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

Xi Cheng, Clément Henry, Francesco P. Andriulli, Christian Person and Joe Wiart
Additional contact information
Xi Cheng: Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France
Clément Henry: Department of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, Italy
Francesco P. Andriulli: Department of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, Italy
Christian Person: IMT Atlantique/Lab-STICC UMR CNRS 6285, Technopole Brest Iroise-CS83818-29238, 29238 Brest CEDEX 03, France
Joe Wiart: Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France

IJERPH, 2020, vol. 17, issue 7, 1-11

Abstract: 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.

Keywords: artificial neural networks; uncertainty quantification; specific absorption rate (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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