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Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

4 Issues per year


IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228

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2300-1941
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Volume 24, Issue 1 (Mar 2017)

Issues

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Stanisław Osowski
  • Corresponding author
  • 1) Warsaw University of Technology, Faculty of Electrical Engineering, Pl. Politechniki 1, 00-661 Warsaw, Poland
  • 2) Military University of Technology, Faculty of Electronic Engineering, Gen. S. Kaliskiego 1, 00-908 Warsaw, Poland
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  • De Gruyter OnlineGoogle Scholar
/ Krzysztof Siwek
Published Online: 2017-03-20 | DOI: https://doi.org/10.1515/mms-2017-0015

Abstract

The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

Keywords: data mining; electronic nose; gasoline blends; random forest; support vector machine; wavelet denoising

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About the article

Received: 2016-08-11

Accepted: 2017-01-14

Published Online: 2017-03-20

Published in Print: 2017-03-01


Citation Information: Metrology and Measurement Systems, ISSN (Online) 2300-1941, DOI: https://doi.org/10.1515/mms-2017-0015.

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© 2017 Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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