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).
- Developers:
- Release Date:
- 2017-10-19
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Matlab
- Licenses:
-
Other (Commercial or Open-Source): https://github.com/rNMF/HNMF/blob/master/License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC52-06NA25396
- Code ID:
- 45528
- Site Accession Number:
- C17017; 7757
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, and Iliev, Filip.
ShiftNMFk 1.2.
Computer Software.
https://github.com/rNMF/HNMF.
USDOE.
19 Oct. 2017.
Web.
doi:10.11578/dc.20201001.45.
Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, & Iliev, Filip.
(2017, October 19).
ShiftNMFk 1.2.
[Computer software].
https://github.com/rNMF/HNMF.
https://doi.org/10.11578/dc.20201001.45.
Alexandrov, Boian S., Vesselinov, Velimir V., Stanev, Valentin, and Iliev, Filip.
"ShiftNMFk 1.2." Computer software.
October 19, 2017.
https://github.com/rNMF/HNMF.
https://doi.org/10.11578/dc.20201001.45.
@misc{
doecode_45528,
title = {ShiftNMFk 1.2},
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).},
doi = {10.11578/dc.20201001.45},
url = {https://doi.org/10.11578/dc.20201001.45},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20201001.45}},
year = {2017},
month = {oct}
}