Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2010, Article ID 697687, 15 pages
http://dx.doi.org/10.1155/2010/697687
Research Article

Parametric and Nonparametric Empirical Regression Models: Case Study of Copper Bromide Laser Generation

1Department of Applied Mathematics and Modeling, Faculty of Mathematics and Informatics, University of Plovdiv “Paisii Hilendarski”, 24 Tzar Assen Street, 4000 Plovdiv, Bulgaria
2Department of Physics, Technical University of Sofia, Branch Plovdiv, 25 Tz. Djusstabanov Street, 4000 Plovdiv, Bulgaria

Received 26 December 2009; Accepted 2 March 2010

Academic Editor: J. Jiang

Copyright © 2010 S. G. Gocheva-Ilieva and I. P. Iliev. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. V. Sabotinov, “Metal vapor lasers,” in Gas Lasers, M. Endo and R. F. Walter, Eds., pp. 449–494, CRC Press, Boca Raton, Fla, USA, 2006. View at Google Scholar
  2. P. G. Foster, Industrial applications of copper bromide laser technology, Ph.D. dissertation, Deprtment of Physics and Mathematical Physics, School of Chemistry and Physics, University of Adelaide, Adelaide, Australia, 2005.
  3. M. J. Kushner and B. E. Warner, “Large-bore copper-vapor lasers: kinetics and scaling issues,” Journal of Applied Physics, vol. 54, no. 6, pp. 2970–2982, 1983. View at Publisher · View at Google Scholar · View at Scopus
  4. R. J. Carman, D. J. W. Brown, and J. A. Piper, “A self-consistent model for the discharge kinetics in a high-repetition-rate copper-vapor laser,” IEEE Journal of Quantum Electronics, vol. 30, no. 8, pp. 1876–1895, 1994. View at Publisher · View at Google Scholar · View at Scopus
  5. A. M. Boichenko, G. S. Evtushenko, and S. N. Torgaev, “Simulation of a CuBr laser,” Laser Physics, vol. 18, no. 12, pp. 1522–1525, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. NIST/SEMATECH, “e-Handbook of Statistical Methods,” chapter 4.5.1.2, http://www.itl.nist.gov/div898/handbook/.
  7. I. P. Iliev, S. G. Gocheva-Ilieva, D. N. Astadjov, N. P. Denev, and N. V. Sabotinov, “Statistical analysis of the CuBr laser efficiency improvement,” Optics and Laser Technology, vol. 40, no. 4, pp. 641–646, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. I. P. Iliev, S. G. Gocheva-Ilieva, D. N. Astadjov, N. P. Denev, and N. V. Sabotinov, “Statistical approach in planning experiments with a copper bromide vapor laser,” Quantum Electronics, vol. 38, no. 5, pp. 436–440, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. I. P. Iliev and S. G. Gocheva-Ilieva, “Statistical techniques for examining copper bromide laser parameters,” in Proceedings of the International Conference on Numerical Analysis and Applied Mathematics (ICNAAM '07), vol. 936, pp. 267–270, Corfu, Greece, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. I. P. Iliev, S. G. Gocheva-Ilieva, and N. V. Sabotinov, “Classification analysis of CuBr laser parameters,” Quantum Electronics, vol. 39, no. 2, pp. 143–146, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. J. L. Lu and L. J. Wang, “The orthonormal design of experiments for the optimization of the parameters of the discharge circuit in the CuBr vapour lasers power supply,” Laser Technology, vol. 30, pp. 113–115, 2006. View at Google Scholar
  12. http://www.spss.com/.
  13. http://reference.wolfram.com/mathematica/guide/Mathematica.html.
  14. http://salford-systems.com/products/mars/overview.html.
  15. D. N. Astadjov, N. V. Sabotinov, and N. K. Vuchkov, “Effect of hydrogen on CuBr laser power and efficiency,” Optics Communications, vol. 56, no. 4, pp. 279–282, 1985. View at Google Scholar · View at Scopus
  16. D. N. Astadjov, K. D. Dimitrov, C. E. Little, N. V. Sabotinov, and N. K. Vuchkov, “CuBr laser with 1.4 W/cm3 average output power,” IEEE Journal of Quantum Electronics, vol. 30, no. 6, pp. 1358–1360, 1994. View at Publisher · View at Google Scholar · View at Scopus
  17. V. M. Stoilov, D. N. Astadjov, N. K. Vuchkov, and N. V. Sabotinov, “High spatial intensity 10 W-CuBr laser with hydrogen additives,” Optical and Quantum Electronics, vol. 32, no. 11, pp. 1209–1217, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. “NATO contract SfP, 97 2685, 50W Copper Bromide laser,” 2000.
  19. D. N. Astadjov, K. D. Dimitrov, D. R. Jones et al., “Influence on operating characteristics of scaling sealed-off CuBr lasers in active length,” Optics Communications, vol. 135, no. 4-6, pp. 289–294, 1997. View at Google Scholar · View at Scopus
  20. K. D. Dimitrov and N. V. Sabotinov, “High-power and high-efficiency copper bromide vapor laser,” in Proceedings of the 9th International School on Quantum Electronics: Lasers—Physics and Applications, vol. 3052 of Proceeding of SPIE, pp. 126–130, Varna, Bulgaria, September 1996. View at Publisher · View at Google Scholar
  21. D. N. Astadjov, K. D. Dimitrov, D. R. Jones et al., “Copper bromide laser of 120-W average output power,” IEEE Journal of Quantum Electronics, vol. 33, no. 5, pp. 705–709, 1997. View at Google Scholar · View at Scopus
  22. N. P. Denev, D. N. Astadjov, and N. V. Sabotinov, “Analysis of the copper bromide laser efficiency,” in Proceedings of the 4th International Symposium on Laser Technologies and Lasers, pp. 153–156, Plovdiv, Bulgaria, 2006.
  23. I. T. Jolliffe, “A note on the use of principal components in regression,” Journal of the Royal Statistical Society: Series C, vol. 31, pp. 300–303, 1982. View at Google Scholar
  24. “Computation with solo power analysis,” BMDP Statistical Software Inc., LA, 1993.
  25. J. H. Friedman, “Multivariate adaptive regression splines,” The Annals of Statistics, vol. 19, no. 1, pp. 1–141, 1991. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  26. P. Craven and G. Wahba, “Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized cross-validation,” Numerische Mathematik, vol. 31, no. 4, pp. 377–403, 19779. View at Publisher · View at Google Scholar · View at MathSciNet
  27. A. J. Izenman, Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning, Springer Texts in Statistics, Springer, New York, NY, USA, 2008. View at Publisher · View at Google Scholar · View at MathSciNet