Stochastic dosimetry, combining electromagnetic computational techniques and statistics to build surrogate models, allows assessing exposure to EMF accounting for variability and uncertainty intrinsic of real scenarios. In this study, we present some examples of exposure assessment of children and fetuses to RF devices in uncertain scenarios using stochastic dosimetry. Polynomial chaos expansions and low rank tensor approximations, applied to build surrogate models of Specific Absorption Rate (SAR), permitted a fast estimation of the variability of the exposure due to the variation in the RF source position.

Stochastic Dosimetry for Radio-Frequency Exposure Assessment in Realistic Scenarios

E Chiaramello;S Fiocchi;M Parazzini;P Ravazzani;
2019

Abstract

Stochastic dosimetry, combining electromagnetic computational techniques and statistics to build surrogate models, allows assessing exposure to EMF accounting for variability and uncertainty intrinsic of real scenarios. In this study, we present some examples of exposure assessment of children and fetuses to RF devices in uncertain scenarios using stochastic dosimetry. Polynomial chaos expansions and low rank tensor approximations, applied to build surrogate models of Specific Absorption Rate (SAR), permitted a fast estimation of the variability of the exposure due to the variation in the RF source position.
2019
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
Inglese
Canavero, Flavio
Uncertainty Modeling for Engineering Applications
89
102
14
978-3-030-04870-9
Sì, ma tipo non specificato
Stochastic dosimetry
Surrogate models
EMF Exposure
Uncertainty modeling
Polynomial-chaos expansions
Low-rank-tensor approximations
4
02 Contributo in Volume::02.01 Contributo in volume (Capitolo o Saggio)
268
none
E. Chiaramello ; S. Fiocchi ; M. Parazzini ; P. Ravazzani ; J. Wiart
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/348596
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