Simplified two media method: A modified approach for measuring linear attenuation coefficient of odd shaped archaeological samples of unknown thickness

Sidhu, Baltej Singh; Dhaliwal, A.S.; Mann, Kulwinder Singh; Kahlon, K.S.

doi:10.1016/j.apradiso.2011.06.007

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Among the different methods which have been developed for the determination of attenuation coefficient are: 1-Gamma ray transmission geometry [2][3][4] and two media method [5][6][7] which have been based on measurements 2-MAC estimation using Monte Carlo modelling [8][9][10] and sensitivity analysis which have been based on calculations [11]. These methods have their own advantages/disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods

Moshkbar-Bakhshayesh¹

2020

J. Inst.

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A: Multilayer perceptron (MLP) neural networks have been used extensively for estimation/regression of parameters. Moreover, recent studies have shown that learning algorithms of MLP which are based on Gaussian function are more accurate. In this paper, the mass attenuation coefficient (MAC) of gamma radiation for light-weight materials (e.g. O-8), mid-weight materials (e.g. Al-13), and heavy-weight materials (e.g. Pb-82) is modelled using Gaussian function based regularization of MLP (i.e. Bayesian regularization (BR)) and by a modular estimator. The results are compared with the Reference results. To show better performance of the utilized algorithm, the results of the different supervised methods including support vector machine (SVM) with different kernel functions, decision tree (DT), and radial basis network (RBN) are given. Average mean relative error (AMRE) and cumulative distribution function (CDF) of errors of MACs estimation are calculated. Comparison of the results indicates that MLP-BR gives more accurate results (e.g.

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Section: Introductionmentioning

confidence: 99%

Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods

Moshkbar-Bakhshayesh¹

2020

J. Inst.

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“…For unknown thickness and odd shaped materials (e.g. rocks or construction materials) another experimental approach named "Two media method" has 2019 JINST 14 P10010 been proposed [10][11][12]. Moreover, calculation of attenuation coefficient using Monte Carlo codes has been done [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Development of a modular system for estimating attenuation coefficient of gamma radiation: comparative study of different learning algorithms of cascade feed-forward neural network

Moshkbar-Bakhshayesh¹

2019

J. Inst.

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In this paper, a modular system is developed for estimation of mass attenuation coefficient (MAC) of different materials/energies using artificial neural network (ANN). Cascade feed-forward neural network (CFFNN) as a type of ANN constructs mapping function between input patterns and the targets (i.e. MAC). Performance of different learning algorithms of CFFNN including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are compared. For training, different categories of input patterns features are utilized to show the more appropriate one. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the results indicate that BR learning algorithm accompany with the selected category of features (i.e. Z, E, and ρ) is more accurate for estimation of MAC (e.g. CDF Al (0.0069) = 0.99 and AMRE Al = 0.0017). The advantages of the present method are: 1-Estimation of MAC is done fast (i.e. in comparison with Monte Carlo methods) and is done at a lower cost (i.e. without need to extra experiments) 2-Modular system reduces the risk of incorrect estimation 3-It is possible to extend the number of estimators for more materials/mixtures without unfavorably affecting the existing system.

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Attenuation coefficients of gamma and X-rays passing through six materials

Zhang

et al. 2019

NUCL SCI TECH

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Simplified two media method: A modified approach for measuring linear attenuation coefficient of odd shaped archaeological samples of unknown thickness

Cited by 7 publications

References 10 publications

Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods

Bayesian regularization of multilayer perceptron neural network for estimation of mass attenuation coefficient of gamma radiation in comparison with different supervised model-free methods

Development of a modular system for estimating attenuation coefficient of gamma radiation: comparative study of different learning algorithms of cascade feed-forward neural network

Attenuation coefficients of gamma and X-rays passing through six materials

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