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Title: ShiftNMFk 1.2

Abstract

The ShiftNMFk1.2 code, or as we call it, GreenNMFk, represents a hybrid algorithm combining unsupervised adaptive machine learning and Green's function inverse method. GreenNMFk allows an efficient and high performance de-mixing and feature extraction of a multitude of nonnegative signals that change their shape propagating through the medium. The signals are mixed and recorded by a network of uncorrelated sensors. The code couples Non-negative Matrix Factorization (NMF) and inverse-analysis Green's functions method. GreenNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of the governing partial differential equation to identify the unknown sources and their charecteristics. GreenNMF can be applied directly to any problem controlled by a known partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations. Full GreenNMFk method is a subject LANL U.S. Patent application S133364.000 August, 2017. The ShiftNMFk 1.2 version here is a toy version of this method that can work with a limited number of unknown sources (4 or less).

Authors:
 [1];  [1];  [1];  [1]
  1. LANL
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Los Alamos National Laboratory (LANL)
OSTI Identifier:
1410443
Report Number(s):
GreenNMFk; 005539MLTPL00
C17017
DOE Contract Number:
AC52-06NA25396
Resource Type:
Software
Software Revision:
00
Software Package Number:
005539
Software CPU:
MLTPL
Open Source:
Yes
Open Source under the BSD license.
Source Code Available:
No
Country of Publication:
United States

Citation Formats

Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, and Iliev, Filip. ShiftNMFk 1.2. Computer software. https://www.osti.gov//servlets/purl/1410443. Vers. 00. USDOE. 19 Oct. 2017. Web.
Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, & Iliev, Filip. (2017, October 19). ShiftNMFk 1.2 (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1410443.
Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, and Iliev, Filip. ShiftNMFk 1.2. Computer software. Version 00. October 19, 2017. https://www.osti.gov//servlets/purl/1410443.
@misc{osti_1410443,
title = {ShiftNMFk 1.2, Version 00},
author = {Alexandrov, Boian S. and Vesselinov, Velimir V. and Stanev, Valentin and Iliev, Filip},
abstractNote = {The ShiftNMFk1.2 code, or as we call it, GreenNMFk, represents a hybrid algorithm combining unsupervised adaptive machine learning and Green's function inverse method. GreenNMFk allows an efficient and high performance de-mixing and feature extraction of a multitude of nonnegative signals that change their shape propagating through the medium. The signals are mixed and recorded by a network of uncorrelated sensors. The code couples Non-negative Matrix Factorization (NMF) and inverse-analysis Green's functions method. GreenNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of the governing partial differential equation to identify the unknown sources and their charecteristics. GreenNMF can be applied directly to any problem controlled by a known partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations. Full GreenNMFk method is a subject LANL U.S. Patent application S133364.000 August, 2017. The ShiftNMFk 1.2 version here is a toy version of this method that can work with a limited number of unknown sources (4 or less).},
url = {https://www.osti.gov//servlets/purl/1410443},
doi = {},
year = {Thu Oct 19 00:00:00 EDT 2017},
month = {Thu Oct 19 00:00:00 EDT 2017},
note =
}

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