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Authors: Abdenour Hacine-Gharbi 1 and Philippe Ravier 2

Affiliations: 1 University of Bordj Bou Arréridj, Algeria ; 2 University of Orléans, France

ISBN: 978-989-758-276-9

Keyword(s): Non-Intrusive Load Monitoring (NILM), Electrical Appliances Identification, Feature Extraction (FE), Harmonic Analysis, Short-Time Fourier Series (STFS), Wavelet Analysis, Discrete Wavelets, Wavelet Cepstral Coefficient (WCC), Hidden Markov Models (HMM), Features Relevance, Wrappers Feature Selection (WFS).

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Feature Selection and Extraction ; Health Engineering and Technology Applications ; Pattern Recognition ; Signal Processing ; Software Engineering ; Theory and Methods

Abstract: In previews work, a construction of electrical appliances identification system has been proposed using Hidden Markov Models combined with STFS (Short-Time Fourier Series) features extraction. This paper proposes many extensions: (i) a larger spectral band up to the maximum frequency value for the analysis of the data is investigated, but requiring a higher dimensionality of the STFS feature vector; (ii) a more compact representation than the SFTS vector is investigated with the wavelet based approaches; (iii) the relevance of the wavelet based features are investigated using feature selection procedure. The results show that increasing the number of harmonics in STFS from 50 to 249 does not necessarily improve the CR because of the peaking phenomenon observed with high dimensionality. The wavelet cepstral coefficients (WCC) descriptor with 8 cycle time analysis windows presents a higher performance comparing to the STFS, discrete wavelet energy (DWE) and log wavelet energy (LWE) desc riptors. Recommendations are also given for selecting wavelet family, the mother wavelet order within the family and the decomposition depth. It turns out that the Daubechies wavelet of order 4 and decomposition depth 6 (or Coiflet wavelet with order 2 and depth 7) is recommended in order to achieve the better CR values. (More)

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Paper citation in several formats:
Hacine-Gharbi, A. and Ravier, P. (2018). Wavelet Cepstral Coefficients for Electrical Appliances Identification using Hidden Markov Models.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 541-549. DOI: 10.5220/0006662305410549

@conference{icpram18,
author={Abdenour Hacine{-}Gharbi. and Philippe Ravier.},
title={Wavelet Cepstral Coefficients for Electrical Appliances Identification using Hidden Markov Models},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={541-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006662305410549},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Wavelet Cepstral Coefficients for Electrical Appliances Identification using Hidden Markov Models
SN - 978-989-758-276-9
AU - Hacine-Gharbi, A.
AU - Ravier, P.
PY - 2018
SP - 541
EP - 549
DO - 10.5220/0006662305410549

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