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At least 433 records · Page 24

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NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗

NASA NTRS2004

Comparison of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Earth Observing One (EO-1) Advanced Land Imager

We compare images from the Enhanced Thematic Mapper Plus (ETM+) sensor on Landsat-7 and the Advanced Land Imager (ALI) instrument on Earth Observing One (EO-1) over a test site in Rochester, New York. The site contains a variety of features, ranging from water of varying depths, deciduous/coniferous forest, and grass fields, to urban areas. Nearly coincident cloud-free images were collected one minute apart on 25 August 2001. We also compare images of a forest site near Howland, Maine, that were collected on 7 September, 2001. We atmospherically corrected each pair of images with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmosphere model, using aerosol optical thickness and water vapor column density measured by in situ Cimel sun photometers within the Aerosol Robotic Network (AERONET), along with ozone density derived from the Total Ozone Mapping Spectrometer (TOMS) on the Earth Probe satellite. We present true-color composites from each instrument that show excellent qualitative agreement between the multispectral sensors, along with grey-scale images that demonstrate a significantly improved ALI panchromatic band. We quantitatively compare ALI and ETM+ reflectance spectra of a grassy field in Rochester and find < or equal to 6% differences in the visible/near infrared and approx. 2% differences in the short wave infrared. Spectral comparisons of forest sites in Rochester and Howland yield similar percentage agreement except for band 1, which has very low reflectance. Principal component analyses and comparison of normalized difference vegetation index histograms for each sensor indicate that the ALI is able to reproduce the information content in the ETM+ but with superior signal-to-noise performance due to its increased 12-bit quantization.

Pedelty, Jeffrey A.↗

NASA NTRS2004

Polymeric Materials Resistant to Erosion by Atomic Oxygen

Polymer-matrix composites are ideally suited for space vehicles because of high strength to weight ratios. The principal component of the low earth orbit (LEO) is atomic oxygen. Atomic oxygen causes surface erosion to polymeric materials. Polymer films with an organometallic additive showed greater resistance to atomic oxygen than the pure polymer in laboratory experiments and in the OPM/MIR experiment. In MISSE, the film with the organometallic additive was still intact after the pure film had completely eroded.

Kiefer, Richard L.↗

NASA NTRS2004

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Neural Networks (Computer)↗

NASA NTRS2005

On the Behavior of Phosphorus During the Aqueous Alteration of CM2 Carbonaceous Chondrites

During the earliest period of solar system formation, water played an important role in the evolution of primitive dust, both after accretion of planetesimals and possible before accretion within the protoplanetary disk. Many chondrites show evidence of variable degrees of aqueous alteration, the CM2 chondrites being among the most studied [1]. This group of chondrites is characterized by mineral assemblages of both primary and secondary alteration phases. Hence, these meteorites retain a particularly important record of the reactions that occurred between primary high temperature nebular phases and water. Studies of these chondrites can provide information on the conditions and environments of aqueous alteration and the mobility of elements during alteration. This latter question is at the core of a debate concerning the location of aqueous alteration, i.e. whether alteration occurred predominantly within a closed system after accretion (parent body alteration) or whether some degree of alteration occurred within the solar nebula or on ephemeral protoplanetary bodies prior to accretion. At the core of the parent body alteration model is the hypothesis that elemental exchange between different components, principally chondrules and matrix, must have occurred. chondrules and matrix, must have occurred. In this study, we focus on the behavior of the minor element, phosphorus. This study was stimulated by observations of the behavior of P during the earliest stages of alteration in glassy mesostasis in type II chondrules in CR chondrites and extends the preliminary observations of on Y791198 to other CM chondrites.

Brearley, Adrian J.↗

NASA NTRS2005

PlanWorks: A Debugging Environment for Constraint Based Planning Systems

Numerous planning and scheduling systems employ underlying constraint reasoning systems. Debugging such systems involves the search for errors in model rules, constraint reasoning algorithms, search heuristics, and the problem instance (initial state and goals). In order to effectively find such problems, users must see why each state or action is in a plan by tracking causal chains back to part of the initial problem instance. They must be able to visualize complex relationships among many different entities and distinguish between those entities easily. For example, a variable can be in the scope of several constraints, as well as part of a state or activity in a plan; the activity can arise as a consequence of another activity and a model rule. Finally, they must be able to track each logical inference made during planning. We have developed PlanWorks, a comprehensive system for debugging constraint-based planning and scheduling systems. PlanWorks assumes a strong transaction model of the entire planning process, including adding and removing parts of the constraint network, variable assignment, and constraint propagation. A planner logs all transactions to a relational database that is tailored to support queries for of specialized views to display different forms of data (e.g. constraints, activities, resources, and causal links). PlanWorks was specifically developed for the Extensible Universal Remote Operations Planning Architecture (EUROPA(sub 2)) developed at NASA, but the underlying principles behind PlanWorks make it useful for many constraint-based planning systems. The paper is organized as follows. We first describe some fundamentals of EUROPA(sub 2). We then describe PlanWorks' principal components. We then discuss each component in detail, and then describe inter-component navigation features. We close with a discussion of how PlanWorks is used to find model flaws.

Daley, Patrick↗

NASA NTRS2003

Preliminary Results Obtained in Integrated Safety Analysis of NASA Aviation Safety Program Technologies

The goal of the NASA Aviation Safety Program (AvSP) is to develop and demonstrate technologies that contribute to a reduction in the aviation fatal accident rate by a factor of 5 by the year 2007 and by a factor of 10 by the year 2022. Integrated safety analysis of day-to-day operations and risks within those operations will provide an understanding of the Aviation Safety Program portfolio. Safety benefits analyses are currently being conducted. Preliminary results for the Synthetic Vision Systems (SVS) and Weather Accident Prevention (WxAP) projects of the AvSP have been completed by the Logistics Management Institute under a contract with the NASA Glenn Research Center. These analyses include both a reliability analysis and a computer simulation model. The integrated safety analysis method comprises two principal components: a reliability model and a simulation model. In the reliability model, the results indicate how different technologies and systems will perform in normal, degraded, and failed modes of operation. In the simulation, an operational scenario is modeled. The primary purpose of the SVS project is to improve safety by providing visual-flightlike situation awareness during instrument conditions. The current analyses are an estimate of the benefits of SVS in avoiding controlled flight into terrain. The scenario modeled has an aircraft flying directly toward a terrain feature. When the flight crew determines that the aircraft is headed toward an obstruction, the aircraft executes a level turn at speed. The simulation is ended when the aircraft completes the turn.

Reveley, Mary S.↗

NASA NTRSDate not supplied

Nondestructive Evaluation for the Space Shuttle's Wing Leading Edge

The loss of the Space Shuttle Columbia highlighted concerns about the integrity of the Shuttle's thermal protection system, which includes Reinforced Carbon-Carbon (RCC) on the leading edge. This led NASA to investigate nondestructive evaluation (NDE) methods for certifying the integrity of the Shuttle's wing leading edge. That investigation was performed simultaneously with a large study conducted to understand the impact damage caused by errant debris. Among the many advanced NDE methods investigated for applicability to the RCC material, advanced digital radiography, high resolution computed tomography, thermography, ultrasound, acoustic emission and eddy current systems have demonstrated the maturity and success for application to the Shuttle RCC panels. For the purposes of evaluating the RCC panels while they are installed on the orbiters, thermographic detection incorporating principal component analysis (PCA) and eddy current array scanning systems demonstrated the ability to measure the RCC panels from one side only and to detect several flaw types of concern. These systems were field tested at Kennedy Space Center (KSC) and at several locations where impact testing was being conducted. Another advanced method that NASA has been investigating is an automated acoustic based detection system. Such a system would be based in part on methods developed over the years for acoustic emission testing. Impact sensing has been demonstrated through numerous impact tests on both reinforced carbon-carbon (RCC) leading edge materials as well as Shuttle tile materials on representative aluminum wing structures. A variety of impact materials and conditions have been evaluated including foam, ice, and ablator materials at ascent velocities as well as simulated hypervelocity micrometeoroid and orbital debris impacts. These tests have successfully demonstrated the capability to detect and localize impact events on Shuttle's wing structures. A first generation impact sensing system has been designed for the next Shuttle flight and is undergoing final evaluation for deployment on the Shuttle's first return to flight. This system will employ wireless accelerometer sensors that were qualified for other applications on previous Shuttle flights. These sensors will be deployed on the wing's leading edge to detect impacts on the RCC leading edge panels. The application of these methods will help to insure the continued integrity of the Shuttle wing's leading edge system as the Shuttle flights resume and until their retirement.

Eric I Madaras↗

NASA NTRS2005

Sunspot Pattern Classification using PCA and Neural Networks (Poster)

The sunspot classification scheme presented in this paper is considered as a 2-D classification problem on archived datasets, and is not a real-time system. As a first step, it mirrors the Zuerich/McIntosh historical classification system and reproduces classification of sunspot patterns based on preprocessing and neural net training datasets. Ultimately, the project intends to move from more rudimentary schemes, to develop spatial-temporal-spectral classes derived by correlating spatial and temporal variations in various wavelengths to the brightness fluctuation spectrum of the sun in those wavelengths. Once the approach is generalized, then the focus will naturally move from a 2-D to an n-D classification, where "n" includes time and frequency. Here, the 2-D perspective refers both to the actual SOH0 Michelson Doppler Imager (MDI) images that are processed, but also refers to the fact that a 2-D matrix is created from each image during preprocessing. The 2-D matrix is the result of running Principal Component Analysis (PCA) over the selected dataset images, and the resulting matrices and their eigenvalues are the objects that are stored in a database, classified, and compared. These matrices are indexed according to the standard McIntosh classification scheme.

Rajkumar, T.↗

NASA NTRS2005

The Application of Infrared Thermographic Inspection Techniques to the Space Shuttle Thermal Protection System

The Nondestructive Evaluation Sciences Branch at NASA s Langley Research Center has been actively involved in the development of thermographic inspection techniques for more than 15 years. Since the Space Shuttle Columbia accident, NASA has focused on the improvement of advanced NDE techniques for the Reinforced Carbon-Carbon (RCC) panels that comprise the orbiter s wing leading edge. Various nondestructive inspection techniques have been used in the examination of the RCC, but thermography has emerged as an effective inspection alternative to more traditional methods. Thermography is a non-contact inspection method as compared to ultrasonic techniques which typically require the use of a coupling medium between the transducer and material. Like radiographic techniques, thermography can be used to inspect large areas, but has the advantage of minimal safety concerns and the ability for single-sided measurements. Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. A typical implementation of PCA is when the eigenvectors are generated from the data set being analyzed. Although it is a powerful tool for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from the RCC materials. Details of a one-dimensional analytic model and a two-dimensional finite-element model will be presented. An overview of the PCA process as well as a quantitative signal-to-noise comparison of the results of performing both embodiments of PCA on thermographic data from various RCC specimens will be shown. Finally, a number of different applications of this technology to various RCC components will be presented.

Cramer, K. E.↗

NASA NTRS2006

Signal to Noise Studies on Thermographic Data with Fabricated Defects for Defense Structures

There is a growing international interest in thermal inspection systems for asset life assessment and management of defense platforms. The efficacy of flash thermography is generally enhanced by applying image processing algorithms to the observations of raw temperature. Improving the defect signal to noise ratio (SNR) is of primary interest to reduce false calls and allow for easier interpretation of a thermal inspection image. Several factors affecting defect SNR were studied such as data compression and reconstruction using principal component analysis and time window processing.

Zalameda, Joseph N.↗

NASA NTRS2003

Kernel PLS-SVC for Linear and Nonlinear Discrimination

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Rosipal, Roman↗

NASA NTRS2006

Global Aspects of Heliosphere-Geosphere Coupling

The magnetosphere serves both to hold off the solar wind and to couple it selectively to the Earth through the auroral zones. It has long been understood that the plasmasphere consists of geogenic plasmas that expand out of the sunlit low latitude ionosphere, but it was initially assumed that the hot low density plasma beyond the plasmasphere is largely of solar wind origin with a minor admixture of ionospheric plasmas, discovered via mass spectrometric observations in the early 70's. Since then, elaborate simulation models have been developed that have taken us from an era of cartoon physics to a new era of quantitative global comparisons between observations and theory. In most current global circulation models of the magnetosphere, the ionospheric load on the system is taken to lie exclusively in the thin F layer of the ionosphere. This layer is coupled with solar wind and magnetospheric plasmas via Maxwell stresses communicated by field aligned current systems, and with the thermosphere via ion-neutral charge exchange and Coulomb collisions. However, recent observations have shown us that ionospheric plasmas flow sporadically in various forms into the plasmasphere and up into the high latitude circulation cells of the outer magnetosphere, as driven by the solar wind and its variable intensity and magnetic field. Under some conditions, ionospheric material is observed to be the principal component of plasmas at the dayside magnetopause and in the plasma sheet and ring current regions. Given a global model of magnetospheric circulation, it is relatively straightforward to investigate the behaviors of ionospheric plasmas in response to solar wind drivers, and we report the results of such efforts here. We find that ionospheric plasmas dominate the plasma pressure in the magnetosphere in some regions, particularly when the solar wind is especially intense and-or southward directed. This result violates the assumption that the ionospheric load is confined to the F layer, and shows that the ionosphere is often an important dynamic element of the solar wind-ionosphere system throughout the magnetosphere, especially during larger geospace storms. This means that future global circulation models must account for ionospheric plasma inertia, heat capacity, and pressure, to be quantitatively credible.

Moore, Thomas E.↗

NASA NTRS1994

Anthropomorphic Telemanipulation System in Terminus Control Mode

This paper describes a prototype anthropomorphic kinesthetic telepresence system that is being developed at JPL. It utilizes dexterous terminus devices in the form of an exoskeleton force-sensing master glove worn by the operator and a replica four finger anthropomorphic slave hand. The newly developed master glove is integrated with our previously developed non-anthropomorphic six degree of freedom (DOF) universal force-reflecting hand controller (FRHC). The mechanical hand and forearm are mounted to an industrial robot (PUMA 560), replacing its standard forearm. The notion of 'terminus control mode' refers to the fact that only the terminus devices (glove and robot hand) are of anthropomorphic nature, and the master and slave arms are non-anthropomorphic. The system is currently being evaluated, focusing on tool handling and astronaut equivalent task executions. The evaluation revealed the system's potential for tool handling but it also became evident that hand tool manipulations and space operations require a dual arm robot. This paper describes the system's principal components, its control and computing architecture, discusses findings of the tool handling evaluation, and explains why common tool handling and EVA space tasks require dual arm robots.

glove exoskeleton force-sensing master glove EVA s↗

NASA NTRS1993

The El Nino/Southern Oscillation and Future Soybean Prices

Recently, it was shown that the application of a method combining singular spectrum analysis (SSA) and the maximum entropy method to univariate indicators of the coupled ocean-atmosphere El Nino/Southern Oscillation (ENSO) phenomenon can be helpful in determining whether an El Nino (EN) or La Nina (LN) event will occur. SSA - a variant of principal component analysis applied in the time domain - filters out variability unrelated to ENSO and separates the quasi-biennial (QB), two-to-three year variability, from a lower-frequency (LF) four-to-six year EN-LN cycle; the total variance associated with ENSO combines the QB and LF modes. ENSO has been known to affect weather conditions over much of the globe. For example, EN events have been connected with unusually rainy weather over the Central and Western US, while the opposite phases of the oscillation (LN) have been plausibly associated with extreme dry conditions over much of the same geographical area...

Keppenne, C.↗

NASA NTRS1999

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

meteorites Amino acids Multivariate Analysis↗

NASA NTRS2006

Probabilistic Classification Using Elemental Abundance Distributions and Lossless Image Compression in Apollo 17 Lunar Dust Samples from Mare Serenitatis

We have previously outlined a strategy for the detection of fossils [Storrie-Lombardi and Hoover, 2004] and extant microbial life [Storrie-Lombaudi and Hoover, 20051 during robotic missions to Mars using co-registered structural and chemical signatures. Data inputs included image lossless compression indices to estimate relative textural complexity and elemental abundance distributions. Two exploratory classification algorithms (principal component analysis and hierarchical cluster analysis) provide an initial tentative classification of all targets. Nonlinear stochastic neural networks are then trained to produce a Bayesian estimate of algorithm classification accuracy. The strategy previously has been successful in distinguishing regions of biotic and abiotic alteration of basalt glass from unaltered samples. [Storrie-Lombardi and Fisk, 2004; Storrie-Lombardi and Fisk, 2004] Such investigations of abiotic versus biotic alteration of terrestrial mineralogy on Earth are compromised by .the difficulty finding mineralogy completely unaffected by the ubiquitous presence of microbial life on the planet. The renewed interest in lunar exploration offers an opportunity to investigate geological materials that may exhibit signs of aqueous alteration, but are highly unlikely to contain contaminating biological weathering signatures. We here present an extension of our earlier data set to include lunar dust samples obtained during the Apollo 17 mission. Apollo 17 landed in the Taurus-Littrow Valley in Mare Serenitatis. Most of the rock samples from this region of the lunar highlands are basalts comprised primarily of plagioclase and pyroxene and selected examples of orange and black volcanic glass. SEM images and elemental abundances (C6, N7, O8, Na11, Mg12, Al13, Si14, P15, S16, Cll7, K19, Ca20, Fe26) for a series of targets in the lunar dust samples are compared to the extant cyanobacteria, fossil trilobites, Orgueil meteorite, and terrestrial basalt targets previously discussed. The data set provides a first step in producing a quantitative probabilistic methodology for geobiological analysis of returned lunar samples or in situ exploration.

Storrie-Lombardi, Michael C.↗

NASA NTRS2005

Fault Diagnosis in HVAC Chillers

Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

Choi, Kihoon↗