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Title: A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

Abstract

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90%more » classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

Authors:
 [1];  [1];  [1];  [2]; ORCiD logo [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Tennessee Technological Univ., Cookeville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1376388
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Electromagnetic Compatibility
Additional Journal Information:
Journal Volume: PP; Journal Issue: 99; Journal ID: ISSN 0018-9375
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; harmonic; linear discriminant analysis (LDA); modulation; nonintrusive load monitoring (NILM); unintended radiated emissions (URE)

Citation Formats

Vann, Jason Michael, Karnowski, Thomas P., Kerekes, Ryan, Cooke, Corey D., and Anderson, Adam L. A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions. United States: N. p., 2017. Web. doi:10.1109/TEMC.2017.2692962.
Vann, Jason Michael, Karnowski, Thomas P., Kerekes, Ryan, Cooke, Corey D., & Anderson, Adam L. A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions. United States. doi:10.1109/TEMC.2017.2692962.
Vann, Jason Michael, Karnowski, Thomas P., Kerekes, Ryan, Cooke, Corey D., and Anderson, Adam L. Mon . "A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions". United States. doi:10.1109/TEMC.2017.2692962. https://www.osti.gov/servlets/purl/1376388.
@article{osti_1376388,
title = {A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions},
author = {Vann, Jason Michael and Karnowski, Thomas P. and Kerekes, Ryan and Cooke, Corey D. and Anderson, Adam L.},
abstractNote = {Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.},
doi = {10.1109/TEMC.2017.2692962},
journal = {IEEE Transactions on Electromagnetic Compatibility},
number = 99,
volume = PP,
place = {United States},
year = {Mon Apr 24 00:00:00 EDT 2017},
month = {Mon Apr 24 00:00:00 EDT 2017}
}

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