Rapid radionuclide identification algorithm based on the discrete cosine transform and BP neural network
Rapid radionuclide identification algorithm based on the discrete cosine transform and BP neural network
复制标题
基于离散余弦变换和BP神经网络的放射性核素快速识别算法
DOI：
10.1016/j.anucene.2017.09.032
复制
发表时间：
2018-02-01
影响因子：
1.9
通讯作者：
Gao, Le
中科院分区：
文献类型：
--
作者：
He, Jianping;Tang, Xiaobin;Gao, Le
传统的基于峰值检测的放射性核素识别算法无法在短时间内识别放射性物质。本研究提出一种基于离散余弦变换和误差反向传播神经网络的放射性核素快速识别算法。使用检测率和准确的放射性核素识别距离来评价所提出的方法。实验结果表明，提取的频谱特征向量不受时间、活动、距离的影响。该算法在相对真实的环境中获得了较好的结果，并且具有预测混合谱同位素组成的能力。除相关放射性核素发射的伽马射线被明显屏蔽外，该方法对被屏蔽材料屏蔽的放射性核素能谱具有较好的识别性能。还特别推荐用于光谱辐射入口监测器、放射性同位素识别装置和其他辐射监测仪器的快速放射性核素识别。 (C) 2017 Elsevier Ltd. 保留所有权利。
Traditional radionuclide identification algorithm based on peak detection cannot recognize radioactive material in a short time. This study proposes a rapid radionuclide identification algorithm-based on the discrete cosine transform and error back propagation neural network. Detection rate and accurate radionuclide identification distance were used to evaluate the proposed method. Experimental results show that the extracted feature vector of the spectrum is not influenced by time, activity, and distance. The proposed algorithm obtained better results in a relatively authentic environment, and it has the ability to predict the isotopic compositions of the mixed spectrum. The proposed method has a better identification performance for the spectrum of radionuclide masked by shielding material except the gamma rays emitted by related radionuclide are significantly shielded. It is also particularly recommended for the fast radionuclide identification of spectroscopic radiation portal monitors, radioisotope identification devices, and other radiation monitoring instruments. (C) 2017 Elsevier Ltd. All rights reserved.