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Title: Assessment of Gamma-Ray-Spectra 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, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, 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. 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:
 [1] ;  [2]
  1. Univ. of Utah, Salt Lake City, UT (United States); Purdue Univ., West Lafayette, IN (United States). Applied Intelligent Systems Lab. School of Nuclear Engineering
  2. 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 (NA-20)
Country of Publication:
United States
Language:
English
Subject:
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; 97 MATHEMATICS AND COMPUTING; fireworks algorithm; spectrum fitting; gamma-ray analysis; nuclear nonproliferation; error measures
OSTI Identifier:
1440899

Alamaniotis, Miltiadis, and Tsoukalas, Lefteri H. Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures. United States: N. p., Web. doi:10.4018/978-1-5225-5134-8.ch007.
Alamaniotis, Miltiadis, & Tsoukalas, Lefteri H. Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures. United States. doi:10.4018/978-1-5225-5134-8.ch007.
Alamaniotis, Miltiadis, and Tsoukalas, Lefteri H. 2018. "Assessment of Gamma-Ray-Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures". United States. doi:10.4018/978-1-5225-5134-8.ch007.
@article{osti_1440899,
title = {Assessment of Gamma-Ray-Spectra 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, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. In this paper, 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. 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 = {10.4018/978-1-5225-5134-8.ch007},
journal = {Critical Developments and Applications of Swarm Intelligence},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}