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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Advances in automatic extraction of information from multispectral scanner data

The state-of-the-art of automatic multispectral scanner data analysis and interpretation is reviewed. Sources of system variability which tend to obscure the spectral characteristics of the classes under consideration are discussed, and examples of the application of spatial and temporal discrimination bases are given. Automatic processing functions, techniques and methods, and equipment are described with particular attention to those that are applicable to large land surveys using satellite data. The development and characteristics of the Multivariate Interactive Digital Analysis System (MIDAS) for processing aircraft or satellite multispectral scanning data are discussed in detail. The MIDAS system combines the parallel digital implementation capabilities of a low-cost processor with a general purpose PDP-11/45 minicomputer to provide near-real-time data processing. The preprocessing functions are user-selectable. The input subsystem accepts data stored on high density digital tape, computer compatible tape, and analog tape.

Erickson, J. D.↗
NASA NTRS1997

Analysis of Forest Foliage Using a Multivariate Mixture Model

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Hlavka, C. A.↗
NASA NTRS2005

Self-Organizing-Map Program for Analyzing Multivariate Data

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

Li, P. Peggy↗
NASA NTRS1979

A comparison of limited-area energetic processes between observations and primitive equation model predictions

Energetic analyses of the NMC initial conditions and NMC six-layer primitive equation operational prediction model 12-hr forecast for a developing cyclone are presented. Consideration is given to the total kinetic energy, the energetics of the divergent and nondivergent flows and the baroclinic (vertical shear flow) and barotropic (vertical mean flow) components of the kinetic energy. It is found that the model initial conditions lose 10-15% of the kinetic energy at various levels compared to a limited-area multivariate statistical analysis of the observational data, leading to a decrease in the horizontal kinetic energy flux, a misrepresentation of the synoptic scale wave system in the 12-hr forecast. Similar results are obtained for the nondivergent flow, while the divergent flow energetics are not reproduced accurately by the model. The horizontal flux terms of the vertical mean and vertical shear energetics are also not found to be reproduced in the upper levels, although horizontal flux contributions to the baroclinic component are improved at middle and lower levels. Finally, vertical shear kinetic energy generation is found to be well represented in the model prediction, however kinetic energy conversion between vertical shear and mean flow is not reproduced in the lower layer.

Alpert, J. C.↗
NASA NTRS1991

The manifold of globular clusters

A multivariate statistical analysis of a large data set on the Galactic globular clusters indicates that at least 4 (possibly 5 or 6) parameters are needed in order to describe fully the global properties of these objects. Some nontrivial correlations are present in the data, and are described in the text. One of them is the scaling relation between the luminosity and the central velocity dispersion, whose possible physical origin is briefly discussed. Many of the systematic trends found in the data suggest that dynamical effects played an important or even a dominant role in determining the present-day global properties of globular clusters.

Djorgovski, S.↗
NASA NTRS1976

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Source record↗
NASA NTRS1982

The role of multispectral scanners as data sources for EPA hydrologic models

An estimated cost savings of 30% to 50% was realized from using LANDSAT-derived data as input into a program which simulates hydrologic and water quality processes in natural and man-made water systems. Data from the satellite were used in conjunction with EPA's 11-channel multispectral scanner to obtain maps for characterizing the distribution of turbidity plumes in Flathead Lake and to predict the effect of increasing urbanization in Montana's Flathead River Basin on the lake's trophic state. Multispectral data are also being studied as a possible source of the parameters needed to model the buffering capability of lakes in an effort to evaluate the effect of acid rain in the Adirondacks. Water quality in Lake Champlain, Vermont is being classified using data from the LANDSAT and the EPA MSS. Both contact-sensed and MSS data are being used with multivariate statistical analysis to classify the trophic status of 145 lakes in Illinois and to identify water sampling sites in Appalachicola Bay where contaminants threaten Florida's shellfish.

Slack, R.↗
NASA NTRS1991

Using Gaussian windows to explore a multivariate data set

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

Jaeckel, Louis A.↗
NASA NTRS1990

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Park, Steve↗
NASA NTRS1982

Accuracy of remotely sensed data: Sampling and analysis procedures

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given.

Congalton, R. G.↗
NASA NTRS2011

Parametric Testing of Launch Vehicle FDDR Models

For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.

Schumann, Johann↗
NASA NTRS1981

Methods for presentation and display of multivariate data

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

Myers, R. H.↗
NASA NTRS1993

An observing system simulation experiment for the Laser Atmospheric Wind Sounder (LAWS)

The present observing system-simulation experiments evaluate the potential of a Laser Atmospheric Wind Sounder (LAWS) instrument for 5-day forecasting, using a primitive-equation multilevel spectral global circulation model. A 55-deg-inclined and a 98-deg sun-synchronous orbit are examined, by adding LAWS wind profiles into a global 4D data-assimilation system, and comparing both the analyses and forecasts to a control experiment. The 4D data-assimilation system consists of a multivariate optimum interpolation analysis and a nonlinear, normal-mode intialization, using the aforementioned global circulation model.

Rohaly, G. D.↗
NASA NTRS1983

Conic sectors for sampled-data feedback systems

The conic-sector analysis of the closed-loop stability and robustness of a multivariable-analog-system controller based on sampled-data feedback compensation is investigated. Conic sectors and sampled-data feedback systems are defined, and the existence of a conic sector containing a sampled-data operator is established mathematically. An example is presented to prove that the conic sector is computable and gives sufficient conditions of closed-loop stability. A procedure for determining sampled-data-operator gain is also derived.

Thompson, P. M.↗
NASA NTRS1995

Chemical studies of H chondrites. 6: Antarctic/non-Antarctic compositional differences revisited

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, T1, and In (ordered by putative volatility during nebular condensation and accretion) determined by radiochemical neutron activation analysis of 14 additional H5 and H6 chondrite falls. Data for the 10 most volatile elements (Rb to In) treated by the multivariate techniques of linear discriminant analysis and logistic regression in these and 44 other falls are compared with those of 59 H4-6 chondrites from Antarctica. Various populations are tested by the multivariate techniques, using the previously developed method of randomization-simulation to assess significance levels. An earlier conclusion, based on fewer examples, that H4-6 chondrite falls are compositionally distinguishable from the Antarctic suite is verified by the additional data. This distinctiveness is highly significant because of the presence of samples from Victoria Land in the Antarctic population, which differ compositionally from falls beyond any reasonable doubt. However, it cannot be proven unequivocally that falls and Antarctic samples from Queen Maud Land are compositionally distinguishable. Trivial causes (e.g., analyst bias, weathering) cannot explain the Victoria Land (Antarctic)/non-Antarctic compositional difference for paradigmatic H4-6 chondrites. This seems to reflect a time-dependent variation of near-Earth meteoroid source regions differing in average thermal history.

Wolf, Stephen F.↗
NASA NTRS1982

Identification of multivariable high performance turbofan engine dynamics from closed loop data

The multivariable instrumental variable/approximate maximum likelihood (IV/AML) method or recursive time-series analysis is used to identify the multivariable (four inputs-three outputs) dynamics of the Pratt and Whitney F100 engine. A detailed nonlinear engine simulation is used to determine linear engine model structures and parameters at an operating point using open loop data. Also, the IV/AML method is used in a direct identification mode to identify models from actual closed loop engine test data. Models identified from simulated and test data are compared to determine a final model structure and parameterization that can predict engine response for a wide class of inputs. The ability of the IV/AML algorithm to identify useful dynamic models from engine test data is assessed.

Merrill, W.↗
NASA NTRS1983

Identification of multivariable high performance turbofan engine dynamics from closed loop data

The multivariable instrumental variable/approximate maximum likelihood (IV/AML) method of recursive time-series analysis is used to identify the multivariable (four inputs-three outputs) dynamics of the Pratt and Whitney F100 engine. A detailed nonlinear engine simulation is used to determine linear engine model structures and parameters at an operating point using open loop data. Also, the IV/AML method is used in a direct identification made to identify models from actual closed loop engine test data. Models identified from simulated and test data are compared to determine a final model structure and parameterization that can predict engine response for a wide class of inputs. The ability of the IV/AML algorithm to identify useful dynamic models from engine test data is assessed. Previously announced in STAR as N82-20339

Merrill, W.↗
NASA NTRS2003

A Step Beyond Simple Keyword Searches: Services Enabled by a Full Content Digital Journal Archive

The problems of managing and searching large archives of scientific journal articles can potentially be addressed through data mining and statistical techniques matured primarily for quantitative scientific data analysis. A journal paper could be represented by a multivariate descriptor, e.g., the occurrence counts of a number key technical terms or phrases (keywords), perhaps derived from a controlled vocabulary ( e . g . , the American Meteorological Society's Glossary of Meteorology) or bootstrapped from the journal archive itself. With this technique, conventional statistical classification tools can be leveraged to address challenges faced by both scientists and professional societies in knowledge management. For example, cluster analyses can be used to find bundles of "most-related" papers, and address the issue of journal bifurcation (when is a new journal necessary, and what topics should it encompass). Similarly, neural networks can be trained to predict the optimal journal (within a society's collection) in which a newly submitted paper should be published. Comparable techniques could enable very powerful end-user tools for journal searches, all premised on the view of a paper as a data point in a multidimensional descriptor space, e.g.: "find papers most similar to the one I am reading", "build a personalized subscription service, based on the content of the papers I am interested in, rather than preselected keywords", "find suitable reviewers, based on the content of their own published works", etc. Such services may represent the next "quantum leap" beyond the rudimentary search interfaces currently provided to end-users, as well as a compelling value-added component needed to bridge the print-to-digital-medium gap, and help stabilize professional societies' revenue stream during the print-to-digital transition.

Boccippio, Dennis J.↗