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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
25. Abe, S., "Fuzzy support vector machines for multi label classification," Pattern Recognition, Vol. 48, No. 6, 2110-2117, Jun. 2015.
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.
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.