分类： 物理学 >> 核物理学 提交时间： 2023-10-15
摘要： In -ray imaging, localization of the -ray interaction in the scintillator is critical. Convolutional neural network (CNN) techniques are highly promising for improving -ray localization. Our study evaluated the generalization capabilities of a CNN localization model with respect to the -ray energy and thickness of the crystal. The model maintained a high positional linearity (PL) and spatial resolution (SR) for ray energies between 591460 keV. The PL at the incident surface of the detector was 0.99, and the resolution of the central incident point source ranged between 0.521.19 mm. In modified uniform redundant array (MURA) imaging systems using a thick crystal, the CNN -ray localization model significantly improved the useful field-of-view (UFOV) from 60.32% to 93.44% compared to the classical centroid localization methods. Additionally, the signal-to-noise ratio (SNR) of the reconstructed images increased from 0.95 to 5.63.
分类： 核科学技术 >> 粒子加速器 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
摘要： In this paper, a genetic-algorithm-based artificial neural network (GAANN) model radioactivity prediction is proposed, which is verified by measuring results from Long Range Alpha Detector (LRAD). GAANN can integrate capabilities of approximation of Artificial Neural Networks (ANN) and of global optimization of Genetic Algorithms (GA) so that the hybrid model can enhance capability of generalization and prediction accuracy, theoretically. With this model, both the number of hidden nodes and connection weights matrix in ANN are optimized using genetic operation. The real data sets are applied to the introduced method and the results are discussed and compared with the traditional Back Propagation (BP) neural network, showing the feasibility and validity of the proposed approach.
分类： 核科学技术 >> 辐射物理与技术 提交时间： 2025-02-06
摘要： 中子飞行时间 （ToF） 测量是一种通过测量中子速度来获取 A 中子动能的高精度方法，但需要精确获取中子信号的到达时间。然而，与获取中子 ToF 信号相关的高硬件成本和数据负担带来了重大挑战。较高的采样率会增加数据量、数据处理和存储硬件成本。压缩采样可以解决这些挑战，但它面临着最佳采样效率和高质量重建信号方面的问题。本文提出了一种革命性的基于深度学习的压缩采样 （DL-CS） 算法，用于重建中子 ToF 信号，其性能优于传统的压缩采样方法。这种方法包括四个模块：随机投影、上升维度、初始重建和最终重建。最初，该技术使用三个卷积层自适应地按顺序压缩中子 ToF 信号，取代了传统压缩采样理论中的随机测量矩阵。随后，使用修改后的起始模块、长短期记忆和自我注意重建信号。这种深度压缩采样方法的性能使用百分比均方根差、相关系数和重建时间进行量化。实验结果表明，与其他压缩采样方法相比，我们提出的 DL-CS 方法可以显著提高信号质量。对于使用电子束驱动的光中子源生成的中子 ToF 信号，采样率低于 10% 时获得的均方根百分比差、相关系数和重建时间结果分别为 5%、0.9988 和 0.0108 s，证明了这一点。结果表明，与其他压缩采样方法相比，所提出的 DL-CS 方法显著提高了信号质量，表现出优异的重建精度和速度。
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