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

Journal: International journal of environmental research and public health
Published Date: Apr 9, 2020

Abstract

This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulations, an artificial neural networkarchitecture for uncertainty quantification involving high-dimensional data is proposed in this paper.The proposed method is demonstrated to be an attractive alternative to conventional uncertaintyquantification methods because of its considerable advantage in the computational expense andspeed.

Authors

  • Xi Cheng
    Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology (OCICB), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of HealthRockville, MD, USA.
  • Clément Henry
    Department of Electronics and Telecommunications, Politecnico di Torino, IT-10129 Turin, Italy.
  • Francesco P 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, Brest Cedex 03, France.
  • Joe Wiart
    Chaire C2M, LTCI, Télécom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France.

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