Automated method for the systematic interpretation of resonance peaks in spectrum data
Abstract
A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.
 Inventors:

 Knoxville, TN
 Issue Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 OSTI Identifier:
 870914
 Patent Number(s):
 5623579
 Assignee:
 Martin Marietta Energy Systems, Inc. (Oak Ridge, TN)
 Patent Classifications (CPCs):

Y  NEW / CROSS SECTIONAL TECHNOLOGIES Y02  TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE Y02E  REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
Y  NEW / CROSS SECTIONAL TECHNOLOGIES Y10  TECHNICAL SUBJECTS COVERED BY FORMER USPC Y10S  TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 DOE Contract Number:
 AC0584OR21400
 Resource Type:
 Patent
 Country of Publication:
 United States
 Language:
 English
 Subject:
 automated; method; systematic; interpretation; resonance; peaks; spectrum; data; spectral; signature; creation; mathematical; model; process; neural; network; training; set; developed; based; generate; measurable; phenomena; input; parameter; correspond; physical; condition; adjust; internal; parameters; represented; monitored; extracting; features; measured; spectra; determine; specifically; correlates; corresponding; relate; specific; components; consequently; physical condition; automated method; spectral features; neural network; mathematical model; spectrum data; resonance peaks; neural net; resonance peak; spectral signature; process based; /706/376/
Citation Formats
Damiano, Brian, and Wood, Richard T. Automated method for the systematic interpretation of resonance peaks in spectrum data. United States: N. p., 1997.
Web.
Damiano, Brian, & Wood, Richard T. Automated method for the systematic interpretation of resonance peaks in spectrum data. United States.
Damiano, Brian, and Wood, Richard T. Wed .
"Automated method for the systematic interpretation of resonance peaks in spectrum data". United States. https://www.osti.gov/servlets/purl/870914.
@article{osti_870914,
title = {Automated method for the systematic interpretation of resonance peaks in spectrum data},
author = {Damiano, Brian and Wood, Richard T},
abstractNote = {A method for spectral signature interpretation. The method includes the creation of a mathematical model of a system or process. A neural network training set is then developed based upon the mathematical model. The neural network training set is developed by using the mathematical model to generate measurable phenomena of the system or process based upon model input parameter that correspond to the physical condition of the system or process. The neural network training set is then used to adjust internal parameters of a neural network. The physical condition of an actual system or process represented by the mathematical model is then monitored by extracting spectral features from measured spectra of the actual process or system. The spectral features are then input into said neural network to determine the physical condition of the system or process represented by the mathematical. More specifically, the neural network correlates the spectral features (i.e. measurable phenomena) of the actual process or system with the corresponding model input parameters. The model input parameters relate to specific components of the system or process, and, consequently, correspond to the physical condition of the process or system.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1997},
month = {1}
}
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