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2020-06-27
GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression
By
Shahram Hosseinzadeh,

    Dr. Shahram Hosseinzadeh

    Department of Engineering

    Azarbaijan University of Tarbiat Moallem

    Iran

    Homepage

Mehdi Shaghaghi

    Dr. Mehdi Shaghaghi

    Electronic Campus

    Islamic Azad University

    Iran

    Homepage

Progress In Electromagnetics Research M, Vol. 93, 175-184, 2020
doi:10.2528/PIERM20050805 | Google Scholar

Abstract
In this paper, the problem of determining the depth and radius of a circular pipe along with the soil characteristics is studied, using electromagnetic waves with a fuzzy support vector machine as well as a fuzzy support vector machine. To this end, three neural network based fuzzy support vectors are used to determine the soil, depth and dimensions. Also, using the 2D time domain numerical simulations of electromagnetic field scattering, along with MATLAB software, 1030 data are generated for training as well as neural network verification. Given the fact that for each of the three parameters the nature of the problem is different, separate neural networks are considered with different parameters, thus the number of different data for the network training is considered. In all three cases, the neural network parameters are optimized using genetic algorithm to reduce the error and also reduce the number of support vectors. It should be noted that the objective function of the genetic algorithm consists of two components of the error, as well as the number of membership functions, which can be determined by determining a control parameter. For soil permittivity, the algorithm can accurately predict 93% of permittivities, and it decreases to 89.8 for the pipe depth determination. For diameter it is seen that for 69.3 of the cases the algorithm can correctly classify the pipes.
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Citation
Shahram Hosseinzadeh, and Mehdi Shaghaghi, "GPR Data Regression and Clustering by the Fuzzy Support Vector Machine and Regression," Progress In Electromagnetics Research M, Vol. 93, 175-184, 2020.
doi:10.2528/PIERM20050805
References

1. Jol, H. M., Ground Penetrating Radar: Theory and Application, 1st Ed., Elsevier Science, 2009.

2. Daniels, D. J., Ground Penetrating Radar, 2nd Ed., The Institution of Engineering and Technology, Oct. 1, 2004.

3. Persico, R., Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing, 1st Ed., Wiley-IEEE Press, Jun. 9, 2014.

4. Wang, J. and Y. Su, "Fast detection of GPR objects with cross correlation and hough transform," Progress In Electromagnetics Research C, Vol. 38, 229-239, 2013.
doi:10.2528/PIERC13022510        Google Scholar

5. Ratto, C. R., K. D. Morton, L. M. Collins, and P. A. Torrione, "Analysis of linear prediction for soil characterization in GPR data for countermine applications," Sensing and Imaging, Vol. 15, No. 1, 1-20, 2014.
doi:10.1007/s11220-014-0086-8        Google Scholar

6. Thomas, S. B. and L. P. Roy, "Thin coal layer thickness estimation using MUSIC algorithm," Proceeding of 2017 IEEE Microwaves, Radar and Remote Sensing Symposium, 99-104, Ukraine, Aug. 29-31, 2017.        Google Scholar

7. Bastard, C. L., V. Baltazart, Y. Wang, and J. Saillard, "Thin pavement thickness estimation using GPR with high resolution and supper-resolution methods," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 5, 2511-2519, Aug. 2007.
doi:10.1109/TGRS.2007.900982        Google Scholar

8. Shrestha, S. and I. Arai, "Signal processing of ground penetrating radar using spectral estimation techniques to estimate the position of buried targets," EURASIP Journal on Applied Signal Processing, Vol. 12, 1198-1209, 2003.        Google Scholar

9. Pan, J., C. L. Bastard, Y. Wang, and M. Sun, "Time-delay estimation using ground-penetrating radar with a support vector regression-based linear prediction method," IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 5, 2833-2840, May 2018, DOI: 10.1109/TGRS.2017.2784567.
doi:10.1109/TGRS.2017.2784567        Google Scholar

10. Xie, X., P. Li, H. Qin, L. Liu, and D. C. Nobes, "GPR identification of voids inside concrete based on the support vector machine algorithm," Journal of Geophysics and Engineering, Vol. 10, No. 3, 034002, 2013.
doi:10.1088/1742-2132/10/3/034002        Google Scholar

11. Williams, R. M., L. E. Ray, J. H. Lever, and A. M. Burzynski, "Crevasse detection in ice sheets using ground penetrating radar and machine learning," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 12, 4836-4848, Dec. 2014.
doi:10.1109/JSTARS.2014.2332872        Google Scholar

12. Zou, H. and F. Yang, "Study on signal interpretation of GPR based on support vector machines," Proceeding of International Conference on Life System Modeling and Simulation, 533-539, LSMS, 2007.        Google Scholar

13. El-Mahallawy, M. S. and M. Hashim, "Material classification of underground utilities from GPR images using DCT-based SVM approach," IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 6, 1542-1546, Nov. 2013.
doi:10.1109/LGRS.2013.2261796        Google Scholar

14. Bastard, C. L., Y. Wang, V. Baltazart, and X. Derobert, "Time delay and permittivity estimation by ground-penetrating radar with support vector regression," IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 4, 873-877, Apr. 2014.
doi:10.1109/LGRS.2013.2280500        Google Scholar

15. Shao, W., A. Bouzerdoum, S. L. Phung, L. Su, B. Indraratna, and C. Rujikiatkamjorn, "Automatic classification of ground penetrating radar signals for railway ballast assessment," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 10, 3961-3972, Oct. 2011.
doi:10.1109/TGRS.2011.2128328        Google Scholar

16. Sharma, P., B. Kumar, D. Singh, and S. P. Gaba, "Non-metallic pipe detection using SF-GPR: A new approach using neural network," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6609-6612, Beijing, China, Jul. 2016.        Google Scholar

17. Sharma, P., B. Kumar, and D. Singh, "Novel adaptive buried nonmetallic pipe crack detection algorithm for ground penetrating radar," Progress In Electromagnetics Research M, Vol. 65, 79-90, 2018.
doi:10.2528/PIERM17101002        Google Scholar

18. Kumar, B., P. Sharma, and D. Singh, "Development of an efficient approach for MMW imaging system to identify concealed targets inside the book," Microwave and Optical Technology Letter, Vol. 59, No. 12, 2982-2990, Dec. 2017.
doi:10.1002/mop.30858        Google Scholar

19. Dumin, O., V. Plakhtii, D. Shyrokorad, O. Prishchenko, and G. Pochanin, "UWB subsurface radiolocation for object location classification by artificial neural networks based on discrete tomography approach," 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), 182-187, 2019.
doi:10.1109/UKRCON.2019.8879827        Google Scholar

20. Dumin, O., V. Plakhtii, O. Pryshchenko, and G. Pochanin, "Comparison of ANN and cross-correlation approaches for ultra short pulse subsurface survey," 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 1-6, Feb. 25-29, 2020.        Google Scholar

21. http://www.svms.org/history.html, Last Visited 09/07/2018.

22. Abe, S., Support Vector Machines for Pattern Classification, 2nd Ed., Springer, 2010.
doi:10.1007/978-1-84996-098-4

23. Inoue, T. and S. Abe, "Fuzzy support vector machines for pattern classification," Proceedings of International Joint Conference on Neural Networks, 2001, IJCNN'01, 1449-1454, 2001.        Google Scholar

24. Lin, C. F. and S. D. Wang, "Fuzzy support vector machines," IEEE Transactions on Neural Networks, Vol. 13, No. 2, 467-471, Mar. 2002.        Google Scholar

25. Abe, S., "Fuzzy support vector machines for multi label classification," Pattern Recognition, Vol. 48, No. 6, 2110-2117, Jun. 2015.
doi:10.1016/j.patcog.2015.01.009        Google Scholar

26. Wu, K. and K.-H. Yap, "Fuzzy SVM for content-based image retrieval a pseudo-label support vector machine framework," IEEE Computational Intelligence Magazine, 10-16, May 2006.        Google Scholar

27. Sevakula, R. K. and N. K. Verma, "Compounding general purpose membership functions for fuzzy support vector machine under noisy environment," IEEE Transactions on Fuzzy Systems, Vol. 25, No. 6, 1446-1459, Dec. 2017.
doi:10.1109/TFUZZ.2017.2722421        Google Scholar

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