TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Efficient Iterative Data Generation Using Evaluation of Prioritized Input Parameters in ANNs for SAR Prediction in Human Head Models at 13.56 MHz
 
Options

Efficient Iterative Data Generation Using Evaluation of Prioritized Input Parameters in ANNs for SAR Prediction in Human Head Models at 13.56 MHz

Publikationstyp
Journal Article
Date Issued
2024-08-15
Sprache
English
Author(s)
Esmaeili, Hamideh  
Theoretische Elektrotechnik E-18  
Yang, Cheng  
Theoretische Elektrotechnik E-18  
Schuster, Christian  
Theoretische Elektrotechnik E-18  
TORE-URI
https://tore.tuhh.de/handle/11420/52807
Journal
IEEE transactions on electromagnetic compatibility  
Volume
66
Issue
6
Citation
IEEE Transactions on Electromagnetic Compatibility 66 (6): 1947-1957 (2024)
Publisher DOI
10.1109/temc.2024.3439468
Scopus ID
2-s2.0-85213305425
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
In this work, an efficient iterative dataset generation strategy is proposed, considering prediction accuracy and high impact input parameters as figures of merit to define a sufficient number of samples for reliable machine learning (ML) results. Parameter prioritization in combination with artificial neural networks (ANNs) is designed for examination of multiparameterized simulation setups in bioelectromagnetic (BEM), aiming to avoid redundant parameters and excessive sample sizes and to provide an alternative to expensive measurements, full-wave simulations, and the limitations of adaptive sampling methods in high-dimensional BEM problems. Specifically, the variation of mass-averaged specific absorption ratio (SAR) in each individual tissue in human head models is studied, considering up to
±\pm
95% uncertainty in a uniform distribution of electrical properties of tissues. Up to 3500 full-wave simulations for seven different scenarios are performed. Utilizing parameter prioritization in ANNs enables high accuracy results with fewer input parameters, allowing improved physical interpretability. By applying this method, the required number of numerical simulations (samples) for an optimal dataset is approximately 5 to 10 times the total number of input parameters. The results of this innovative method demonstrate that the reduced dataset successfully encapsulates the core aspects of the SAR problem under investigation, resulting in ML prediction accuracy surpassing 95% while reducing time and memory consumption by approximately 60%.
Subjects
MLE@TUHH
DDC Class
600: Technology
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback