Working Paper Article Version 1 This version is not peer-reviewed

Artificial Neural Network for Uncertainty Quantification in RF Radiation Modeling with High-Dimensional Data

Version 1 : Received: 4 March 2020 / Approved: 6 March 2020 / Online: 6 March 2020 (02:57:06 CET)

A peer-reviewed article of this Preprint also exists.

Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data. Int. J. Environ. Res. Public Health 2020, 17, 2586. Cheng, X.; Henry, C.; Andriulli, F.P.P.; Person, C.; Wiart, J. A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data. Int. J. Environ. Res. Public Health 2020, 17, 2586.

Journal reference: Int. J. Environ. Res. Public Health 2020, 17, 2586
DOI: 10.3390/ijerph17072586

Abstract

This paper focuses on quantifying the uncertainty in the specific absorption rate values of brain induced by the uncertain positions of the electroencephalography electrodes placed on 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.

Subject Areas

artificial neural networks; uncertainty quantification; specific absorption rate

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.