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http://dx.doi.org/10.1016/j.net.2020.08.020

Development of de-noised image reconstruction technique using Convolutional AutoEncoder for fast monitoring of fuel assemblies  

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)
Publication Information
Nuclear Engineering and Technology / v.53, no.3, 2021 , pp. 888-893 More about this Journal
Abstract
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-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.
Keywords
Tomographic imaging; Verification of fuel assemblies; Deep learning-based denoising process; Convolutional autoencoder;
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  • Reference
1 A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 2012.
2 M. Zendel, IAEA safeguards equipment, Int. J. Nucl. Energy Sci. Technol. 4 (1) (2008) 72.   DOI
3 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.   DOI
4 L. Rao, B. Zhang, J. Zhao, Hardware implementation of reconfigurable 1-D convolution, Journal of Signal Processing Systems 82 (1) (2016) 1-16.   DOI
5 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.
6 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.
7 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.
8 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.
9 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.
10 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.
11 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.   DOI
12 S. Techniques, Equipment 2003 Edition, International Nuclear Verification Series No. 1 (Revised), IAEA, Vienna, 2003.
13 T.D. Gedeon, D. Harris, Progressive image compression, in: IJCNN International Joint Conference on Neural Networks, 4, 1992.
14 L. Gondara, Medical image denoising using convolutional denoising autoencoders, in: 2016 IEEE 16th International Conference on Data Mining Workshops, 2016.
15 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.   DOI
16 O.E. David, N.S. Netanyahu, Deeppainter: painter classification using deep convolutional autoencoders, in: International Conference on Artificial Neural Networks, Springer, Cham, 2016.