Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/29472
Title: Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: A comparative study
Authors: Manohara, S. R.
Hanagodimath, S. M.
Gerward, Leif
Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.
Küçük, Nil
0000-0002-9193-4591
24436223800
Keywords: Chemistry
Nuclear science & technology
Physics
Buildup factor
Gamma-ray
Energy absorption
Thermo luminescence dosimetry
Neural network
Geometrical progression
Training algorithms
100 mfp
Approximation
Technologies
Prediction
Parameters
Signals
Depths
Dosimetry
Energy absorption
Neural networks
Thermoluminescence
Buildup factor
Computational effort
Incident photon energy
Interpolation method
Levenberg-Marquardt learning algorithms
Multi-layer perceptron neural networks
Multi-layered Perceptron
Thermoluminescence dosimetry
Gamma rays
Issue Date: May-2013
Publisher: Pergamon-Elsevier Science
Citation: Küçük, N. vd. (2013). "Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: A comparative study". Radiation Physics and Chemistry, 85, 10-22.
Abstract: In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca-3(PO4)(2)] in the energy region 0.015-15 MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg-Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.43 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula.
URI: https://doi.org/10.1016/j.radphyschem.2013.01.021
https://www.sciencedirect.com/science/article/pii/S0969806X13000261
http://hdl.handle.net/11452/29472
ISSN: 0969-806X
Appears in Collections:Scopus
Web of Science

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