We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach

by Ritaban Dutta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 9
Year of Publication: 2013
Authors: Ritaban Dutta
10.5120/10495-5252

Ritaban Dutta . Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach. International Journal of Computer Applications. 63, 9 ( February 2013), 23-30. DOI=10.5120/10495-5252

@article{ 10.5120/10495-5252,
author = { Ritaban Dutta },
title = { Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 9 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number9/10495-5252/ },
doi = { 10.5120/10495-5252 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:13:53.073099+05:30
%A Ritaban Dutta
%T Environmental Sensor Drift Correction using Wavelet-mFCM-FIS Architecture: An Unsupervised Machine Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 9
%P 23-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A novel hybrid sensor informatics architecture based on Discrete Wavelet Transform (DWT), Fuzzy logic based clustering (FCM) and Fuzzy Rule based Inference system (FIS) has been investigated and proposed to estimate dynamic generic sensor drift during physical sampling. DWT has been used for sensor pre-processing; data dimension reduction and feature extraction from sensor time series, where as DWT-FCM based approach has been used to estimate the cumulative drift in our sensory system. An intelligent generalized algorithm using multiple FCM maps (m-FCM) has been proposed to implement the drift correction on any future unknown data sets. In the next stage a newly proposed DWT-FCM-FIS architecture has been tested on multisensory environmental data sets covering three consecutive years (2009 – 11) to predict probable month (environmental time of the year) with up to 93% accuracy. This novel hybrid data processing architecture have been implemented and tested upon a real time estuary sensory platform of temperature, conductivity and salinity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. This newly proposed approach could be an adaptive solution to tackle drifts in the sensor networks and improve overall monitoring quality.

References
  1. C. Sharman, "Tasmanian Marine Analysis Network: Portal," http://www. csiro. au/tasman/portal, July 2011.
  2. Ding hui, Liu Jun-hua, Shen Zhong-ru, Drift reduction of gas sensor by wavelet and principal component analysis, Sensors and Actuators B 96 (2003) 354–363.
  3. Alexander Vergaraa, Shankar Vembua, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer, Ramón Huertaa, Chemical gas sensor drift compensation using classifier ensembles, Sensors and Actuators B 166– 167 (2012) 320– 329.
  4. M. Zuppa, C. Distante, P. Siciliano, K. C. Persaud, Drift counteraction with multiple self-organizing maps for an electronic nose, Sens. Actuators B Chem. 98 (2004) 305–317.
  5. Australia's Integrated Marine Observing System (IMOS), http://imos. org. au/ (2012)
  6. C. D'Este, A. Terhorst, G. Timms, J. McCulloch, and C. Sharman, "Adaptive marine monitoring via the sensor web," in IEEE Conference on Intelligent Robots and Systems: Workshop on Robotics for Environmental Monitoring, San Francisco, USA, 2011.
  7. G. Timms, B. Howell, P. de Souza, C. D'Este, D. Smith, R. Atkins, and C. Sharman, Automated assessment and visualisation of measurement uncertainties in a low-cost marine sensor network, in European Geographic Union General Assembly, Vienna, Austria, 2011.
  8. R. R. Brooks and S. S. Iyengar, Multi-Sensor Fusion: Fundamentals and Applications with Software, Prentice Hall PTR, Upper Saddle River, New Jersey 07458, ISBN 0-13-901653-8.
  9. http://www. greenspan. com. au/ (2012)
  10. T. L. Chen, R. Z. YouA, Novel fault-tolerant sensor system for sensor drift compensation, Sensors and Actuators A 147 (2008) 623–632.
  11. A. Ziyatdinov, S. Marcoe, A. Chaudry, K. Persaud, P. Caminal, A. Pereraa, Drift compensation of gas sensor array data by common principal component analysis, Sensors and Actuators B 146 (2010) 460–465.
  12. A. Ray, R. Luck, An introduction to sensor signal validation in redundant measurement systems, Control Systems Magazine IEEE (1991) 44–49.
  13. R Dutta, D Morgan, N Baker, J Gardner, E Hines, Identification of Staphylococcus aureus infections (MRSA, MSSA and C-NS) in hospital environment: Electronic Nose based approach, Sensors and Actuators B: Chemical, 109 (2), pp. 355-362.
  14. F. W. Fairman, Linear Control Theory: The State Space Approach, John Wiley & Sons, 1998, pp. 147–166.
  15. R Dutta, OBJECT ORIENTED EXPERT ELECTRONIC NOSE SYSTEM, in ENCYCLOPEDIA OF SENSORS, Volume 7, Section 'O', Chapter 1, pp. 1-54, American Scientific Publishers. Encyclopedia of Sensors, 10-Volume Set. Edited by Craig A. Grimes, Elizabeth C. Dickey, and Michael V. Pishko. The Pennsylvania State University, University Park, USA. FOREWORD by Professor Rudolph A. Marcus, Nobel Prize Laureate, 2006ca. 8,000 pages, Hardcover, ISBN: 1-58883-056-X.
  16. F. Hossein-Babaei, V. Ghafarinia, Compensation for the drift-like terms caused by environmental fluctuations in the responses of chemoresistive gas sensors, Sensors and Actuators B 143 (2010) 641–648.
  17. G. Hu,W. Chen, Y. Chen, D. Liu, Adaptive kalman filtering for vehicle navigation, Journal of global position systems 2 (1) (2003) 42–47.
  18. B. Howell, Tasmanian Marine Analysis Network: Web Data Service, http://www. csiro. au/tasman/WDS/wds?request=GetDeployment&format=xml, (2011).
  19. Wikipedia Website Main Page (2012)
  20. J. J. Slotine, W. Li, Applied Nonlinear Control, Prentice Hall, 1991, pp. 40–97.
  21. C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, Upper Saddle River, NJ 07458, ISBN 0-13-235169-2, 1996.
  22. H. K. Khalil, Nonlinear Systems, third edition, Prentice Hall, 2000, pp. 111–181.
  23. www. mathworks. com (2012).
Index Terms

Computer Science
Information Sciences

Keywords

Sensor Drift Drift Area Discrete Wavelet Transform Fuzzy C means Fuzzy Inference system

Powered by PhDFocusTM