NDLI: A signal denoising technique based on wavelets modulus maxima lines and a self-scalable grid classifier

Content Provider	IEEE Xplore Digital Library
Author	Vasconcelos Machado, R.G. de Oliveira Mota, H.
Copyright Year	2015
Description	Author affiliation: Electr. Eng., Fed. Univ. of Minas Gerais, Belo Horizonte, Brazil (Vasconcelos Machado, R.G.) \|\| Dept. of Electr. Eng., Fed. Univ. of Minas Gerais, Belo Horizonte, Brazil (de Oliveira Mota, H.)
Abstract	This paper presents the description of a signal processing technique using the Wavelets Transform and a self-scalable grid classifier. The procedure is based on the cycle-spinning approach applied to the Translation-invariant Wavelet Transform. It exploits the characteristics of the Wavelets modulus maxima propagation along decomposition levels (scales) as the criterion to select the relevant coefficients. Selection was performed by a data classifier inspired on a Self-organizing Map but with enhancements to incorporate self-scalability and multiple instance learning capabilities. The procedure was employed for the processing of Partial Discharge signals, which is a technique for the diagnostics of high-voltage equipment. We performed comparisons with standard form classifiers based on the Multilayer Perceptron and Support Vector Machines. The results show that the technique allows the same orders of accuracy and generalization of those classifiers, but with the advantages of self-scalability, dimensional independence, low processing cost and high degree of parallelization.
Starting Page	1
Ending Page	6
File Size	2893645
Page Count	6
File Format	PDF
e-ISBN	9781467396042
DOI	10.1109/SiPS.2015.7344974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Partial discharges Neurons Wavelet transforms Training Noise reduction Transient analysis partial discharges wavelets transform denoising self-organizing maps multiple-instance learning
Content Type	Text
Resource Type	Article

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