1. //
  2. Start
  3. //
  4. Publications
  5. Median based algorithm as an entropy fun...
  6. Output formats
  7. //

Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation

Research output: Contributions to collected editions/works › Chapter › peer-review

Standard

Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. / Mercorelli, Paolo.
New Developments in Mathematics Research. ed. / Natalie L Clarke; Alex P Ronson. Nova Science Publishers, Inc., 2011. p. 85-104.

Research output: Contributions to collected editions/works › Chapter › peer-review

Harvard

Mercorelli, P 2011, Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. in NL Clarke & AP Ronson (eds), New Developments in Mathematics Research. Nova Science Publishers, Inc., pp. 85-104.

APA

Mercorelli, P. (2011). Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. In N. L. Clarke, & A. P. Ronson (Eds.), New Developments in Mathematics Research (pp. 85-104). Nova Science Publishers, Inc..

Vancouver

Mercorelli P. Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. In Clarke NL, Ronson AP, editors, New Developments in Mathematics Research. Nova Science Publishers, Inc. 2011. p. 85-104

Bibtex

@inbook{8b524f19b1944e37b6f09474d286ca03,
title = "Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation",
abstract = "The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.",
keywords = "Data reconciliation, Fault detection, Haar functions, Noise detection, Signal processing, Variance, Wavelets, Wavelets'packets, Engineering",
author = "Paolo Mercorelli",
year = "2011",
language = "English",
isbn = "9781613242520",
pages = "85--104",
editor = "Clarke, {Natalie L} and Ronson, {Alex P}",
booktitle = "New Developments in Mathematics Research",
publisher = "Nova Science Publishers, Inc.",
address = "United States",

}

RIS

TY - CHAP

T1 - Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation

AU - Mercorelli, Paolo

PY - 2011

Y1 - 2011

N2 - The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.

AB - The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.

KW - Data reconciliation

KW - Fault detection

KW - Haar functions

KW - Noise detection

KW - Signal processing

KW - Variance

KW - Wavelets

KW - Wavelets'packets

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84895347837&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:84895347837

SN - 9781613242520

SP - 85

EP - 104

BT - New Developments in Mathematics Research

A2 - Clarke, Natalie L

A2 - Ronson, Alex P

PB - Nova Science Publishers, Inc.

ER -

Other publications by the same author(s)

An Over-Actuated Hexacopter Tilt-Rotor UAV Prototype for Agriculture of Precision: Modeling and Control

Pimentel, G. O., Santos, M. F. D., Lima, J., Mercorelli, P. & Fernandes, F. M., 15.01.2025, In: Sensors. 25, 2, 27 p., 479.

Research output: Journal contributions › Journal articles › Research › peer-review

Automatic Tuning of Extended Kalman Filter in Synchronous Reluctance Motor Drives with a Master-Slave Configuration

Rigon, S., Haus, B., Mercorelli, P. & Zigliotto, M., 27.02.2025, In: IEEE Open Journal of Power Electronics. 6, p. 734-746 13 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Business Analytics and Making Decision Based on Kalman Filter in Stock Prediction Case

Kurniawan, S. & Mercorelli, P., 27.05.2025, 5th Congress on Intelligent Systems, CIS 2024: Volume 1. Kumar, S., Mary Anita, E. A., Kim, J. H. & Nagar, A. (eds.). Springer Science and Business Media Deutschland, p. 309-320 12 p. (Lecture Notes in Networks and Systems; vol. 1275).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Dynamic Performance Analysis and Fault Ride-Through Enhancement by a Modified Fault Current Protection Scheme of a Grid-Connected Doubly Fed Induction Generator

Talani, R. A., Kaloi, G. S., Ali, A., Bijarani, M. A., Abbas, G., Hatatah, M., Mercorelli, P. & Touti, E., 02.2025, In: Machines. 13, 2, 20 p., 110.

Research output: Journal contributions › Journal articles › Research › peer-review

Inner loop model predictive control and outer loop PI reference governor for PMSMs with input and state saturation for torque control

Zwerger, T. & Mercorelli, P., 01.02.2025, In: Mathematics and Computers in Simulation. 228, p. 178-201 24 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Recently viewed

Activities

  1. Presentaton of the paper entitled "Estimation of Parameters in the SIR Model using a Particle Swarm Optimization Algorithm" at the 2nd International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2023)
  2. Model Predictive Control for Switching Gain Adaptation in a Sliding Mode Controller of a DC Drive with Nonlinear Friction
  3. Perturbation Analysis to Design a Robust Decoupling Geometric Technique in Linear Multi-Input Multi-Output Systems
  4. Interpreting Strings, Weaving Threads – Structuring Provenance Data with AI
  5. Can the ability to identify criteria explain why some selection procedures work? Results and unresolved issues
  6. SIAM Conference on Applications of Dynamical Systems - DS 2023
  7. Data-efficient Pattern Detection in Elite Soccer
  8. Plenary lecture entitled: "Wavelet Packets for Applications in Signal Processing and Control Systems"
  9. Presentation of the paper entitled: "Combining a PI Controller with an Adaptive Feedforward Control in PMSM"
  10. Predicting dropout in Internet Interventions - A framework to analyse user journey data
  11. Institutional Proxy Representatives of Future Generations: A Comparative Analysis of Types and Design Features

Publications

  1. Agile knowledge graph testing with TESTaLOD
  2. How to get really smart: Modeling retest and training effects in ability testing using computer-generated figural matrix items
  3. Semi-supervised learning for structured output variables
  4. Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems
  5. Different complex word problems require different combinations of cognitive skills
  6. Simultaneous Constrained Adaptive Item Selection for Group-Based Testing
  7. Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm
  8. Applying Bayesian Parameter Estimation to A/B Tests in e-Business Applications
  9. Problem structuring for transitions
  10. Diffusion-driven microstructure evolution in OpenCalphad
  11. How to combine collaboration scripts and heuristic worked examples to foster mathematical argumentation - when working memory matters
  12. An Improved Approach to the Semi-Process-Oriented Implementation of Standardised ERP-Systems
  13. Global temporal typing patterns in foreign language writing
  14. Gain Scheduling Controller for Improving Level Control Performance
  15. Retest effects in matrix test performance
  16. Anatomy of Haar Wavelet Filter and Its Implementation for Signal Processing
  17. Template-based Question Answering using Recursive Neural Networks
  18. Sequencing and fading worked examples and collaboration scripts to foster mathematical argumentation - working memory capacity matters for fading
  19. Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis