Synergism of Fuzzy Numbers and Data Smoothing for Abrupt Change Detection in Gamma-Ray Measurements

Precise identification of activities associated with the use of nuclear materials - illicit or legal - is the main mission of nuclear security. Data analytics significantly contributes to that cause by recognizing patterns of interest and making inferences. In this paper a new algorithm is introduced for abrupt change detection in gamma ray measurements. Such changes may designate activities pertaining to nuclear material, and thus, their fast and accurate detection of paramount significance. The proposed method is based on the synergism of fuzzy number and data smoothing to label each datapoint in time series measurements as containing a potential threat or merely noise. The method was tested using five time series of real-world gamma measurements with infused peaks of varying magnitude. On average the method was able to identify all the infused peaks when they had a mean magnitude of 1.28 counts or greater.