참고문헌 (11)

1. H. G. Kim, “Electromagnetic band gap structure for frequency selective simultaneous switching noise suppression and study on its miniaturization,” M.S. thesis, Sungkyunkwan University, 2017. 

2. J. H. Kwon, H. D. Choi, “Research trend of electromagnetic interference reduction technology using EBG structure,” The Proceedings of The Korean Institute of Electromagnetic Engineering and Science, vol. 18, no. 1, pp. 32-42, Jan. 2007. 

3. 10.3390/electronics7050076 M. Kim, “A miniaturized electromagnetic bandgap structure using an inductance-enhanced patch for suppression of parallel plate modes in packages and PCBs,” Electronics, vol. 7, no. 5, p. 76, 2018. 10.3390/electronics7050076 

   상세보기

4. 10.1109/TMTT.2003.812555 R. Abhari, G. V. Eleftheriades, “Metallo-dielectric electromagnetic bandgap structures for suppression and isolation of the parallel-plate noise in high-speed circuits,” IEEE Transactions on Microwave Theory and Techniques, vol. 51, no. 6, pp. 1629-1639, Jun. 2003. 10.1109/TMTT.2003.812555 

   상세보기

5. 10.1109/TMTT.2008.2003525 T. Kamgaing, O. M. Ramahi, “Multiband electromagnetic-bandgap structures for applications in small form-factor multichip module packages,” IEEE Transactions on Microwave Theory and Techniques, vol. 56, no. 10, pp. 2293-2300, Oct. 2008. 10.1109/TMTT.2008.2003525 

   상세보기

6. H. H. Park, D. H. Lee, “Machine learning based EMI/EMC design technology trend,” The Proceedings of The Korean Institute of Electromagnetic Engineering and Science, vol. 31, no. 1, pp. 41-51, Jan. 2020. 

7. A. Geron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Sebastopol, CA, O'Reilly Media, 2019. 

8. S. Raschka, V. Mirjalili, Python Machine Learning, Birmingham, Packt, 2017. 

9. 10.1109/CVPR.2015.7298594 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, and D. Anguelov, et al., “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Boston, MA, Jun. 2015, pp. 1-9. 

   

10. 10.1109/CVPR.2016.90 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, Jun. 2016, pp. 770-778. 

    

11. 10.1109/CVPR.2017.243 G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, Jul. 2017, pp. 4700-4708.