---

Journal of Vibration Testing and System Dynamics 6(1) (2022) 13--20 | DOI:10.5890/JVTSD.2022.03.002

Lin-Lin Cheng, Lei-Lei Chen, Qiao-Mei Wang, Ming-Ju Chen, Xing-Zhong Xiong

Automation and information engineering institute, Sichuan University of Science & Engineering, Yibin, Sichuan, China

Download Full Text PDF

 

Abstract

In order to effectively recognize and identify liquor quality by nuclear magnetic resonance (NMR) spectrum data, we propose a hybrid data processing and recognition algorithm. In this algorithm Kernel principal component analysis (KPCA) was used to remove the correlation and reduce dimension of the NMR spectrum, and then convolutional neural network (CNN) was used for classification and identification of the processed spectrum. The compared experiment result show that the proposed KPCA+CNN method can obtain higher accuracy than CNN and PCA+CNN methods based on the NMR spectrum. The KPCA+CNN algorithm has good application prospect and reference value.

Acknowledgments

This research was supported by the Open Fund Project of the Artificial Intelligence Key Laboratory of Sichuan Province (Grant no.2020RZY02), the Fund Project of Sichuan Yibin Wuliangye Group Co. LTD (Grant no. HX2020034).

References

1. [1]	Yang, X.Y. and Wang, N. (2011), Chongqing University: Test the waters to move the library [J], China Education Network, 000(004), 27-28.

2. [2]	Crowley, T.E. (2020), Nuclear magnetic resonance spectroscopy[J], Purification and Characterization of Secondary Metabolites, 67-78.

3. [3]	Geladi, P. and Linderholm, J. (2020), Principal Component Analysis[M], Reference Module in Chemistry, Molecular Sciences and Chemical Engineering.

4. [4]	Mairal, J., Elad, M., and Sapiro, G. (2007), Sparse Representation for Color Image Restoration[J], IEEE Transactions on Image Processing, 17(1), 53-69.

5. [5]	Zhou, W., Wang, Y., and Xiao, X.B., etc. (2018), Auxiliary diagnosis of Alzheimer's disease based on KPCA algorithm[J], Chinese Journal of Medical Physics, 035(004), 404-409.

6. [6]	Cao, H., Li, J.J., Wang, T., etc. (2016), The invention relates to a new biological oscillating incubator[J], Optical Instruments, (1), 91-94.

7. [7]	Adegbola, O.A., Adeyemo, I.A., Semire, F.A., Popoola, S.I., and Atayero, A.A. (2020), A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system, Telkomnika, 18(4), 1892-1896.

8. [8]	Choi, S.W. and Lee, I.B. (2004), Nonlinear dynamic process monitoring based on dynamic kernel PCA, Chemical Engineering Science, 59(24), 5897-5908.

9. [9]	Zhang, Q.Q., Zhang, S.Q., Lei, Z.Y. (2017), Chinese emotion classification based on improved convolutional neural network[J], Computer Engineering and Applications, (22), 116-120.

10. [10]	Yang, J., Qu, X.D., and Chang, M. (2019), An intelligent singular value diagnostic method for concrete dam deformation monitoring, Water Science and Engineering, 12(3), 205-212.

11. [11]	Choudhary, P. and Hazra, A. (2019), Chest disease radiography in twofold: using convolutional neural networks and transfer learning, Evolving Systems, 1-13.

12. [12]	Liang, M. and Hu, X. (2015), Recurrent convolutional neural network for object recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, 3367-3375.

13. [13]	Fei-Yan, Z., Lin-Peng, J., and Jun, D. (2017), Review of Convolutional Neural Network[J], Chinese Journal of Computers.

14. [14]	Niu, D.X., Ma, T.N., Wang, H.C. etc. (2017), Based on KPCA and the NSGA Optimizing the short-term load forecast of ELECTRIC vehicle charging station with CNN parameters[J], Electric power construction, (3).