Application of stochastic and artificial intelligence methods for Nuclear Material Identification

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@TechReport{Pozzi:1999:ORNL,
  title =        "Application of stochastic and artificial intelligence
                 methods for Nuclear Material Identification",
  author =       "Sara Pozzi and F. J. Segovia",
  institution =  "Oak Ridge National Laboratory",
  year =         "1999",
  number =       "ORNL/TM-1999/320",
  address =      "Oak Ridge, Tennessee 37831, USA",
  month =        dec,
  notes =        "oai:CiteSeerX.psu:10.1.1.483.2883 slightly different",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.ornl.gov/reports/1999/3445605368018.pdf",
  size =         "49 pages",
  abstract =     "Nuclear materials safeguard efforts necessitate the
                 use of non-destructive methods to determine the
                 attributes of fissile samples enclosed in special,
                 non-accessible containers. The sample identification
                 problem, in its most general setting, is then to
                 determine the relationship between the observed
                 features of the measurement and the sample attributes
                 and to combine them for the construction of an optimal
                 identification algorithm. The goal of this paper is to
                 develop an artificial intelligence (AI) approach to
                 this problem whereby neural networks (NN) and genetic
                 programming (GP) algorithms are used for sample
                 identification purposes. Monte Carlo simulations of the
                 source-detector cross correlation function for various
                 sample shapes, mass, and enrichment values of uranium
                 metal have been performed to serve as training set for
                 the artificial intelligence algorithms. Both the NN and
                 GP algorithms have shown good capabilities and
                 robustness for mass and enrichment predictions of
                 uranium metal samples. These results serve as a proof
                 of principle for the application of combined stochastic
                 and AI methods to safeguards procedures.",
  notes =        "U.S. DEPARTMENT OF ENERGY contract DE-AC05-960R22464",
  size =         "49 pages",
}

Genetic Programming entries for Sara A Pozzi Javier Segovia

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