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

• Autorzy: Osowski S. , Siwek K.

Identyfikatory

DOI

10.1515/mms-2017-0015

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 1
• Strony: 27--44
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr., wzory

Twórcy

autor

Osowski S.

• Warsaw University of Technology, Faculty of Electrical Engineering, Pl. Politechniki 1, 00-661 Warsaw, Poland
• Military University of Technology, Faculty of Electronic Engineering, Gen. S. Kaliskiego 1, 00-908 Warsaw, Poland

autor

Siwek K.

• Warsaw University of Technology, Faculty of Electrical Engineering, Pl. Politechniki 1, 00-661 Warsaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).