Combination of SVD and Wavelet Transform for Oil Discrimination on 3-D Fluorescence Spectra

Guang Jun Tian; Lu Lu Yang; Zi Chen Yang

doi:10.4028/www.scientific.net/AMM.740.639

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 740Combination of SVD and Wavelet Transform for Oil...

Combination of SVD and Wavelet Transform for Oil Discrimination on 3-D Fluorescence Spectra

Abstract:

Singular Value Decomposition used in spectrum feature extraction, often discards small component that may be important for identifying mineral oil products. This work presents a new method using the Singular Value Division (SVD) on Wavelet Transform (WT) with three-dimensional fluorescence spectra as the source of oil features. WT-SVD feature based fuzzy classification (FCM) is implemented and comparable or better results are yielded in more accurate, and more robust than SVD performance under random noise conditions. The result means that WT-SVD method can strike a balance between data compression and preservation of small valid information in feature extraction of three-dimensional fluorescence spectra of mineral oils. This method is conducive to oil discrimination and pollution analysis in water environment monitoring.

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Periodical:

Applied Mechanics and Materials (Volume 740)

Pages:

639-643

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.740.639

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Online since:

March 2015

Authors:

Guang Jun Tian*, Lu Lu Yang, Zi Chen Yang

Keywords:

3-D Fluorescence Spectrum, Feature Extraction, Fuzzy Clustering, Singular Value Decomposition, Wavelet Transform

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* - Corresponding Author

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