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At least 307 records · Page 17

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NASA NTRS1975

Maximum likelihood signature estimation

Maximum-likelihood estimates are discussed which are based on an unlabeled sample of observations, of unknown parameters in a mixture of normal distributions. Several successive approximation procedures for obtaining such maximum-likelihood estimates are described. These procedures, which are theoretically justified by the local contractibility of certain maps, are designed to take advantage of good initial estimates of the unknown parameters. They can be applied to the signature extension problem, in which good initial estimates of the unknown parameters are obtained from segments which are geographically near the segments from which the unlabeled samples are taken. Additional problems to which these methods are applicable include: estimation of proportions and adaptive classification (estimation of mean signatures and covariances).

Walker, H. F.↗

NASA NTRS1988

Space Station power system autonomy demonstration

The Systems Autonomy Demonstration Program (SADP) represents NASA's major effort to demonstrate, through a series of complex ground experiments, the application and benefits of applying advanced automation technologies to the Space Station project. Lewis Research Center (LeRC) and Marshall Space Flight Center (MSFC) will first jointly develop an autonomous power system using existing Space Station testbed facilities at each center. The subsequent 1990 power-thermal demonstration will then involve the cooperative operation of the LeRC/MSFC power system with the Johnson Space Center (JSC's) thermal control and DMS/OMS testbed facilities. The testbeds and expert systems at each of the NASA centers will be interconnected via communication links. The appropriate knowledge-based technology will be developed for each testbed and applied to problems requiring intersystem cooperation. Primary emphasis will be focused on failure detection and classification, system reconfiguration, planning and scheduling of electrical power resources, and integration of knowledge-based and conventional control system software into the design and operation of Space Station testbeds.

Kish, James A.↗

NASA NTRS1998

Smart Optical RAM for Fast Information Management and Analysis

Statement of Problem Instruments for high speed and high capacity in-situ data identification, classification and storage capabilities are needed by NASA for the information management and analysis of extremely large volume of data sets in future space exploration, space habitation and utilization, in addition to the various missions to planet-earth programs. Parameters such as communication delays, limited resources, and inaccessibility of human manipulation require more intelligent, compact, low power, and light weight information management and data storage techniques. New and innovative algorithms and architecture using photonics will enable us to meet these challenges. The technology has applications for other government and public agencies.

Liu, Hua-Kuang↗

NASA NTRS2014

A Classification Table for Achondrites

Classifying chondrites is relatively easy and the criteria are well documented. It is based on mineral compositions, textural characteristics and more recently, magnetic susceptibility. It can be more difficult to classify achondrites, especially those that are very similar to terrestrial igneous rocks, because mineralogical, textural and compositional properties can be quite variable. Achondrites contain essentially olivine, pyroxenes, plagioclases, oxides, sulphides and accessory minerals. Their origin is attributed to differentiated parents bodies: large asteroids (Vesta); planets (Mars); a satellite (the Moon); and numerous asteroids of unknown size. In most cases, achondrites are not eye witnessed falls and some do not have fusion crust. Because of the mineralogical and magnetic susceptibility similarity with terrestrial igneous rocks for some achondrites, it can be difficult for classifiers to confirm their extra-terrestrial origin. We -as classifiers of meteorites- are confronted with this problem with every suspected achondrite we receive for identification. We are developing a "grid" of classification to provide an easier approach for initial classification. We use simple but reproducible criteria based on mineralogical, petrological and geochemical studies. We presented the classes: acapulcoites, lodranites, winonaites and Martian meteorites (shergottite, chassignites, nakhlites). In this work we are completing the classification table by including the groups: angrites, aubrites, brachinites, ureilites, HED (howardites, eucrites, and diogenites), lunar meteorites, pallasites and mesosiderites. Iron meteorites are not presented in this abstract.

Chennaoui-Aoudjehane, H.↗

NASA NTRS1974

Classification, scale series and nomenclature of lunar charts

Lunar charts are classified with an eye toward solution of special problems of lunar cartography. The initial state of mapping the moon will require charts with scales of 1:5,000,000; 1:1,000,000; 1:250,000; 1:50,000; and 1:10,000.

Florenskiy, K. P.↗

NASA NTRS2016

Statistical Emulator for Expensive Classification Simulators

Expensive simulators prevent any kind of meaningful analysis to be performed on the phenomena they model. To get around this problem the concept of using a statistical emulator as a surrogate representation of the simulator was introduced in the 1980's. Presently, simulators have become more and more complex and as a result running a single example on these simulators is very expensive and can take days to weeks or even months. Many new techniques have been introduced, termed criteria, which sequentially select the next best (most informative to the emulator) point that should be run on the simulator. These criteria methods allow for the creation of an emulator with only a small number of simulator runs. We follow and extend this framework to expensive classification simulators.

Ross, Jerret↗

DOE OSTI2020

Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs

In this work, we consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification step in each iteration to selectively generate cuts that are more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i) sampling cuts and collecting information from training problems and (ii) solving testing problems with a support vector machine (SVM) cut classifier. We run the LearnBD algorithm on instances of capacitated facility location and multicommodity network design under uncertain demand. Our results show that SVM cut classifier works effectively for identifying valuable cuts, and the LearnBD algorithm reduces the total solving time of all instances for different problems with various sizes and complexities.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1992

Magnetic Repeat Station Data

The nature, purpose, availability and distribution of magnetic repeat stations data are reviewed. Attention is paid to how repeat station data are obtained, sources of errors and limitations on accuracy. Repeat station results cannot achieve the accuracy of magnetic observatory estimates of the secular variation, but are, nevertheless, essential for improving the limited spatial coverage provided by observatories. The lack of appropriate standards and reporting procedures have meant that most global field molders have made little, or no, use of repeat data. Partly to rectify this problem, IAGA Working Group V-4 has instigated a scheme for systematic reporting and classification of magnetic repeat station data. The scheme will help to ensure that proper use is made of the wealth of repeat data that is available, and will also promote better observational practices.

Barton, C. E.↗

NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗

NASA NTRS1994

Neural network application to comprehensive engine diagnostics

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

Marko, Kenneth A.↗

NASA NTRS1993

Classification of Simple Vegetation Types Using POLSAR Image Data

Freeman, A.↗

NASA NTRS1986

Incipient fault detection study for advanced spacecraft systems

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

Milner, G. Martin↗

NASA NTRS1976

An ad hoc map evaluation procedure

An ad hoc map evaluation procedure is proposed which is most suitable for evaluating low-resolution classification maps against high resolution ground truth maps, such as maps against interpreted aircraft photographs. Commonly practiced sampling and evaluation procedures are impracticable in this context because of difficulties in registration and in comparing the samples. This ad hoc procedure is designed to overcome these two major problems, and its practicability is discussed. Two widely accepted parameters are estimated by the new procedure; namely, the probability of correct classification and the proportion biases. Statistical qualifications are also provided.

Kan, E. P.↗

NASA NTRS1987

Gaussian maximum likelihood and contextual classification algorithms for multicrop classification

The paper reviews some of the ways in which context has been handled in the remote-sensing literature, and additional possibilities are introduced. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

Di Zenzo, Silvano↗

NASA NTRS1981

Comet nucleus - Some characteristics and a hypothesis on origin and structure

The spectroscopic properties of comets are discussed, and consideration is given to the problems of the cosmic ray irradiation and the origin and structure of cometary nuclei. The classification of cometary spectra as continuum dominant, molecular emissions dominant and CO(+) dominant is examined, and changes in spectral characteristics with heliocentric distance although not with cometary age are pointed out. Spectral evidence for a variety of organic compounds in the cometary nucleus is noted, and a scenario is presented whereby these complex molecules may form as a result of cosmic ray irradiation of cometary ices in the Oort cloud. Finally, a model is proposed for the accumulation of small icy grains to form a cometary nucleus composed of cometesimals of size distribution following a -5th power dependence on mass.

Donn, B.↗

NASA NTRS1989

Fundamentals of tribology at the atomic level

Tribology, the science and engineering of solid surfaces in moving contact, is a field that encompasses many disciplines: solid state physics, chemistry, materials science, and mechanical engineering. In spite of the practical importance and maturity of the field, the fundamental understanding of basic phenomena has only recently been attacked. An attempt to define some of these problems and indicate some profitable directions for future research is presented. There are three broad classifications: (1) fluid properties (compression, rheology, additives and particulates); (2) material properties of the solids (deformation, defect formation and energy loss mechanisms); and (3) interfacial properties (adhesion, friction chemical reactions, and boundary films). Research in the categories has traditionally been approached by considering macroscopic material properties. Recent activity has shown that some issues can be approached at the atomic level: the atoms in the materials can be manipulated both experimentally and theoretically, and can produce results related to macroscopic phenomena.

Ferrante, John↗

NASA NTRS1989

Designers as users - Design supports based on crew system design practices

The behavioral processes in aviation crew system design are theoretically discussed. Interviews with designers concerning actual design experiences were conducted in a structured manner, focusing on the nature of the design problem, the designers's tasks, how decisions governing the human operator's role are made, and how this decision making can be supported. Two taxonomies emerge, one of which summarizes the nature of the design problem and the second of which summarizes the tasks that designers perform toward solving the problem. Combining the taxonomies yields a design problem space that is used to classify existing forms of support. The results of this classification and the implications for needed supports are discussed.

Cody, William J.↗

DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks

Modern neural networks (NNs) often do not generalize well in the presence of a ‘covariate shift’; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels given the data remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more robust, domain-invariant features. Domain adaptation (DA) methods include a broad range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SInkhorn Dynamic Domain Adaptation (SIDDA), an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, real astronomical observations, and remote sensing data. These datasets exhibit covariate shifts due to noise, blurring, differences between telescopes, and variations in imaging wavelengths. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with symmetry-aware equivariant NNs (ENNs). We find that SIDDA consistently enhances the generalization capabilities of NNs, achieving up to a ${\approx}40\%$ improvement in classification accuracy on unlabeled target data, while also providing a more modest performance gain of $\lesssim 1\%$ on labeled source data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, if SIDDA achieves proper domain alignment, it also enhances model calibration on both source and target data, with the most significant gains in the unlabeled target domain—achieving over an order of magnitude improvement in the expected calibration error and Brier score. SIDDA’s versatility across various NN models and datasets, combined with its automated approach to domain alignment, has the potential to significantly advance multi-dataset studies by enabling the development of highly generalizable models.

79 ASTRONOMY AND ASTROPHYSICS↗