Dar ve geniş kuyu yarı-iletken GaAs lazerlerin çizgi genişletme faktörlerinin modellenmesi

In this study color changing effects of UV lights on the different wood samples applied with oil, wax and shellac varnish have been researched. For this purpose samples were prepared from acacia (Robinia pseudoacacia L.), pear (Pirus communis L.), chestnut (Castanea sativa Mill.), oak (Quercus petrean Lieble) and cedar (Cedrus libani A. Rich) all of which grow in Turkey. Specimens were kept under exposure of UV lights for 72 hours after coated shellac varnish, teak oil, and liquid paraffin wax on their surfaces. Discoloration performances were tested by using method of ASTM 2224.02.e1. The results of this study indicated that oil, wax and shellac varnish were not able to protect the wood against discoloration effects of UV lights. The lowest color changing value obtained by using liquid paraffin wax.

Modeling of the linewidth enhancement factors of the narrow and wide GaAs well semiconductor lasers

A different method and single model to determine the linewidth enhancement factor (α (Alpha) parameter) for narrow and wide GaAs Quantum-wells (QWs) as a function of modal peak gain and current density is presented. Based on the Artificial Neural Network (ANN) modeling approach, different learning algorithms are trained and tested. The Levenberg–Marquardt (LM) algorithm, which has a quadratic speed of convergence, gives the best result among other learning algorithms used in the analysis. Both the training and the test results are in very good agreement with the experimental results reported elsewhere.

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1.Henry, C. H.,"Theory of the linewidth of semiconductor lasers", IEEE J. Quantum Electron., Cilt 18,259-264, 1982.
2.Sağıroğlu, S., Çelebi, F.V., Danışman, K.,"Modelling of the linewidth enhancement factor with the use of radial basis function network", Int. J. Electron. Commun.. (AEU), Cilt 56, No.l, 51-54, 2002
3.Newell, T.C., Bossert, D.J., Stintz, A., Fuchs, B.,Malloy, K.J. ve Lester, L.F., "Gain and linewidth enhancement factor in InAs quantum-dot laser diodes", IEEE Photon.Technol. Lett., Cilt 11, 1527-1529, 1999.
4.Maren, A., Handbook of neural computing applications, Academic press, London, 1990.
5.Stohs, J.,Bossert, D.J., Gallant, D., Brueck, J.,"Gain, Refractive Index Change, and Linewidth Enhancement Factor in Broad -Area GaAs and InGaAs Quantum-Well lasers", IEEE J.of Quantum Electronics, Cilt 37,No.11,1449-1459,2001.
6.Rumelhart,D.E.,McClelland J.L., Paralel Distributed Processing, MIT Press, Cambridge,1986
7.Ham,F.M.,Kostanic,I.,Principles of Neurocomputing for Science and Engineering, Me- Graw Hill, 2002.
8.Dennis,J. E., Schnabel, R.B., Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Englewood Cliffs, Prentice-Hall, 1983.
9.Hagan M.T., Demuth H.B., Beale M.H., Neural Network Design, Boston MA, PWS Publishing,1996.
10.Riedmiller,M, ve Braun H., "A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm", Proceedings Of The IEEE International Conference On Neural Networks, San Francisco, CA, Cilt.1, 586-591, Mart 1993.
11.http://www.mathworks.com/access/helpdesk/help /toolbox/nnet/baekprl4.html
12.Hakki,B.W.,Paoli,T.L.,"Gain spectra in GaAs double-heterostructure injection lasers", J. of Appl. Phys., Cilt 46, No. 3, 1299-1306, 1975.
13. Kane, E. 0., "Band structure of indium antimonide", J. Phys. Chem. Solids, Cilt 1, 249-261, 1957.