Determination of neutron fluence-to-dose conversion coefficients by means of artificial neural networks

Hernández-Dávila, V. M.; Soto-Bernal, Tzinnia Gabriela; Vega-Carrillo, Héctor René

doi:10.1016/j.apradiso.2013.04.014

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…This paper considers the neutron and gamma-induced dose rates around the LIEBE target. The dose rate estimation can be obtained after the neutron and gamma flux calculations using the Formula [9]:…”

Section: Mcnp Codementioning

confidence: 99%

Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes

et al. 2022

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To enhance the production of short-lived isotopes, higher beam powers are sought, which require targets able to accommodate them. One such target prototype is a liquid metal target LIEBE, developed at CERN. In this paper, a simulation of the proton beam interaction with the LIEBE target is presented. Simulations were performed by a series of proton transport calculations using the MCNP Monte Carlo code. The latest LIEBE target MCNP input was created in high-fidelity geometry, and the FENDL-3.1 cross-section data library was used. Flux and dose rate maps in the LIEBE target obtained from the simulations are presented in the paper. The maximum obtained dose around the target is roughly 361 Sv/h for gamma rays and 214 Sv/h for neutrons. The 70 MeV–100 µA proton beam penetrates roughly 7 mm deep into the liquid eutectic lead–bismuth. Based on this, further required changes to the LIEBE target can be evaluated.

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Section: Mcnp Codementioning

confidence: 99%

Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes

et al. 2022

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“…1. In common with other ANN-based researches [49,50], the hidden layers and the number of their neurons are selected via the trial-and-error method. Hyperbolic tangent sigmoid (tansig) function is chosen as the activation function of the hidden layers to help the network learn the nonlinear dynamics of the chaotic systems, i.e., the relationships between the inputs and outputs.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Artificial neural network-based modeling of brain response to flicker light

et al. 2015

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Not only does the modeling of dynamical systems, for instance the biological systems, play an important role in the accurate perception and analysis of these systems, but it also makes the prediction and control of their behavior straightforward. The results of multiple researches in the field of the modeling of biological systems have indicated that the chaotic behavior is a prevalent feature of most complex interactive biological systems. Our results demonstrate that the artificial neural network provides us an effective means to model the underlying dynamics of these systems. In this paper, at first, we represent the results of the use of a multilayer feed-forward neural network to model some famous chaotic systems. The specified neural network is trained with the return maps extracted from the time series. We proceed with the paper by evaluating the accuracy and robustness of our model. The ability of the select neural network to model the dynamics of chosen chaotic systems is verified, even in the presence of noise. Afterwards, we model the brain response to the flicker light. It is known that the brain response to some stimuli such as the flicker light recorded as electroretinogram is an exemplar of chaotic behavior. The need remains, however, for realistic modeling of this behavior of the brain. In this paper, we represent the results of the modeling of this chaotic response by utilizing the proposed neural network. The capability of the neural network to model this specific brain response is confirmed.

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“…1. In common with other ANN-based researches [12,13], the hidden layers and the number of their neurons are selected via the trial-anderror method. We use the MATLAB Software (version 7.12) to perform all the simulations.…”

Section: A Artificial Neural Network-based Modelmentioning

confidence: 99%

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

Falahian

Dastjerdi

Gharibzadeh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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The recent meteoric significant developments in the biological and medical sciences have been the culmination of substantial efforts devoted to precisely modeling the behavior of biological systems and their responses to various stimuli. The complicated interactions within varied components of biological systems as well as with their environments make them extremely complex nonlinear systems. The results of several contemporary relevant investigations have manifested their chaotic behavioral patterns. With the aim of modeling this specific behavior of biosystems, we employ a particular multilayer feed-forward neural network. The distinctive feature of our modeling method, which makes it dominant within the modeling techniques, is training the select neural network with the chaotic map extracted from the under-study time series. Our results, which are briefly represented in this paper, confirm that the specified neural network does possess the potentiality to model the chaotic dynamics of biological systems, even in the presence of noise. In pursuance of evaluating our model, we assess and model the chaotic response of the brain to the flicker light through some recorded electroretinogram data.

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Determination of neutron fluence-to-dose conversion coefficients by means of artificial neural networks

Cited by 10 publications

References 5 publications

Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes

Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes

Artificial neural network-based modeling of brain response to flicker light

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

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