Affiliation:
1. University of Utah, USA & Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA
2. Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, USA
Abstract
The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this chapter, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil-2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures.
Cited by
1 articles.
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1. Recent Developments of Fireworks Algorithms;Handbook of Research on Fireworks Algorithms and Swarm Intelligence;2020