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A novel radioactive particle tracking algorithm based on deep rectifier neural network
Nuclear Engineering and Technology ( IF 2.7 ) Pub Date : 2021-01-08 , DOI: 10.1016/j.net.2021.01.002
Roos Sophia de Freitas Dam , Marcelo Carvalho dos Santos , Filipe Santana Moreira do Desterro , William Luna Salgado , Roberto Schirru , César Marques Salgado

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a137Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.



中文翻译:

一种基于深度整流神经网络的放射性粒子跟踪新算法

放射性粒子追踪 (RPT) 是一种微创核技术,它通过数学定位算法追踪感兴趣体积内的放射性粒子。在过去的几十年中,已经开发了许多算法,包括基于人工智能技术的算法。在这项研究中,RPT 技术应用于模拟测试部分,该部分采用填充混凝土的简化搅拌机、六个闪烁体探测器和一个137Cs 放射性粒子发射 662 keV 的伽马射线。测试部分使用MCNPX代码开发,MCNPX代码是基于蒙特卡罗模拟的数学代码,模拟了3516个不同的放射性粒子位置(x,y,z)。本文的新颖之处在于使用基于深度学习模型的定位算法,更具体地说是 6 层深度整流神经网络 (DRNN),其中使用贝叶斯优化方法定义超参数。DRNN 是一种深度前馈神经网络,它替代了传统的基于 sigmoid 的激活函数,传统上用于香草多层感知器网络,用于校正激活函数。结果表明 DRNN 在 RPT 跟踪系统中具有很高的准确性。放射性粒子的 x、y 和坐标的均方根误差分别为 0.03064、0。

更新日期:2021-01-08
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