Novel Method for Predicting PCB Configurations for Near-Field and Far-Field Radiated EMI Using a Neural Network

Kraison AUNCHALEEVARAPAN; Kitti PAITHOONWATANAKIJ; Werachet KHAN-NGERN; Shuichi NITTA

Novel Method for Predicting PCB Configurations for Near-Field and Far-Field Radiated EMI Using a Neural Network

Kraison AUNCHALEEVARAPAN, Kitti PAITHOONWATANAKIJ, Werachet KHAN-NGERN, Shuichi NITTA

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The Neural Network (NN) is applied to recognize basic PCB configurations using its magnetic near-field spectra and radiated far-field emission. The learning process is accomplished by using the computed spectra of the radiated field from PCBs having different configurations. The anomaly is detected through the monitoring of the spectra's amplitude frequency by injecting a voltage pulse at the PCB configuration. The trained NN is then applied to the identification of PCB layouts from radiated emission measurements. The trained NN can identify all of those PCB configurations from the magnetic near-field spectra and the radiated far-field EMI. Moreover, the calculated results of the NN are compared with the actual far-field measurements and other models for evaluation. Finally, the NN used for predicting far-field emission from their magnetic near-field measurement is proposed. Experiments show that the NN can predict the far-field spectra from the magnetic near-field spectra.

Publication: IEICE TRANSACTIONS on Communications Vol.E86-B No.4 pp.1364-1376

Publication Date: 2003/04/01

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Type of Manuscript: PAPER

Category: Electromagnetic Compatibility(EMC)

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Kraison AUNCHALEEVARAPAN, Kitti PAITHOONWATANAKIJ, Werachet KHAN-NGERN, Shuichi NITTA, "Novel Method for Predicting PCB Configurations for Near-Field and Far-Field Radiated EMI Using a Neural Network" in IEICE TRANSACTIONS on Communications, vol. E86-B, no. 4, pp. 1364-1376, April 2003, doi: .
Abstract: The Neural Network (NN) is applied to recognize basic PCB configurations using its magnetic near-field spectra and radiated far-field emission. The learning process is accomplished by using the computed spectra of the radiated field from PCBs having different configurations. The anomaly is detected through the monitoring of the spectra's amplitude frequency by injecting a voltage pulse at the PCB configuration. The trained NN is then applied to the identification of PCB layouts from radiated emission measurements. The trained NN can identify all of those PCB configurations from the magnetic near-field spectra and the radiated far-field EMI. Moreover, the calculated results of the NN are compared with the actual far-field measurements and other models for evaluation. Finally, the NN used for predicting far-field emission from their magnetic near-field measurement is proposed. Experiments show that the NN can predict the far-field spectra from the magnetic near-field spectra.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/e86-b_4_1364/_p

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@ARTICLE{e86-b_4_1364,
author={Kraison AUNCHALEEVARAPAN, Kitti PAITHOONWATANAKIJ, Werachet KHAN-NGERN, Shuichi NITTA, },
journal={IEICE TRANSACTIONS on Communications},
title={Novel Method for Predicting PCB Configurations for Near-Field and Far-Field Radiated EMI Using a Neural Network},
year={2003},
volume={E86-B},
number={4},
pages={1364-1376},
abstract={The Neural Network (NN) is applied to recognize basic PCB configurations using its magnetic near-field spectra and radiated far-field emission. The learning process is accomplished by using the computed spectra of the radiated field from PCBs having different configurations. The anomaly is detected through the monitoring of the spectra's amplitude frequency by injecting a voltage pulse at the PCB configuration. The trained NN is then applied to the identification of PCB layouts from radiated emission measurements. The trained NN can identify all of those PCB configurations from the magnetic near-field spectra and the radiated far-field EMI. Moreover, the calculated results of the NN are compared with the actual far-field measurements and other models for evaluation. Finally, the NN used for predicting far-field emission from their magnetic near-field measurement is proposed. Experiments show that the NN can predict the far-field spectra from the magnetic near-field spectra.},
keywords={},
doi={},
ISSN={},
month={April},}

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TY - JOUR
TI - Novel Method for Predicting PCB Configurations for Near-Field and Far-Field Radiated EMI Using a Neural Network
T2 - IEICE TRANSACTIONS on Communications
SP - 1364
EP - 1376
AU - Kraison AUNCHALEEVARAPAN
AU - Kitti PAITHOONWATANAKIJ
AU - Werachet KHAN-NGERN
AU - Shuichi NITTA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E86-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2003
AB - The Neural Network (NN) is applied to recognize basic PCB configurations using its magnetic near-field spectra and radiated far-field emission. The learning process is accomplished by using the computed spectra of the radiated field from PCBs having different configurations. The anomaly is detected through the monitoring of the spectra's amplitude frequency by injecting a voltage pulse at the PCB configuration. The trained NN is then applied to the identification of PCB layouts from radiated emission measurements. The trained NN can identify all of those PCB configurations from the magnetic near-field spectra and the radiated far-field EMI. Moreover, the calculated results of the NN are compared with the actual far-field measurements and other models for evaluation. Finally, the NN used for predicting far-field emission from their magnetic near-field measurement is proposed. Experiments show that the NN can predict the far-field spectra from the magnetic near-field spectra.
ER -