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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

A Dual-Route Model that Learns to Pronounce English Words

This paper describes a model that learns to pronounce English words. Learning occurs in two modules: 1) a rule-based module that constructs pronunciations by phonetic analysis of the letter string, and 2) a whole-word module that learns to associate subsets of letters to the pronunciation, without phonetic analysis. In a simulation on a corpus of over 300 words the model produced pronunciation latencies consistent with the effects of word frequency and orthographic regularity observed in human data. Implications of the model for theories of visual word processing and reading instruction are discussed.

Remington, Roger W.↗
NASA NTRS2001

Atmospheric Excitation of Planetary Normal Modes

The objectives of this study were to: (1) understand the phenomenon of continuous free oscillations of the Earth and (2) examine the idea of using this phenomenon for planetary seismology. We first describe the results on (1) and present our evaluations of the idea (2) in the final section. In 1997, after almost forty years since the initial attempt by Benioff et al, continuous free oscillations of the Earth were discovered. Spheroidal fundamental modes between 2 and 7 millihertz are excited continuously with acceleration amplitudes of about 0.3-0.5 nanogals. The signal is now commonly found in virtually all data recorded by STS-1 type broadband seismometers at quiet sites. Seasonal variation in amplitude and the existence of two coupled modes between the atmosphere and the solid Earth support that these oscillations are excited by the atmosphere. Stochastic excitation due to atmospheric turbulence is a favored mechanism, providing a good match between theory and data. The atmosphere has ample energy to support this theory because excitation of these modes require only 500-10000 W whereas the atmosphere contains about 117 W of kinetic energy. An application of this phenomenon includes planetary seismology, because other planets may be oscillating due to atmospheric excitation. The interior structure of planets could be learned by determining the eigenfrequencies in the continuous free oscillations. It is especially attractive to pursue this idea for tectonically quiet planets, since quakes may be too infrequent to be recorded by seismic instruments.

Tanimoto, Toshiro↗
NASA NTRS2000

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗
NASA NTRS2002

Machine Learning for Biological Trajectory Classification Applications

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Sbalzarini, Ivo F.↗
NASA NTRS2010

Landmark Detection in Orbital Images Using Salience Histograms

NASA's planetary missions have collected, and continue to collect, massive volumes of orbital imagery. The volume is such that it is difficult to manually review all of the data and determine its significance. As a result, images are indexed and searchable by location and date but generally not by their content. A new automated method analyzes images and identifies "landmarks," or visually salient features such as gullies, craters, dust devil tracks, and the like. This technique uses a statistical measure of salience derived from information theory, so it is not associated with any specific landmark type. It identifies regions that are unusual or that stand out from their surroundings, so the resulting landmarks are context-sensitive areas that can be used to recognize the same area when it is encountered again. A machine learning classifier is used to identify the type of each discovered landmark. Using a specified window size, an intensity histogram is computed for each such window within the larger image (sliding the window across the image). Next, a salience map is computed that specifies, for each pixel, the salience of the window centered at that pixel. The salience map is thresholded to identify landmark contours (polygons) using the upper quartile of salience values. Descriptive attributes are extracted for each landmark polygon: size, perimeter, mean intensity, standard deviation of intensity, and shape features derived from an ellipse fit.

Wagstaff, Kiri L.↗
NASA NTRSDate not supplied

Utilizing Experimental-based Line Positions for Semi-empirical IR Line Lists

Molecular IR line lists computed from semi-empirically refined ab initio potential energy surface and high quality ab initio dipole moment surface may have line position accuracy typically within 0.01 - 0.05 cm−1. For high resolution spectroscopy databases, it is necessary to integrate the computed theoretical IR intensities with the line positions accurately determined from experiments and (or) reliably derived from Effective Hamiltonian models. However, it is not a trivial task, and the choices in reality are heavily contingent upon the coverage, consistency, and accuracy of the data available for a specific molecule or isotopologue. We will present several approaches applied to recent Ames IR line lists of CO 2 1, N 2 O 2 , and OCS to demonstrate how challenging such integration may become, and what we have learned From these projects. Discussions will focus on the relation between each different scenario and the corresponding choice or solution, including their advantages and limits, how the semi-empirical IR line lists can help, and what else may be needed for future improvements. We will emphasize that the “Theory+Experiment” synergy may still play significant role in the determination of the best line positions.

Effective Hamiltonian models↗
NASA NTRS2016

Unique Results and Lessons Learned from the TSS Missions

In 1924, Irvin Langmuir and H. M. Mott-Smith published a theoretical model for the complex plasma sheath phenomenon in which they identified some very special cases which greatly simplified the sheath and allowed a closed solution to the problem. The most widely used application is for an electrostatic, or "Langmuir," probe in laboratory plasma. Although the Langmuir probe is physically simple (a biased wire) the theory describing its functional behavior and its current-voltage characteristic is extremely complex and, accordingly, a number of assumptions and approximations are used in the LMS model. These simplifications, correspondingly, place limits on the model's range of application. Adapting the LMS model to real-life conditions is the subject of numerous papers and dissertations. The Orbit-Motion Limited (OML) model that is widely used today is one of these adaptions that is a convenient means of calculating sheath effects. The OML equation for electron current collection by a positively biased body is simply: I is approximately equal to A x j(sub eo) x 2/√𝜋(phi)(exp ½) where A is the area of the body and phi is the electric potential on the body with respect to the plasma. Since the Langmuir probe is a simple biased wire immersed in plasma, it is particularly tempting to use the OML equation in calculating the characteristics of the long, highly biased wires of an Electric Sail in the solar wind plasma. However, in order to arrive at the OML equation, a number of additional simplifying assumptions and approximations (beyond those made by Langmuir-Mott-Smith) are necessary. The OML equation is a good approximation when all conditions are met, but it would appear that the Electric Sail problem lies outside of the limits of applicability.

Stone, Nobie H.↗
NASA NTRS1995

Inductive Approaches to Improving Diagnosis and Design for Diagnosability

The first research area under this grant addresses the problem of classifying time series according to their morphological features in the time domain. A supervised learning system called CALCHAS, which induces a classification procedure for signatures from preclassified examples, was developed. For each of several signature classes, the system infers a model that captures the class's morphological features using Bayesian model induction and the minimum message length approach to assign priors. After induction, a time series (signature) is classified in one of the classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. A second area of research assumes two sources of information about a system: a model or domain theory that encodes aspects of the system under study and data from actual system operations over time. A model, when it exists, represents strong prior expectations about how a system will perform. Our work with a diagnostic model of the RCS (Reaction Control System) of the Space Shuttle motivated the development of SIG, a system which combines information from a model (or domain theory) and data. As it tracks RCS behavior, the model computes quantitative and qualitative values. Induction is then performed over the data represented by both the 'raw' features and the model-computed high-level features. Finally, work on clustering for operating mode discovery motivated some important extensions to the clustering strategy we had used. One modification appends an iterative optimization technique onto the clustering system; this optimization strategy appears to be novel in the clustering literature. A second modification improves the noise tolerance of the clustering system. In particular, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus making post-clustering analysis easier.

Fisher, Douglas H.↗
NASA NTRS1988

Accretion and primary differentiation of Mars

In collecting samples from Mars to address questions such as whether Mars accreted homogeneously or heterogeneously, how Mars segregated into a metallic core and silicate mantle, and whether Mars outgassed catastrophically coincident with accretion or more serenely on a longer timescale, we must be guided by our experience in addressing these questions for the Earth, Moon, and igneous meteorite parent bodies. A key measurement to be made on any sample returned from Mars is its oxygen isotopic composition. A single measurement will suffice to bind the SNC meteorites to Mars or demonstrate that they cannot be samples of that planet. A positive identification of Mars as the SNC parent planet will permit all that has been learned from the SNC meteorites to be applied to Mars with confidence. A negative result will perhaps be more exciting in forcing us to look for another object that has been geologically active in the recent past. If the oxygen isotopic composition of Earth and Mars are established to be distinct, accretion theory must provide for different compositions for two planets now separated by only 0.5 AU.

Drake, Michael J.↗
NASA NTRS1990

Introduction to Fuzzy Set Theory

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Kosko, Bart↗
NASA NTRS2015

Using Technology to Better Characterize the Apollo Sample Suite: A Retroactive PET Analysis and Potential Model for Future Sample Return Missions

From 1969-1972 the Apollo missions collected 382 kg of lunar samples from six distinct locations on the Moon. Studies of the Apollo sample suite have shaped our understanding of the formation and early evolution of the Earth-Moon system, and have had important implications for studies of the other terrestrial planets (e.g., through the calibration of the crater counting record) and even the outer planets (e.g., the Nice model of the dynamical evolution of the Solar System). Despite nearly 50 years of detailed research on Apollo samples, scientists are still developing new theories about the origin and evolution of the Moon. Three areas of active research are: (1) the abundance of water (and other volatiles) in the lunar mantle, (2) the timing of the formation of the Moon and the duration of lunar magma ocean crystallization, (3) the formation of evolved lunar lithologies (e.g., granites) and implications for tertiary crustal processes on the Moon. In order to fully understand these (and many other) theories about the Moon, scientists need access to "new" lunar samples, particularly new plutonic samples. Over 100 lunar meteorites have been identified over the past 30 years, and the study of these samples has greatly aided in our understanding of the Moon. However, terrestrial alteration and the lack of geologic context limit what can be learned from the lunar meteorites. Although no "new" large plutonic samples (i.e., hand-samples) remain to be discovered in the Apollo sample collection, there are many large polymict breccias in the Apollo collection containing relatively large (approximately 1 cm or larger) previously identified plutonic clasts, as well as a large number of unclassified lithic clasts. In addition, new, previously unidentified plutonic clasts are potentially discoverable within these breccias. The question becomes how to non-destructively locate and identify new lithic clasts of interest while minimizing the contamination and physical degradation of the samples.

Zeigler, R. A.↗
NASA NTRS1994

Building brains for bodies

We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We will build an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to 'think' by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience.

Brooks, Rodney Allen↗
NASA NTRS1993

A new approach for designing self-organizing systems and application to adaptive control

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.

Ramamoorthy, P. A.↗
NASA NTRS1993

Reconfigurable fuzzy cell

This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.

Salazar, George A.↗
NASA NTRS2003

Neural Flight Control System

The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

Gundy-Burlet, Karen↗
NASA NTRS2018

Digital Technologies at NASA for Science and Engineering

While scientific and engineering advancements used to rely primarily on theoretical studies and physical experiments, today digital technology enabled by petaflops-scale supercomputers is an equal, if not a greater, contributor to such achievements. In addition, computational modeling and simulation serves as a predictive tool that is not otherwise available. As a result, the use of high performance computing is integral to NASA's work in all mission areas such as space exploration, aeronautics, and scientific discovery. But traditional supercomputing alone is not sufficient for all of the space agency's needs. The success of many NASA missions depends on solving complex computing challenges, some of which are NP-hard (decision theory) if using classical solution methods. Quantum computing promises an unprecedented ability to solve such intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. Another disruptive digital technology is neuromorphic computing that uses brain-inspired lessons to generate new architectures that are much more energy efficient, and capable of massive parallel processing and learning in-situ. Finally, with large amounts of observational and computational data sets, the opportunities of big data and data analytics can be leveraged to enable deep learning and knowledge discovery - it's all a massive digital transformation. This talk will be an overview how NASA utilizes digital technologies for its science and engineering efforts.

Biswas, Rupak↗
NASA NTRS1963

Integrated Mission Simulation for Long Term Space Flight

The problems of evaluating pilot performance, pilot training, and the integration of the man-machine system for proper functioning become increasingly difficult as the length of projected space flights increases. Further, actual in-flight experience becomes harder to obtain and the major means for training and evaluation becomes simulation. The first major source of difficulty in any simulation stems from a basic requirement of leaning theory: that for increased proficiency of performance to occur within any biologically capable system, the stimuli which are the bases for the expected behavior must be presented in an amenable form to the learner. Further, the sequence of presentation of the pertinent stimuli must be such that the learner can form the appropriate concepts and interactions required for the ultimate use of the learned material. It can then be seen that two basic factors of learning complex skills are: 1) Fidelity of simulation of pertinent stimuli. 2) Appropriate sequencing of stimuli during learning for proper concept formation.

SPACE FLIGHT↗
NASA NTRS1988

Autonomous control systems - Architecture and fundamental issues

A hierarchical functional autonomous controller architecture is introduced. In particular, the architecture for the control of future space vehicles is described in detail; it is designed to ensure the autonomous operation of the control system and it allows interaction with the pilot and crew/ground station, and the systems on board the autonomous vehicle. The fundamental issues in autonomous control system modeling and analysis are discussed. It is proposed to utilize a hybrid approach to modeling and analysis of autonomous systems. This will incorporate conventional control methods based on differential equations and techniques for the analysis of systems described with a symbolic formalism. In this way, the theory of conventional control can be fully utilized. It is stressed that autonomy is the design requirement and intelligent control methods appear at present, to offer some of the necessary tools to achieve autonomy. A conventional approach may evolve and replace some or all of the `intelligent' functions. It is shown that in addition to conventional controllers, the autonomous control system incorporates planning, learning, and FDI (fault detection and identification).

Antsaklis, P. J.↗