Identification of automobile transmission fluid using hyperspectral imaging technology
Keywords:
transmission fluid, hyperspectral image, sparse representation, principal component analysis, identificationAbstract
Abstract: An identification method combining sparse representation with principal component analysis (PCA) was proposed for discriminating varieties of transmission fluid of automobile by using hyperspectral imaging technology. Principal component analysis was applied to obtain the characteristic information in the 874-1 733 nm spectra. For each transmission fluid variety, 80 samples were randomly selected as the training set, and 20 samples as the testing set. The eigenvectors of all training samples form the matrix were used for the sparse representation, and the problem of transmission fluid types classification was transformed into one to solve a sample expressed by the overall training sample matrix through optimization under the 11 norm. The results demonstrate that the accuracy of the algorithm that was composed of sparse representation and principal component analysis (PCA) was 93%. The accuracy is higher than those of PCA-LDA (Linear Discriminant Analysis) and PCA- LS-SVM (Least Squares Support Vector Machine). Therefore, the proposed method provides a better approach for the identification of transmission fluid types. Keywords: transmission fluid, hyperspectral image, sparse representation, principal component analysis, identification DOI: 10.3965/j.ijabe.20140704.009 Citation: Jiang L L, Yu X J, He Y. Identification of transmission fluid of automobile by hyperspectral imaging technology based on sparse representation. Int J Agric & Biol Eng, 2014; 7(4): 81-85.References
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[1] Yao M. Identification of the Automatic Transmission Fluid Replacement. Journal of Car Driver, 2004.
[2] Zou W, Fang H, Liu F, Zhou K Y, Bao Y D, He Y. Identification of rapeseed varieties based on hyperspectral imagery. Journal of Zhejiang University (Agriculture & Life Sciences), 2011; 37(2): 175–180.
[3] Cheng G S, Guo J X, Shi Z, Amuti R, Kang Y X. Prediction of the Weight of Xinjiang Fuji Apple by Hyperspectral Imaging Techniques. Journal of Xinjiang Agricultural University, 2011; 34(3): 249–252.
[4] Cai J R, Wang J H, Chen Q S, Zhao J W. Detection of rust in citrus by hyperspectral imaging technology and band ratio algorithm. Transaction of the CSAE, 2009; 25(1): 127–131.
[5] Hong T S, Qiao J, Wang N, Ngadi, M O, Zhao Z X, Li Z. Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique. Transactions of the CSAE, 2007; 23(2): 151–155.
[6] Xing J, Baerdemaeker J D. Bruise detection on ‘Jonagold’ apples using hyperspectral imaging. Postharvest Biology and Technology, 2005; 37: 152- 162.
[7] Xu S, He J G, Yi Dong, He X G. Nondestructive detection of sugar content in long jujude based on hyperspectral imaging technique. Food and Machinery, 2012; 28(6): 168–170.
[8] Yang S Q, Ning J F, He D J. Identification of varieties of rice based on sparse representation. Transaction of the CSAE, 2011; 27(3): 191–195.
[9] Wright J, Yang A Y, Ganesh A, Sastry S S, Ma Y. Robust face recognition via sparse representation. IEEE T. Pattern Anal, 2009; 31(2): 210-227.
[10] Li X Z, Wu J, Cui Z M, Chen J M. Sparse representation method of vehicle recognition in complex traffic scenes. Journal of Image and Graphics, 2012; 17(3): 387–392.
[11] Absdi, H. Williams L J. Principal component analysis. WIRES Computational Statistics, 2010; 2: 433–459.
[12] Yang S Q, Ning J F, He D J. Identification of varieties of rice based on sparse representation. Transaction of the CSAE, 2011; 27(3): 191–195.
[13] Koh K, Kim S J, Boyd S. Simple MATLAB solver for l1-regularized least squares problems. http://www.stanford. edu/~ boyd/l1_ls/ Accessed on [2008-05-15].
[14] Pelckmans K, Suykens J A K, Gestel T V, Brabanter J D, Lukas L, Hamers B, et al. Least squares-support vector machines. http://www.esat.kuleuven.be/sista/lssvmlab/ Accessed on [2011-08-16].
[15] Bruckstein A, Donoho D, Elad M. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 2009; 51(1): 34–81.
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Published
2014-08-25
How to Cite
Lulu, J., Xinjie, Y., & Yong, H. (2014). Identification of automobile transmission fluid using hyperspectral imaging technology. International Journal of Agricultural and Biological Engineering, 7(4), 81–85. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/930
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Information Technology, Sensors and Control Systems
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