Assessment of GammaRaySpectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures
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, gammaray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gammaray 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 nonzero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and 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.
 Authors:

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 Univ. of Utah, Salt Lake City, UT (United States); Purdue Univ., West Lafayette, IN (United States). Applied Intelligent Systems Lab. School of Nuclear Engineering
 Purdue Univ., West Lafayette, IN (United States). Applied Intelligent Systems Lab. School of Nuclear Engineering
 Publication Date:
 Grant/Contract Number:
 NA0002576
 Type:
 Accepted Manuscript
 Journal Name:
 Critical Developments and Applications of Swarm Intelligence
 Additional Journal Information:
 Journal Name: Critical Developments and Applications of Swarm Intelligence
 Research Org:
 Purdue Univ., West Lafayette, IN (United States); Univ. of Utah, Salt Lake City, UT (United States)
 Sponsoring Org:
 USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA20)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; 97 MATHEMATICS AND COMPUTING; fireworks algorithm; spectrum fitting; gammaray analysis; nuclear nonproliferation; error measures
 OSTI Identifier:
 1440899
Alamaniotis, Miltiadis, and Tsoukalas, Lefteri H. Assessment of GammaRaySpectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures. United States: N. p.,
Web. doi:10.4018/9781522551348.ch007.
Alamaniotis, Miltiadis, & Tsoukalas, Lefteri H. Assessment of GammaRaySpectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures. United States. doi:10.4018/9781522551348.ch007.
Alamaniotis, Miltiadis, and Tsoukalas, Lefteri H. 2018.
"Assessment of GammaRaySpectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures". United States.
doi:10.4018/9781522551348.ch007.
@article{osti_1440899,
title = {Assessment of GammaRaySpectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures},
author = {Alamaniotis, Miltiadis and Tsoukalas, Lefteri H.},
abstractNote = {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, gammaray spectra is the widest utilized type of data in nonproliferation applications. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gammaray 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 nonzero coefficients express the detected signatures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions—MSE, RMSE, Theil2, MAE, MAPE, MAP—with results exhibiting its potential in providing highly accurate and precise signature detection. Finally and 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.},
doi = {10.4018/9781522551348.ch007},
journal = {Critical Developments and Applications of Swarm Intelligence},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}