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Title: NA-22 Particle Classifier Quarterly Report FY18 Q1 [Automated Particle Classifier]

Abstract

This is the first quarter of the third and final year of this project. Much of this quarter was taken up with preparing the comprehensive paper that details the collection of appropriate data, the pre-processing of data, the forms and development of the algorithms, and the scoring of performance. These four processes were still a bit unpolished among the different algorithms, so by synergy the paper raised the quality of performance of each algorithm. For completeness, the group implemented two more kinds of algorithms for performance comparison: Gatti-DeMartini’s optimal linear filter, and a more straightforward (and more effective) implementation of principal component analysis (PCA) than we had previously built.

Authors:
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1424605
Report Number(s):
LLNL-SR-744864
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Wurtz, Ron E. NA-22 Particle Classifier Quarterly Report FY18 Q1 [Automated Particle Classifier]. United States: N. p., 2018. Web. doi:10.2172/1424605.
Wurtz, Ron E. NA-22 Particle Classifier Quarterly Report FY18 Q1 [Automated Particle Classifier]. United States. doi:10.2172/1424605.
Wurtz, Ron E. Tue . "NA-22 Particle Classifier Quarterly Report FY18 Q1 [Automated Particle Classifier]". United States. doi:10.2172/1424605. https://www.osti.gov/servlets/purl/1424605.
@article{osti_1424605,
title = {NA-22 Particle Classifier Quarterly Report FY18 Q1 [Automated Particle Classifier]},
author = {Wurtz, Ron E.},
abstractNote = {This is the first quarter of the third and final year of this project. Much of this quarter was taken up with preparing the comprehensive paper that details the collection of appropriate data, the pre-processing of data, the forms and development of the algorithms, and the scoring of performance. These four processes were still a bit unpolished among the different algorithms, so by synergy the paper raised the quality of performance of each algorithm. For completeness, the group implemented two more kinds of algorithms for performance comparison: Gatti-DeMartini’s optimal linear filter, and a more straightforward (and more effective) implementation of principal component analysis (PCA) than we had previously built.},
doi = {10.2172/1424605},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 16 00:00:00 EST 2018},
month = {Tue Jan 16 00:00:00 EST 2018}
}

Technical Report:

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