<sup>252</sup>Cf-source-driven nuclear material concentration identification based on compressive sensing

Li Pengcheng; Wei Biao; Feng Peng; He Peng; Zhou Mi; Mi Deling; Ren Yong

doi:10.11884/HPLPB201527.074004

Volume 27 Issue 07

Jun. 2015

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Article Navigation > High Power Laser and Particle Beams > 2015 > 27(07): 074004

Wang Ke, Duan Yantao, Shi Lihua, et al. A pulsed magnetic field sensor based on dual-loop differential structure[J]. High Power Laser and Particle Beams, 2022, 34: 043003. doi: 10.11884/HPLPB202234.210337

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Li Pengcheng, Wei Biao, Feng Peng, et al. ²⁵²Cf-source-driven nuclear material concentration identification based on compressive sensing[J]. High Power Laser and Particle Beams, 2015, 27: 074004. doi: 10.11884/HPLPB201527.074004

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²⁵²Cf-source-driven nuclear material concentration identification based on compressive sensing

doi: 10.11884/HPLPB201527.074004

1.
Key Laboratory of Opto-electronics Technology and System,Ministry of Education,Chongqing University,Chongqing 400044,China;
2.
College of Communication Engineering,Chongqing University,Chongqing 400044,China

Received Date: 2015-02-03
Rev Recd Date: 2015-04-23
Publish Date: 2015-06-23

Abstract

Abstract

For solving the identification problem in 252Cf-source-driven noise analysis method, we used the compressive sensing theory and the nearest neighbor recognition algorithm, proposed a new classification method named CSKNN method, and then analysed identification probability. The experimental results show that for the classification and identification purposes, the CSKNN identification method only needs a few observations (the ratio between the number of measured values and the fission neutron signal length is no less than 0.1). When the signal to noise ratio increases, the recognition probability will converge faster to 100% and be less sensitive to K. Hence, the CSKNN method is reasonable and feasible, not only because it improves the real-time performance of nuclear arms control verification, but also effectively reduces the sampling cost. Most importantly, it provides a new theoretical basis and implementation method for the online classification of nuclear material concentration.
- 252Cf-source-driven noise analysis,
- compressive sampling,
- observation rate,
- K-nearest neighbor recognition algorithm,
- classification probability

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