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NTIS 바로가기Nuclear engineering and technology : an international journal of the Korean Nuclear Society, v.53 no.3, 2021년, pp.888 - 893
Choi, Se Hwan (Department of Information and Statistics, Yonsei University) , Choi, Hyun Joon (Department of Radiation Convergence Engineering, Yonsei University) , Min, Chul Hee (Department of Radiation Convergence Engineering, Yonsei University) , Chung, Young Hyun (Department of Radiation Convergence Engineering, Yonsei University) , Ahn, Jae Joon (Department of Information and Statistics, Yonsei University)
The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-...
S. Techniques, Equipment 2003 Edition, International Nuclear Verification Series No. 1 (Revised), IAEA, Vienna, 2003.
M. Zendel, IAEA safeguards equipment, Int. J. Nucl. Energy Sci. Technol. 4 (1) (2008) 72.
T. Honkamaa, F. Levai, A. Turunen, R. Berndt, S. Vaccaro, P. Schwalbach, A Prototype for passive gamma emission tomography, in: IAEA Symposium on International Safeguards: Linking Strategy, Implementation and People, Vienna, 2014.
S. Holcombe, S.J. Svard, L. Hallstadius, A Novel gamma emission tomography instrument for enhanced fuel characterization capabilities within the OECD Halden Reactor Project, Ann. Nucl. Energy 85 (2015) 837-845.
E.L. Smith, S. Jacobsson, V. Mozin, P. Jansson, E. Miller, T. Honkamaa, et al., Viability Study of Gamma Emission Tomography for Spent Fuel Verification: JNT 1955 Phase I Technical Report, 2016.
E.A. Miller, L.E. Smith, R.S. Wittman, et al., Hybrid Gama Emission Tomography (HGET): FY16 Annual Report NO. PNNL-26213, Pacific Northwest National Lab.(PNNL), Richland, WA United States, 2017.
T.D. Gedeon, D. Harris, Progressive image compression, in: IJCNN International Joint Conference on Neural Networks, 4, 1992.
Y.A. Zhang, Better autoencoder for image: convolutional autoencoder ICONIP17-DCEC, Available from, http://users.cecs.anu.edu.au/Tom.Gedeon/conf/ABCs2018/paper/ABCs2018_paper_58.pdf, 2018.
V.A. Knyaz, O. Vygolov, V.V. Kniaz, Y. Vizilter, V. Gorbatsevich, T. Luhmann, N. Conen, Deep learning of convolutional auto-encoder for image matching and 3d object reconstruction in the infrared range, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017.
L. Gondara, Medical image denoising using convolutional denoising autoencoders, in: 2016 IEEE 16th International Conference on Data Mining Workshops, 2016.
H.J. Choi, I.S. Kang, K.B. Kim, Y.H. Chung, C.H. Min, Optimization of singlephoton emission computed tomography system for fast verification of spent fuel assembly: a Monte Carlo study, J. Instrum. 14 (7) (2019) T07002.
L. Rao, B. Zhang, J. Zhao, Hardware implementation of reconfigurable 1-D convolution, Journal of Signal Processing Systems 82 (1) (2016) 1-16.
J. Lemley, S. Bazrafkan, P. Corcoran, Deep Learning for Consumer Devices and Services: pushing the limits for machine learning, artificial intelligence, and computer vision, IEEE Consumer Electronics Magazine 6 (2) (2017) 48-56.
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 2012.
W. Kehl, W. Milletari, F. Tombari, S. Ilic, N. Navab, Deep learning of local RGBD patches for 3D object detection and 6D pose estimation, in: European Conference on Computer Vision, Springer, Cham, 2016.
O.E. David, N.S. Netanyahu, Deeppainter: painter classification using deep convolutional autoencoders, in: International Conference on Artificial Neural Networks, Springer, Cham, 2016.
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