分类： 核科学技术 >> 核探测技术与核电子学 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： As photoelectrically detected 252Cf-source-driven neutron signals always contain noise, a denoising algorithm is proposed based on compressive sensing for the noised neutron signal. In the algorithm, Empirical Mode Decomposition (EMD) is applied to decompose the noised neutron signal and then find out the noised Intrinsic Mode Function (IMF) automatically. Thus, we only need to use the basis pursuit denoising (BPDN) algorithm to denoise these IMFs. For this reason, the proposed algorithm can be called EMDCSDN (Empirical Mode Decomposition Compressive Sensing Denoising). In addition, five indicators are employed to evaluate the denoising effect. The results show that the EMDCSDN algorithm is more effective than the other denoising algorithms including BPDN. This study provides a new approach for signal denoising at the front-end.
分类： 核科学技术 >> 核探测技术与核电子学 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing (CS) and Otsus method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Split-bregman method based on L1/2 regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsus method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method, and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.
分类： 核科学技术 >> 核探测技术与核电子学 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： The 252Cf source-driven verification system (SDVS) can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in 252Cf source-driven noise-analysis (SDNA) measurements. We propose a parallel and optimized genetic Elman network (POGEN) to identify the enrichment of 235U 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.
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