The measurement of total mass attenuation coefficients of CoCuNi alloys

Seven, Sabriye; Karahan, İsmail Hakkı; Bakkaloğlu, Ö.F.

doi:10.1016/s0022-4073(03)00118-3

Cited by 23 publications

(4 citation statements)

References 21 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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“…Up to now, many researchers have determined attenuation coefficient of gamma radiation by experimental methods. In most cases, gamma ray transmission geometry has been the most popular experimental technique for measuring [3][4][5][6][7][8][9]. For unknown thickness and odd shaped materials (e.g.…”

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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“…When chemical composition of the samples is known, this coef icient can be calculated by using the photon cross sections data base [5]. There is one method for determining the linear attenuation coef icient under the assumption that the density of the sample in which a collimated narrow beam is used [6][7][8][9][10] is uniform. However, this method is dif icult to be carried out in a typical laboratory because of high radioactivity of sources prices and the requirement of a large space for the devices.…”

Section: Introductionmentioning

confidence: 99%

Using Mathematical Procedure to Compute the Attenuation Coefficient in Spectrometry Fields

2017

J Radiol Oncol

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In gamma-ray spectrometry, the analysis of the environmental radioactivity samples (soil, sediment and ash of a living organism) needs to know the linear attenuation coeffi cient of the sample matrix. This coeffi cient is required to calculate the self-absorption correction factor through the sample bulk. In addition, these parameters are very important because the unidentifi ed samples can be different in the composition and density from the reference liquid sources which are usually used for effi ciency calibration in the radioactive monitoring process. The present work is essentially concerned to introduce a mathematical method to calculate the linear attenuation coeffi cient without using any collimator. This method was based mainly on the calculations of the effective solid angle subtended by the source-to-the detector confi gurations, the effi ciency transfer technique and the average path lengths through the samples itself. The method can be used as a tool for the calculation of the linear attenuation coeffi cient of unidentifi ed materials with good facility to use it in the calibration process of γ-ray detectors, particularly in the study of soil samples. The results are compared with the data from NIST-XCOM to show how much the results are in close agreement and to give the validity of the approach.

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The measurement of total mass attenuation coefficients of CoCuNi alloys

Cited by 23 publications

References 21 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

Using Mathematical Procedure to Compute the Attenuation Coefficient in Spectrometry Fields

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