Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A deep learning method, cascaded convolutional neural networks, is investigated as a means for the prediction of frequency-dependent intensity distribution of planar radiating sources from frequency-dependent, near-field intensity data. More specifically, two convolutional neural networks are utilized as follows. The first one uses as input the available near-field amplitude data to predict the amplitude and phase of radiated fields on a plane in closer proximity to the radiating sources. Using the obtained distribution as input, the second one estimates the intensity of the planar radiating sources. The proposed method exhibits very good accuracy in the prediction of the radiating source distribution over the frequency range used for the training of the convolutional neural networks.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages610-615
Number of pages6
ISBN (Electronic)9781538691991
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019 - New Orleans, United States
Duration: Jul 22 2019Jul 26 2019

Publication series

Name2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019

Conference

Conference2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019
CountryUnited States
CityNew Orleans
Period7/22/197/26/19

Fingerprint

Neural networks
Deep learning

Keywords

  • convolutional neural network
  • deep learning method
  • frequency-dependent radiated emissions analysis
  • near-field scanning

ASJC Scopus subject areas

  • Signal Processing
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Ma, H., Li, E. P., Schutt-Aine, J., & Cangellaris, A. C. (2019). Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data. In 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019 (pp. 610-615). [8825271] (2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISEMC.2019.8825271

Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data. / Ma, Hanzhi; Li, Er Ping; Schutt-Aine, Jose; Cangellaris, Andreas C.

2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 610-615 8825271 (2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ma, H, Li, EP, Schutt-Aine, J & Cangellaris, AC 2019, Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data. in 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019., 8825271, 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019, Institute of Electrical and Electronics Engineers Inc., pp. 610-615, 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019, New Orleans, United States, 7/22/19. https://doi.org/10.1109/ISEMC.2019.8825271
Ma H, Li EP, Schutt-Aine J, Cangellaris AC. Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data. In 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 610-615. 8825271. (2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019). https://doi.org/10.1109/ISEMC.2019.8825271
Ma, Hanzhi ; Li, Er Ping ; Schutt-Aine, Jose ; Cangellaris, Andreas C. / Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data. 2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 610-615 (2019 IEEE International Symposium on Electromagnetic Compatibility, Signal and Power Integrity, EMC+SIPI 2019).
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