A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture f...

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Main Authors: Xi Cheng, Clément Henry, Francesco P. Andriulli, Christian Person, Joe Wiart
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:International Journal of Environmental Research and Public Health
Subjects:
artificial neural networks
uncertainty quantification
specific absorption rate
Online Access:https://www.mdpi.com/1660-4601/17/7/2586
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spelling doaj-f0d5dcf2bbc041e495ff224281797ff82020-11-25T02:37:37ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012020-04-01172586258610.3390/ijerph17072586A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional DataXi Cheng0Clément Henry1Francesco P. Andriulli2Christian Person3Joe Wiart4Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, FranceDepartment of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, ItalyIMT Atlantique/Lab-STICC UMR CNRS 6285, Technopole Brest Iroise-CS83818-29238, 29238 Brest CEDEX 03, FranceChaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, FranceThis paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.https://www.mdpi.com/1660-4601/17/7/2586artificial neural networksuncertainty quantificationspecific absorption rate
collection DOAJ
language English
format Article
sources DOAJ
author Xi Cheng
Clément Henry
Francesco P. Andriulli
Christian Person
Joe Wiart
spellingShingle Xi Cheng
Clément Henry
Francesco P. Andriulli
Christian Person
Joe Wiart
A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
International Journal of Environmental Research and Public Health
artificial neural networks
uncertainty quantification
specific absorption rate
author_facet Xi Cheng
Clément Henry
Francesco P. Andriulli
Christian Person
Joe Wiart
author_sort Xi Cheng
title A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
title_short A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
title_full A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
title_fullStr A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
title_full_unstemmed A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data
title_sort surrogate model based on artificial neural network for rf radiation modelling with high-dimensional data
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1661-7827
1660-4601
publishDate 2020-04-01
description This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.
topic artificial neural networks
uncertainty quantification
specific absorption rate
url https://www.mdpi.com/1660-4601/17/7/2586
work_keys_str_mv AT xicheng asurrogatemodelbasedonartificialneuralnetworkforrfradiationmodellingwithhighdimensionaldata
AT clementhenry asurrogatemodelbasedonartificialneuralnetworkforrfradiationmodellingwithhighdimensionaldata
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AT clementhenry surrogatemodelbasedonartificialneuralnetworkforrfradiationmodellingwithhighdimensionaldata
AT francescopandriulli surrogatemodelbasedonartificialneuralnetworkforrfradiationmodellingwithhighdimensionaldata
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AT joewiart surrogatemodelbasedonartificialneuralnetworkforrfradiationmodellingwithhighdimensionaldata
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