Noise level estimation using haar wavelet packet trees for sensor robust outlier detection

Paolo Mercorelli; Alexander Frick

doi:10.1007/11751540_92

Noise level estimation using haar wavelet packet trees for sensor robust outlier detection

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

Noise level estimation using haar wavelet packet trees for sensor robust outlier detection. / Mercorelli, Paolo; Frick, Alexander.

Computational Science and Its Applications – ICCSA 2006: international conference, Glasgow, UK, May 8 - 11, 2006; proceedings. Hrsg. / Marina Gavrilova; Osvaldo Gervasi; Vipin Kumar; C.J. Kenneth Tan; David Taniar; Antonio Laganà; Youngsong Mun; Hyunseung Choo. Band 1 Berlin : Springer, 2006. S. 847-856 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 3980 LNCS).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

Mercorelli, P & Frick, A 2006, Noise level estimation using haar wavelet packet trees for sensor robust outlier detection. in M Gavrilova, O Gervasi, V Kumar, CJK Tan, D Taniar, A Laganà, Y Mun & H Choo (Hrsg.), Computational Science and Its Applications – ICCSA 2006: international conference, Glasgow, UK, May 8 - 11, 2006; proceedings. Bd. 1, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 3980 LNCS, Springer, Berlin, S. 847-856, International Conference on Computational Science and Its Applications - ICCSA 2006, Glasgow, Großbritannien / Vereinigtes Königreich, 08.05.06. https://doi.org/10.1007/11751540_92

APA

Mercorelli, P., & Frick, A. (2006). Noise level estimation using haar wavelet packet trees for sensor robust outlier detection. in M. Gavrilova, O. Gervasi, V. Kumar, C. J. K. Tan, D. Taniar, A. Laganà, Y. Mun, & H. Choo (Hrsg.), Computational Science and Its Applications – ICCSA 2006: international conference, Glasgow, UK, May 8 - 11, 2006; proceedings (Band 1, S. 847-856). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 3980 LNCS). Springer. https://doi.org/10.1007/11751540_92

Vancouver

Mercorelli P, Frick A. Noise level estimation using haar wavelet packet trees for sensor robust outlier detection. in Gavrilova M, Gervasi O, Kumar V, Tan CJK, Taniar D, Laganà A, Mun Y, Choo H, Hrsg., Computational Science and Its Applications – ICCSA 2006: international conference, Glasgow, UK, May 8 - 11, 2006; proceedings. Band 1. Berlin: Springer. 2006. S. 847-856. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11751540_92

Bibtex

@inbook{45c4ad451bf74d3680b448c5cb8d5afd,

title = "Noise level estimation using haar wavelet packet trees for sensor robust outlier detection",

abstract = "The paper is related to the on-line noise variance estimation. In practical use, it is important to estimate the noise level from the data rather than to assume that the noise level is known. The paper presented a free thresholding method related to the on-line peak noise variance estimation even for signal with small S/N ratio. The basic idea is to characterize the noise like an incoherent part of the measured signal. This is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum. The paper provides to show nice general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB{\textquoteright}s industry division.",

keywords = "Engineering, Noise Variance, Wavelet Packet, Minimum Description Length, Thresholding Method, Wavelet Shrinkage",

author = "Paolo Mercorelli and Alexander Frick",

note = "Conference code: 67786 Export Date: 22 May 2012 Source: Scopus doi: 10.1007/11751540_93 Language of Original Document: English Correspondence Address: Cattani, C.; Department of Mechanical Engineering, University of Salerno, Via Ponte Don Melillo-Invariante, 84084 Fisciano, SA, Italy; email: ccattani@unisa.it References: Amphlett, J.C., Baumert, R.M., Mann, R.F., Peppley, B.A., Roberge, P.R., Performance modelling of the ballard mark iv solid polymer electrolyte fuel cell (1995) Journal of Electrochemical Society, 142 (1), pp. 9-15; Cattani, C., Harmonic wavelet solutions of the schr{\"o}dinger equation (2003) International Journal of Fluid Mechanics Research, 5 (1-10), pp. 1064-2277. , ISNN; Cattani, C., Harmonic wavelets towards solution of nonlinear pde (2003) Computers and Mathematics with Applications, 50, pp. 1191-1210; Cattani, C., The wavelet-based technique in dispersive wave propagation (2003) International Applied Mechanics, 39 (4), pp. 493-501; Cattani, C., Ciancio, A., (2002) Wavelet Analysis of Linear Transverse Acoustic Waves, 80, pp. 1-20. , ISNN; Newland, D.E., (1993) Harmonic Wavelet Analysis. A, 443, pp. 203-225; Mercorelli, P., Rode, M., Terwiesch, P., (2001), System and methodology for dominant frequency detection by using a set of trigonometric wavelet functions. Patent N 7759 in Patentamt ABB Corporate Research Mannheim. Code in the German Patent Office 10 25 89 21. 6Mercorelli, P., Terwiesch, P., A black box identification in harmonic domain (2003) VDE European Transactions on Electrical Power, 13 (1), pp. 29-40; Springer, T.E., Zawoddzinski, T.A., Gottesfeld, S., Polymer electrolyte fuel cell model (1991) Journal of Electrochimical Society, 138 (8), pp. 2334-2342; Muniandy, S.V., Moroz, I.M., Galerkin modelling of the burgers equation using harmonic wavelets (1997) Phys. Lett, A, 235, pp. 352-356 Sponsors: Institute of Electrical Engineering, IEE, UK; University of Perugia, Italy; University of Calgary, Canada; University of Minnesota, MN, ISA; Queens' University of Belfast, UK; International Conference on Computational Science and Its Applications - ICCSA 2006, ICCSA ; Conference date: 08-05-2006 Through 11-05-2006",

year = "2006",

month = jan,

day = "1",

doi = "10.1007/11751540_92",

language = "English",

isbn = "3-540-34070-X",

volume = "1",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "847--856",

editor = "Marina Gavrilova and Osvaldo Gervasi and Vipin Kumar and Tan, {C.J. Kenneth} and David Taniar and Antonio Lagan{\`a} and Youngsong Mun and Hyunseung Choo",

booktitle = "Computational Science and Its Applications – ICCSA 2006",

address = "Germany",

}

RIS

TY - CHAP

T1 - Noise level estimation using haar wavelet packet trees for sensor robust outlier detection

AU - Mercorelli, Paolo

AU - Frick, Alexander

N1 - Conference code: 6

PY - 2006/1/1

Y1 - 2006/1/1

N2 - The paper is related to the on-line noise variance estimation. In practical use, it is important to estimate the noise level from the data rather than to assume that the noise level is known. The paper presented a free thresholding method related to the on-line peak noise variance estimation even for signal with small S/N ratio. The basic idea is to characterize the noise like an incoherent part of the measured signal. This is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum. The paper provides to show nice general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB’s industry division.

AB - The paper is related to the on-line noise variance estimation. In practical use, it is important to estimate the noise level from the data rather than to assume that the noise level is known. The paper presented a free thresholding method related to the on-line peak noise variance estimation even for signal with small S/N ratio. The basic idea is to characterize the noise like an incoherent part of the measured signal. This is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum. The paper provides to show nice general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB’s industry division.

KW - Engineering

KW - Noise Variance

KW - Wavelet Packet

KW - Minimum Description Length

KW - Thresholding Method

KW - Wavelet Shrinkage

UR - http://www.scopus.com/inward/record.url?scp=33745951086&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b1d00728-717d-328c-aacf-48cecd84b6e7/

U2 - 10.1007/11751540_92

DO - 10.1007/11751540_92

M3 - Article in conference proceedings

SN - 3-540-34070-X

SN - 978-3-540-34070-6

VL - 1

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 847

EP - 856

BT - Computational Science and Its Applications – ICCSA 2006

A2 - Gavrilova, Marina

A2 - Gervasi, Osvaldo

A2 - Kumar, Vipin

A2 - Tan, C.J. Kenneth

A2 - Taniar, David

A2 - Laganà, Antonio

A2 - Mun, Youngsong

A2 - Choo, Hyunseung

PB - Springer

CY - Berlin

T2 - International Conference on Computational Science and Its Applications - ICCSA 2006

Y2 - 8 May 2006 through 11 May 2006

ER -

DOI

https://doi.org/10.1007/11751540_92
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Noise level estimation using haar wavelet packet trees for sensor robust outlier detection

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