<sup>252</sup>Cf-source-driven nuclear material concentration identification based on deep learning

Chen Lelin; Wei Biao; Li Pengcheng; Feng Peng; Zhou Mi

doi:10.11884/HPLPB201830.170487

Volume 30 Issue 9

Sep. 2018

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Chen Lelin, Wei Biao, Li Pengcheng, et al. 252Cf-source-driven nuclear material concentration identification based on deep learning[J]. High Power Laser and Particle Beams, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487

Citation:

Chen Lelin, Wei Biao, Li Pengcheng, et al. ²⁵²Cf-source-driven nuclear material concentration identification based on deep learning[J]. High Power Laser and Particle Beams, 2018, 30: 096001. doi: 10.11884/HPLPB201830.170487

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²⁵²Cf-source-driven nuclear material concentration identification based on deep learning

doi: 10.11884/HPLPB201830.170487

Key Laboratory of Opto-electronics Technology and System of Ministry of Education, College of Opto-Electronics Engineering, Chongqing University, Chongqing 400044, China

Received Date: 2017-12-04
Rev Recd Date: 2018-04-18
Publish Date: 2018-09-15

Abstract

Abstract

For the problem of concentration identification of nuclear material in nuclear weapon/material identification system, we used the Monte Carlo method, established a database of neutron signal obtained by fission of nuclear material driven by ²⁵²Cf-source under the condition of different distance and angle of detectors. Based on the convolutional neural network in deep learning area, a method for ²⁵²Cf-source-driven nuclear material concentration identification was constructed, thereby, the identification of test samples was realized. Then a contrast experiment was conducted with the BP neural network and K-nearest neighbor method. The experimental results show that using the constructed method, a high identification rate of 92.05% is got. The problem of the accuracy of the nuclear material concentration identification was affected by the change of the distance and angle of the detector is solved, and the accuracy of this method is better than that of the BP neural network and K-nearest neighbor methods. This paper provides a new idea for the ²⁵²Cf-source-driven nuclear material concentration identification.
- nuclear weapon/material,
- fission neutron signal database,
- deep learning,
- convolutional neural network,
- concentration identification

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References(13)

References

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