ELECTROSTATIC LENS SYSTEM DESIGN WITH THE ARTIFICIAL NEURAL NETWORKS

Yıl 2020, , 388 - 396, 25.06.2020

Nimet Işık , Ali Hakan Isık

https://doi.org/10.21923/jesd.566702

Cited By: 2

Öz

Successful applications have been developed in many disciplines with artificial algorithms in recent years. The data obtained from experimental or simulation programs have been processed with the corresponding algorithms. Prediction and classification studies are carried out by processing the data with the designed algorithm architectures. From these algorithms, it is of great importance to select the algorithm that is appropriate for the purpose and data set. In this context, using artificial neural network algorithms in innovative studies in the field of physics ensures high performance values. Artificial neural network (ANN), inspired by biological neurons, is parallel computing system having learning ability. In this study, the parallel beam mode of the five-element electrostatic cylindrical lenses is determined using a three layer artificial neural network. The data set used in the study was obtained with the aid of the CPO (Charged Particle Optics) program enabling highly accurate calculation. Analysis of the data was performed using Matlab R2012b program. According to the obtained results, it has been revealed that the artificial neural network has high performance values in determining the parallel beam mode in the field of physics and it is an alternative method to the finite difference and boundary element method in electrostatic problem solutions. The generated YSA algorithm correctly classifies 85.7% of the test data.

Anahtar Kelimeler

Electrostatic Lenses, Parallel Electron Beam, Artificial Neural Networks

YAPAY SİNİR AĞLARI İLE ELEKTROSTATİK LENS SİSTEM TASARIMI

Öz

Yapay zekâ algoritmalarıyla son yıllarda birçok bilim dalında başarılı uygulamalar geliştirilmektedir. Deneysel veya benzetim programlarından elde edilen veriler söz konusu algoritmalarla işlenmektedir. Tasarlanan algoritma mimarileri ile veriler işlenerek tahmin ve sınıflandırma çalışmaları yapılmaktadır. Bu algoritmalardan, amaca ve veri kümesine uygun olan algoritmanın seçilmesi büyük önem taşımaktadır. Bu kapsamda, fizik alanındaki yenilikçi çalışmalarda yapay sinir ağı algoritması kullanmak yüksek performans değerleri elde etmeyi sağlamaktadır. Biyolojik nöronlardan esinlenen yapay sinir ağı (YSA), öğrenme yeteneğine sahip paralel hesaplama sistemidir. Bu çalışmada, üç katmanlı yapay sinir ağı kullanılarak beş elemanlı elektrostatik silindir lenslerin paralel demet modu belirlenmektedir. Çalışmada kullanılan veri kümesi, yüksek doğrulukta hesaplama yapabilen CPO(Yüklü Parçacık Optiği) programı yardımıyla elde edilmiştir. Verilerin analizi Matlab R2012b programı kullanılarak gerçekleştirilmiştir. Elde edilen sonuçlara göre, yapay sinir ağının fizik alanında paralel demet modunu belirlemede yüksek performans değerlerine sahip olan ve elektrostatik problem çözümlerinde sonlu fark ve sınır eleman metoduna alternatif bir metot olduğu ortaya konulmuştur. Oluşturulan YSA algoritması, test verilerinin %85,7’sini doğru olarak sınıflandırmıştır.

Anahtar Kelimeler

Elektrostatik Lensler, Paralel Elektron Demeti, Yapay Sinir ağları

Kaynakça

• Al-Hagan O., Kaiser C., Madison D., Murray A. J., 2009. Atomic and Molecular Signatures for Charged Particle Ionization, Nature Physics, 5, 59-63.
• Bayram T., Akkoyun S., Kara S. O., 2014 . A Study on Ground-State Energies of Nuclei by using Neural Networks, Ann. Nucl. En., 63, 172-175.
• Cubric D., Lencova B., Read F. H., Zlamal J.,. 1999. Comparison Of FDM, FEM and BEM for Electrostatic Charged Particle Optics, Nucl. Inst. Meth. Phys. Res. Sec. A: Acc. Spect. Det. Assoc. Equip., 427:1, 357-362.
• Harting E., Read F. H., 1976. Electrostatic Lenses, Elsevier Science Yayınevi.
• Haykin S., 1999. Neural Networks: A Comprehensive Foundation, Englewood Cliffs, Prentice-Hall,
• Heddle D.W.O., 2000. Electrostatic Lens Systems, IOP Press, London.
• Işık A. H., 2015a. The Investigation of Electron-Optical Parameters Using Artificial Neural Networks, Acta Phy. Pol. A., 127:4, 1317-1319.
• Işık A. H., 2015b. Prediction of Two-Element Cylindrical Electrostatic Lens Parameters using Dynamic Artificial Neural Network, Acta Phy. Pol. A., 127:6, 1717-1721.
• Işık A. H., Işık N., 2016b. Time Series Artificial Neural Network Approach for Prediction of Optical Lens Properties, Acta Phy. Pol. A., 129:4, 514-516.
• Işık N., 2016. Determination of Electron Optical Properties for Aperture Zoom Lenses using an Artificial Neural Network Method, Microscopy and Microanalysis, Cilt. 22:2, 458-462,
• Işık N., Doğan M., Bahçeli S., 2016. Triple Differential Cross Section Measurements for the Outer Valence Molecular Orbitals (1t2) of A Methane Molecule at 250 eV Electron Impact, J. Phys. B. At. Mol. Opt. Phys., 49, 065203-1-5.
• Işık N., Işık A. H., 2016a. Classification of Electron Gun Operation Modes using Artificial Neural Networks, Acta Phy. Pol. A., 129:4, 628-630.
• Khan J., Wei J.S., Ringner M., Saal L.H., Ladanyi M., Westermann F., Berthold F., Schwab M., Antonescu C.R., Peterson C., Meltzer P.S., 2001. Classification and Diagnostic Prediction of Cancers using Gene Expression Profiling and Artificial Neural Networks Nature Medicine 7, 673-679.
• Lagaris I. E., Likas A., Fotiadis D. I., 1997. Artificial Neural Network Methods in Quantum Mechanics, Comp. Phys. Com., 104, 1-14.
• Lahmam-Bennani A., 1991. Recent Developments and New Trends in (e,2e) And (e,3e) Studies, J. Phys. B: At. Mol. Opt. Phys., 24, 2401-2442.
• Levenberg K., 1944. A method for the solution of certain non-linear problems in least squares, Quart. Appl. Math., 2, 164–168.
• Marquardt D., 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM J. Appl. Math., 11, 431-441.
• Nouri T., Pierre-Jean E., 1992. Knowledge Based Optical System Design, Proceedings of EXPER SYS-92, 147-152.
• Petkovic D., Pavlovic N. T., Shamshirband S., Mat Kiah M.L., Badrul Anuar N., Idna Idris M.Y., 2014. Adaptive Neuro-Fuzzy Estimation of Optimal Lens System Parameters, Opt. Lasers Eng. 55. 84-93.
• Read F. H., Adams A., Soto-Montiel J. R., 1971. Electrostatic Cylinder Lenses. I. Two Element Lenses, J. Phys. E (Sci. Instrum), 4, 625-632.
• Read F. H., Bowring N.J., 2011. The CPO Programs and The BEM for Charged Particle Optics, Nucl. Inst. Meth. Phys., 645, 273-277.
• Sigman M. E., Rives S. S., 1994. Prediction of Atomic Ionization Potentials I-Iii Using An Artificial Neural Network, J. Chem. Int. Comput. Sci., 34, 617-620.
• Sise O., Okumus N., Ulu M., Dogan M., 2009. Computer Simulation of Electrostatic Aperture Lens Systems for Electron Spectroscopy, J.Elec. Spectr. Rel. Phen., 175, 76-86.
• Sumpter B. G., Getino C., Noid D. W., 1992. A Neural Network Approach to The Study of Internal Energy Flow in Molecular Systems, J. Chem. Phys., 97, 293-306.
• Tam S.M., Kwong C.K., Ip W.H. 2000. A Hybrid Artificial Intelligence System for Optical Lens Design, Int. J. Comp. Appl. Techn. 13, 229-236.
• Weng Z.C, Chen Z.Y., Yang Y.H., Ren T., Tong X.J., 1991. An Attempt to Develop a Zoom Lens Design Expert System, Proceedings of the SPIE the International Society for Optical Engineering, SPIE, 1527, 349-356.