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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Classification of Aircraft Maneuvers for Fault Detection

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.

Oza, Nikunj C.↗
NASA NTRS1979

Classification and equivalence in estimation theory

A method is proposed for classifying estimation problems based on the Lie algebra generated by the operators which appear in the conditional density equation. A natural class of automorphisms of this algebra is examined and a systematic method of generating equivalent problems is developed. Finally, a new class of nonlinear filtering problems with essentially nonlinear filtering equations are presented.

Brockett, R. W.↗
NASA NTRS2002

Classification of Aircraft Maneuvers for Fault Detection

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

Oza, Nikunj↗
NASA NTRS1979

Classification and mensuration of LACIE segments

The theory of classification methods and the functional steps in the manual training process used in the three phases of LACIE are discussed. The major problems that arose in using a procedure for manually training a classifier and a method of machine classification are discussed to reveal the motivation that led to a redesign for the third LACIE phase.

Heydorn, R. P.↗
NASA NTRS1979

Remote Sensing and Problems of the Hydrosphere

A discussion of freshwater and marine systems is presented including areas of the classification of lakes, identification and quantification of major functional groups of phytoplankton, sources and sinks of biochemical factors, and temporal and regional variability of surface features. Atmospheric processes linked to hydrospheric process through the transfer of matter via aerosols and gases are discussed. Particle fluxes to the aquatic environment and global geochemical problems are examined.

Goldberg, E. D.↗
NASA NTRS1974

STANSORT - Stanford Remote Sensing Laboratory pattern recognition and classification system

The principal barrier to routine use of the ERTS multispectral scanner computer compatible tapes, rather than photointerpretation examination of the images, has been the high computing costs involved due to the large quantity of information (4 Mbytes) contained in a scene. STANSORT, the interactive program package developed at Stanford Remote Sensing Laboratories alleviates this problem, providing an extremely rapid, flexible and low cost tool for data reduction, scene classification, species searches and edge detection. The primary classification procedure, utilizing a search with variable gate widths, for similarities in the normalized, digitized spectra is described along with associated procedures for data refinement and extraction of information. The more rigorous statistical classification procedures are also explained.

Honey, F. R.↗
NASA NTRS1993

The dependence on morphology of the gas content in galactic disks

The classification S0 was introduced by Hubble to serve as a description of galaxies whose morphological characteristics seemed to lie between the disk-dominated spirals and the spheroidal elliptical systems. Since then there has been extensive discussion as to whether this classification sequence is also an evolutionary sequence. Many studies have focussed on a particular feature such as the luminosity profile, the bulge-to-disk ratio, or the nature of the interstellar matter, but the question of the evolution remains contentious. Equally contentious is the question of the classification itself. For systems with well-developed disks there usually is no problem. Many spheroidal systems also are unambiguously classified as ellipticals in most catalogs. However, there are a number of early systems which have been reclassified following review using improved optical material. For example, Eder et al. (AJ, 102, 572, 1991) found that many of the S0 galaxies which are rich in neutral hydrogen have faint spiral features. The confusion about classification propagates into the discussion of the properties of early-type systems. Attempts to put the classification system on a quantitative basis have in general been unsuccessful. Recently Sandage (private communication) has reviewed the classification of early systems and has defined a set of sub-classes for these objects. The S0 galaxies are divided into three groups, depending on the prominence of the disk. There are six subdivisions of Sa galaxies, depending upon the relative prominence of knots and other arm-like characteristics. We have explored the total gas content in these objects to see if there is a dependence on the galaxy morphology, as denoted by these new subclasses.

Hogg, D. E.↗
NASA NTRS1977

Application of LANDSAT data to wetland study and land use classification in west Tennessee

The Obion-Forked Deer River Basin in northwest Tennessee is confronted with several acute land use problems which result in excessive erosion, sedimentation, pollution, and hydrologic runoff. LANDSAT data was applied to determine land use of selected watershed areas within the basin, with special emphasis on determining wetland boundaries. Densitometric analysis was performed to allow numerical classification of objects observed in the imagery on the basis of measurements of optical densities. Multispectral analysis of the LANDSAT imagery provided the capability of altering the color of the image presentation in order to enhance desired relationships. Manual mapping and classification techniques were performed in order to indicate a level of accuracy of the LANDSAT data as compared with high and low altitude photography for land use classification.

Jones, N. L.↗
NASA NTRS1980

Decision making and problem solving with computer assistance

In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.

Kraiss, F.↗
NASA NTRS1980

Aircraft noise effects: An inter-disciplinary study of the effect of aircraft noise on man. Part 3: Supplementary analyses of the social-scientific portion of the study on aircraft noise conducted by the DFG

Variables in a study of noise perception near the Munich-Reims airport are explained. The interactive effect of the stimulus (aircraft noise) and moderator (noise sensitivity) on the aircraft noise reaction (disturbance or annoyance) is considered. Methods employed to demonstrate that the moderator has a differencing effect on various stimulus levels are described. Results of the social-scientific portion of the aircraft noise project are compared with those of other survey studies on the problem of aircraft noise. Procedures for contrast group analysis and multiple classification analysis are examined with focus on some difficulties in their application.

Schumer, R.↗
NASA NTRS1992

Automating sky object classification in astronomical survey images

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomer Observatory Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 10(exp 7) galaxies and 10(exp 8) stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. The size of this data set precludes manual analysis. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3* and O-BTree, two inductive learning techniques, learn classification decision trees from examples. These classifiers will be used to process the rest of the data. This paper gives an overview of the machine learning techniques used, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our approach is well-suited to the problem. The primary benefits of the approach are increased data reduction throughput and consistency of classification. The classification rules which are the product of the inductive learning techniques will form an object, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically cataloged data.

Fayyad, Usama M.↗
NASA NTRS1990

Autonomous power management and distribution

The goal of the Autonomous Power System program is to develop and apply intelligent problem solving and control to the Space Station Freedom's electric power testbed being developed at NASA's Lewis Research Center. Objectives are to establish artificial intelligence technology paths, craft knowledge-based tools and products for power systems, and integrate knowledge-based and conventional controllers. This program represents a joint effort between the Space Station and Office of Aeronautics and Space Technology to develop and demonstrate space electric power automation technology capable of: (1) detection and classification of system operating status, (2) diagnosis of failure causes, and (3) cooperative problem solving for power scheduling and failure recovery. Program details, status, and plans will be presented.

Dolce, Jim↗
NASA NTRS1993

Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning

Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals.

discretization continuous-valued attributes classi↗
NASA NTRS1974

Craters on Earth, Moon, and Mars - Multivariate classification and mode of origin

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian calderas, and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components.

Pike, R. J.↗
NASA NTRS2010

Confidence-Based Feature Acquisition

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

Wagstaff, Kiri L.↗
NASA NTRS2011

Numerical Treatment of Degenerate Diffusion Equations via Feller's Boundary Classification, and Applications

A numerical method is devised to solve a class of linear boundary-value problems for one-dimensional parabolic equations degenerate at the boundaries. Feller theory, which classifies the nature of the boundary points, is used to decide whether boundary conditions are needed to ensure uniqueness, and, if so, which ones they are. The algorithm is based on a suitable preconditioned implicit finite-difference scheme, grid, and treatment of the boundary data. Second-order accuracy, unconditional stability, and unconditional convergence of solutions of the finite-difference scheme to a constant as the time-step index tends to infinity are further properties of the method. Several examples, pertaining to financial mathematics, physics, and genetics, are presented for the purpose of illustration.

Cacio, Emanuela↗
NASA NTRS1993

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

A series of cloud and sea ice retrieval algorithms are being developed in support of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Science Team objectives. These retrievals include the following: cloud fractional area, cloud optical thickness, cloud phase (water or ice), cloud particle effective radius, cloud top heights, cloud base height, cloud top temperature, cloud emissivity, cloud 3-D structure, cloud field scales of organization, sea ice fractional area, sea ice temperature, sea ice albedo, and sea surface temperature. Due to the problems of accurately retrieving cloud properties over bright surfaces, an advanced cloud classification method was developed which is based upon spectral and textural features and artificial intelligence classifiers.

Welch, Ronald M.↗