Details Details PDF BIBTEX RIS Title Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose Journal title Metrology and Measurement Systems Yearbook 2017 Volume vol. 24 Issue No 1 Authors Osowski, Stanisław ; Siwek, Krzysztof Keywords Data Mining ; electronic nose ; gasoline blends ; random forest ; support vector machine ; wavelet denoising Divisions of PAS Nauki Techniczne Publisher Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation Date 2017.03.30 Type Artykuły / Articles Identifier DOI: 10.1515/mms-2017-0015 ; ISSN 0860-8229 Source Metrology and Measurement Systems; 2017; vol. 24; No 1 Pages 27-44 References Mallat (1989), A theory for multiresolution signal decomposition : the wavelet representation Transactions on Pattern Analysis and Machine, IEEE Intelligence, 11, 674. ; Di Natale (2005), Pre - processing and pattern recognition methods for artificial olfaction systems : a review, Metrol Meas Syst, 12. ; Liu (2013), Comparison of random forest , support vector machine back propagation neural network for electronic tongue data classification : Application to the recognition of orange beverage Chinese vinegar and, Sensors Actuators, 177. ; Brudzewski (2006), Classification of gasoline with supplement of bio - products by means of an electronic nose SVM neural network and, Sensors Actuators, 113. ; Tan (2006), Introduction to data mining, Education. ; Botre (2010), Embedded electronic nose supporting software tool for its parameter optimization and, Sensors Actuators, 146. ; Bielecki (2012), Sensors and systems for the detection of explosive devices an overview, Metrol Meas Syst, 19, doi.org/10.2478/v10178-012-0001-3 ; Zuppa (2004), Drift counteraction with multiple self - organizing maps for an electronic nose and, Sensors Actuators, 98. ; Daubechies (1992), Ten lectures on wavelets Philadelphia, SIAM. ; Breiman (2001), Random forests, Machine Learning, 45, 5, doi.org/10.1023/A:1010933404324 ; MathWorks (2014), Matlab user manual Natick, USA. ; Hassanpour (2008), A time - frequency approach for noise reduction, Digital Signal Processing, 18, 728, doi.org/10.1016/j.dsp.2007.09.014 ; Fonollosa (2016), Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization and, Sensors Actuators, 236. ; Boeker (2014), On Electronic Nose methodology and, Sensors Actuators, 204. ; Murguía (2013), Two - dimensional wavelet transform feature extraction for porous silicon chemical sensors, Analytica Chimica Acta, 785. ; Jha (2011), Denoising by singular value decomposition its application to electronic nose data processing, IEEE Sensors Journal, 11, 1, doi.org/10.1109/JSEN.2010.2049351 ; Osowski (2004), Neuro - fuzzy TSK network for calibration of semiconductor sensor array for gas measurements on Measurements, IEEE Trans Instrumentation, 53. ; Pardo (2005), Classification of electronic nose data with support vector machines and, Sensors Actuators, 107. ; McCarrick (1996), Fuel identification by neural network analysis of the response of vapour - sensitive sensor arrays, Analytical Chemistry, 68. ; Guney (2012), Multiclass classification of n - butanol concentrations with k - nearest neighbor algorithm support vector machine in an electronic nose, Sensors Actuators, 166. ; Wiziack (2009), A sensor array based on mass capacitance transducers for the detection of adulterated gasolines and, Sensors Actuators, 140.