Open AccessDesign of Radionuclides Identification Algorithm Based on Sequence Bayesian Method
Author(s) -
Zeqian Wu,
Bairong Wang,
Jiwei Sun
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/5/052047
Subject(s) - identification (biology) , algorithm , energy (signal processing) , radionuclide , detector , process (computing) , computer science , constant false alarm rate , bayesian probability , false alarm , sequence (biology) , set (abstract data type) , selection (genetic algorithm) , artificial intelligence , mathematics , statistics , physics , chemistry , nuclear physics , telecommunications , biochemistry , botany , biology , programming language , operating system
An efficient method of radionuclides identification is essential for the work in the nuclear-related area. In this paper, sequential Bayesian method is embedded into a new radionuclides identification algorithm. Through the process of energy screening, parameter estimation, decision-making function updating and judging, the types of radionuclides can be identified quickly and accurately. This algorithm is tested by using the measured energy spectrum detected by LaBr 3 (Ce) detector, and the results are composed of the following four parts for the case where the widths of region of interest are 4σ and 6σ respectively: the selection experiment of two pre-set parameters of the algorithm; false alarm rate and missed alarm rate of radionuclides identification; the effect of energy drift on the identification accuracy; the average number of total collected particles and valid particles needed for identification process.