4719920

    Article  Google Scholar 

  24. Gubarev, F.A., Shiyanov, D.V., Sukhanov, V.B., Evtushenko, G.S.: Capacitive-discharge-pumped CuBr laser with 12 W average output power. IEEE J. Quantum Electron. 49(1), 89–94 (2013). https://doi.org/10.1109/JQE.2012.2227952

    Article  Google Scholar 

  25. Shiyanov, D.V., Sukhanov, V.B., Gubarev, F.A.: Influence of peaking capacitance on the output power of capacitive-discharge-pumped metal halide vapor lasers. IEEE J. Quantum Electron. 54(2), 1500107 (2018). https://doi.org/10.1109/JQE.2018.2806943

    Article  Google Scholar 

  26. Gubarev, F.A., Shiyanov, D.V., Sukhanov, V.B.: Capacitive-discharge-pumped copper bromide vapor laser with output power up to 15 W. In: 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), Toyama, 8597961, pp. 1909–1914 (2018). https://doi.org/https://doi.org/10.23919/PIERS.2018.8597961

  27. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  28. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  29. Izenman, A.J.: Modern Multivariate Statistical Techniques Regression, Classification, and Manifold Learning. Springer, New York (2008)

    MATH  Google Scholar 

  30. IBM SPSS Statistics software: https://www.ibm.com/analytics/spss-statistics-software

  31. Salford Predictive Modeler: https://www.minitab.com/en-us/products/spm/

Download references

Acknowledgements

This work has been carried out with financial support from the MES through grant no. D01-271/16.12.2019 for NCDSC part of the Bulgarian National Roadmap on RIs. The second author was supported by grant no. MU19-FMI-010 of NPD at University of Plovdiv Paisii Hilendarski, financed by the Bulgarian Ministry of Education and Science.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics and Modeling, Faculty of Mathematics and Informatics, University of Plovdiv Paisii Hilendarski, 24 Tzar Asen St, 4000, Plovdiv, Bulgaria

    Atanas Valev Ivanov, Dimitar Vaskov Fidanov & Snezhana Georgieva Gocheva-Ilieva

Authors
  1. Atanas Valev Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitar Vaskov Fidanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Snezhana Georgieva Gocheva-Ilieva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atanas Valev Ivanov.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ivanov, A.V., Fidanov, D.V. & Gocheva-Ilieva, S.G. Random forests for statistical modeling of experimental data for CuBr vapor lasers used as brightness amplifiers. J Comput Electron 20, 958–965 (2021). https://doi.org/10.1007/s10825-020-01652-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-020-01652-w

Keywords

Search

Navigation