1.Key Laboratory of Opto-electronics Technology & System, Ministry of Education, Chongqing University, Chongqing 400044, China
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Peng FENG, Biao WEI, Jing JIN. Parallel and optimized genetic Elman network for 252Cf source-driven verification system. [J]. Nuclear Science and Techniques 26(4):040404(2015)
Peng FENG, Biao WEI, Jing JIN. Parallel and optimized genetic Elman network for 252Cf source-driven verification system. [J]. Nuclear Science and Techniques 26(4):040404(2015) DOI： 10.13538/j.1001-8042/nst.26.040404.
The ,252,Cf source-driven verification system (SDVS) can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in ,252,Cf source-driven noise-analysis (SDNA) measurements. We propose a parallel and optimized genetic Elman network (POGEN) to identify the enrichment of ,235,U based on the physical properties of the measured autocorrelation functions. Theoretical analysis and experimental results indicate that, for 4 different enrichment fissile materials, due to higher information utilization, more efficient network architecture, and optimized parameters, the POGEN-based algorithm can obtain identification results with higher recognition accuracy, compared to the integrated autocorrelation function (IAF) method.
Nuclear noise analysisNeutron detectionParallel and optimized genetic Elman networkEnrichment identification
T E Valentine. Review of subcritical source-driven noise analysis measurements. The U.S. department of energy report, ORNL/TM-1999/288, 1999, 1-15. DOI: 10.2172/15041http://doi.org/10.2172/15041
J K T Mattingly, J T Mihalczo, J A Mullens, et al. Physical and mathematical description of nuclear weapons identification system (NWIS) signatures. The U.S. department of energy report, Y/LB-15946-R3, 1997, 1-43. DOI: 10.2172/1878http://doi.org/10.2172/1878
Z L Luo and A R Luo. Physics of experimental reactor. Beijing (China): Atomic Energy Press, 1987. (in Chinese)
C G Liu and J Wu. An introduction of verification technology of nuclear arms control. Beijing (China): National Defense Industry Press, 2007. (in Chinese)
B Wei, F Yang, P Feng, et al. A new NMIS characteristic signature acquisition method based on time-domain fission correlation spectrum. Nucl Power Eng, 2014, 35: 10-13. (in Chinese)
M Zhou, B Wei, D L Mi, et al. Simulation study for high enriched-uranium components with reflector based on 252Cf source-driven noise analysis method. High Power Laser Part Beam, 2014, 26: 050101. DOI: 10.11884/HPLPB201426.050101http://doi.org/10.11884/HPLPB201426.050101
P Feng, S Y Liu, B Wei, et al. Simulation and experimental study of a random neutron analyzing system with 252Cf neutron source. Nucl Sci Tech, 2011, 22: 39-46.
M Zhou, B Wei, F Yang, et al. Simulation study of neutrons time-correlated coincidence count for uranium components based on 252Cf source-driven noise analysis method. Nucl Tech, 2013, 36: 060202. (in Chinese) DOI: 10.11889/j.0253-3219.2013.hjs.36.060202http://doi.org/10.11889/j.0253-3219.2013.hjs.36.060202
J K Mattingly, T E Valentine and J T Mihalczo. NWIS measurements for uranium metal annular casting. The U.S. department of energy report, No. Y/LB-15,971, Oak Ridge Y-12 Plant, 1998, 1-25.
J K Mattingly, T E Valentine, J T Mihalczo, et al. Enrichment and uranium mass from NMIS for HEU metal. Proceedings of the 41st Instrument for Nuclear Materials Management Annual Meeting, 2000, 51-55.
J T Mihalczo, J A Mullens, J K Mattingly, et al. Physical description of nuclear materials identification system (NMIS) signatures. Nucl Instrum Meth A, 2000, 450: 531-555. DOI: 10.1016/S0168-9002(00)00304-1http://doi.org/10.1016/S0168-9002(00)00304-1
J Jin. Signal processing and recognition of 252Cf neutron source spectrum analysis system for nuclear arms control verification based on photoelectric detection. Ph.D. Thesis, Chongqing University, 2011. (in Chinese)
S A Pozzi and J Segovia. 252Cf source-correlated transmission measurements and genetic programming for nuclear safeguards, Nucl Instrum Meth A, 2002, 491: 326-341. DOI: 10.1016/S0168-9002(02)01120-8http://doi.org/10.1016/S0168-9002(02)01120-8
L X Liu, X B Xia, Y X Sheng, et al. Effects of scattered neutrons on the neutron radiation field generated by Cf-252 neutron source with a shield. Nucl Tech, 2013, 36: 100201. (in Chinese) DOI: 10.11889/j.0253-3219.2013.hjs.36.100201http://doi.org/10.11889/j.0253-3219.2013.hjs.36.100201
D P Xu, Z E Yao, H Y Feng, et al. Preliminary study on biological effects of pea seeds (Pisum sativum L.) induced by 252Cf neutron source. Nucl Tech, 2013, 36: 110207. (in Chinese) DOI: 10.11889/j.0253-3219.2013.hjs.36.110207http://doi.org/10.11889/j.0253-3219.2013.hjs.36.110207
T E Portegys. A maze learning comparison of Elman, long short-term memory, and Mona neural networks. Neural Networks, 2010, 23: 306-313. DOI: 10.1016/j.neunet.2009.11.002http://doi.org/10.1016/j.neunet.2009.11.002
P Ciarlini and U Maniscalco. Wavelets and Elman neural networks for monitoring environmental variables. J Comput Appl Math, 2008, 221: 302-309. DOI: 10.1016/j.cam.2007.10.040http://doi.org/10.1016/j.cam.2007.10.040
J Schmidhuber. Deep learning in neural networks: an overview. Neural Networks, 2015, 61: 85-117. DOI: 10.1016/j.neunet.2014.09.003http://doi.org/10.1016/j.neunet.2014.09.003
D T Pham and D Karaboga. Training Elman and Jordan networks for system identification using genetic algorithms. Artif Intell Eng, 1999, 13: 107-117. DOI: 10.1016/S0954-1810(98)00013-2http://doi.org/10.1016/S0954-1810(98)00013-2
L Araujo, H Zaragoza, J R Pérez-Agüera, et al. Structure of morphologically expanded queries: A genetic algorithm approach. Data Knowl Eng, 2010, 69: 279-289. DOI: 10.1016/j.datak.2009.10.010http://doi.org/10.1016/j.datak.2009.10.010