Acknowledgement
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF-2018R1D1A1B07045976) in (2018).
References
- A. I. Aderibigbe, Isaac Samuel, B. Adetokun and S. Tobi, "Monte Carlo Simulation Approach to Soil Layer Resistivity Modelling for Grounding System," International Journal of Applied Engineering Research, Volume 12, pp. 13759-13766, 2017.
- B. Zhang, X. Cui, L. Li, and J. L. He, "Parameter estimation of horizontal multilayer earth by complex image method," IEEE Trans. on Power Delivery, Vol. 20, pp. 1394-1401, 2005. https://doi.org/10.1109/TPWRD.2004.834673
- H.C Kim, C.J Boo, and M.J Kang, "Estimation of kernel function using the measured apparent earth Resistivity," International Journal of Advanced Smart Convergence Vol.9 No.3 pp.97-104, 2020 http://dx.doi.org/10.7236/IJASC.2020.9.3.97
- H.C Kim and M.J Kang, "A Fast Calculation of Apparent Soil Resistivity Using Exponential Sampling Method," International Journal of Advanced Culture Technology, Vol.7 No.4 pp.268-273, 2019 https://doi.org/10.17703/IJACT.2019.7.4.268.
- T. Takahashi and T. Kawase, "Analysis of apparent resistivity in a multi-layer earth structure," IEEE Trans. on Power Delivery, Vol. 5, pp. 604-612, 1990. https://doi.org/10.1109/61.53062
- J. Alamo, "A comparison among eight different techniques to achieve an optimum estimation of electrical grounding parameters in two-layered earth," IEEE Trans. Power Del., vol. 8, pp. 1890-1899, Oct. 1993. https://doi.org/10.1109/61.248299
- H. Yang, J. Yuan, and W. Zong, "Determination of three-layer earth model from Wenner four-probe test data," IEEE Trans. Magn., vol. 37, pp. 3684-3687, Sep. 2001. https://doi.org/10.1109/20.952690
- Ammar Alali1, Frank Dale Morgan, and Darrell Coles, "Novel Approach for 1D Resistivity Inversion Using the Systematically Determined Optimum Number of Layers" Journal of Geology & Geophysics, Vol. 9, No: 481, 2020. http://hdl.handle.net/1721.1/117908
- U.K. Singh, R.K. Tiwari and S. B. Singh, "One-dimensional of geo-electrical resisitivity sounding data using artificial neural networks- a case study," Computer & Geoscience, Vol. 31, pp.99-108, 2005. https://doi.org/10.1016/j.cageo.2004.09.014
- Liheng Zhong, Lana Hu, and Hang Zhou, "Deep learning based multi-temporal crop classification," Remote Sensing Environment, vol. 221, pp430-443, Feb. 2019. DOI: 10.1016/j.rse.2018.11.032
- Jiacheng Sun, Xiangyong Cao, and Zhenguo LI, "New Interpretations of Normalization Methods in Deep Learning," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34 No. 04, 2020 DOI: https://doi.org/10.1609/aaai.v34i04.6046
- K. H. Jin, M. T. Mccann, E. Froustey, and M. Unser, "Deep convolutional neural network for inverse problems in imaging," IEEE Trans. Image Process., vol. 26, no. 9, pp. 4509-4522, Sep. 2017. https://doi.org/10.1109/TIP.2017.2713099
- H.C Kim and M.J Kang, "A comparison of methods to reduce overfitting in neural networks," International Journal of Advanced Smart Convergence, Vol.9 No.2 pp.173-178, 2020. http://dx.doi.org/10.7236/IJASC.2020.9.2.173
- M.J Kang and H.C Kim, "Comparison of Weight Initialization Techniques for Deep Neural Networks," International Journal of Advanced Culture Technology, Vol.7 No.4 pp.283-284, 2019 DOI 10.17703/IJACT.2019.7.4.283