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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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At least 433 records · Page 24

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NASA NTRS1991

Architecture for Survivable System Processing (ASSP)

The Architecture for Survivable System Processing (ASSP) Program is a multi-phase effort to implement Department of Defense (DOD) and commercially developed high-tech hardware, software, and architectures for reliable space avionics and ground based systems. System configuration options provide processing capabilities to address Time Dependent Processing (TDP), Object Dependent Processing (ODP), and Mission Dependent Processing (MDP) requirements through Open System Architecture (OSA) alternatives that allow for the enhancement, incorporation, and capitalization of a broad range of development assets. High technology developments in hardware, software, and networking models, address technology challenges of long processor life times, fault tolerance, reliability, throughput, memories, radiation hardening, size, weight, power (SWAP) and security. Hardware and software design, development, and implementation focus on the interconnectivity/interoperability of an open system architecture and is being developed to apply new technology into practical OSA components. To insure for widely acceptable architecture capable of interfacing with various commercial and military components, this program provides for regular interactions with standardization working groups (e.g.) the International Standards Organization (ISO), American National Standards Institute (ANSI), Society of Automotive Engineers (SAE), and Institute of Electrical and Electronic Engineers (IEEE). Selection of a viable open architecture is based on the widely accepted standards that implement the ISO/OSI Reference Model.

Wood, Richard J.↗

NASA NTRS1992

Real-time antenna fault diagnosis experiments at DSS 13

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

Mellstrom, J.↗

NASA NTRS1991

Artificial neural networks and approximate reasoning for intelligent control in space

A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

Berenji, Hamid R.↗

NASA NTRS1992

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

Sartori, Michael A.↗

NASA NTRS1993

Data communication network at the ASRM facility

This report describes the simulation of the overall communication network structure for the Advanced Solid Rocket Motor (ASRM) facility being built at Yellow Creek near Iuka, Mississippi as of today. The report is compiled using information received from NASA/MSFC, LMSC, AAD, and RUST Inc. As per the information gathered, the overall network structure will have one logical FDDI ring acting as a backbone for the whole complex. The buildings will be grouped into two categories viz. manufacturing intensive and manufacturing non-intensive. The manufacturing intensive buildings will be connected via FDDI to the Operational Information System (OIS) in the main computing center in B_1000. The manufacturing non-intensive buildings will be connected by 10BASE-FL to the OIS through the Business Information System (BIS) hub in the main computing center. All the devices inside B_1000 will communicate with the BIS. The workcells will be connected to the Area Supervisory Computers (ASCs) through the nearest manufacturing intensive hub and one of the OIS hubs. Comdisco's Block Oriented Network Simulator (BONeS) has been used to simulate the performance of the network. BONeS models a network topology, traffic, data structures, and protocol functions using a graphical interface. The main aim of the simulations was to evaluate the loading of the OIS, the BIS, and the ASCs, and the network links by the traffic generated by the workstations and workcells throughout the site.

Moorhead, Robert J., II↗

NASA NTRS1992

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Hsu, Ken-Yuh↗

NASA NTRS1994

How Captain Amerika uses neural networks to fight crime

Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication).

Rogers, Steven K.↗

NASA NTRS1996

Fluid Stochastic Petri Nets: Theory, Applications, and Solution

In this paper we introduce a new class of stochastic Petri nets in which one or more places can hold fluid rather than discrete tokens. We define a class of fluid stochastic Petri nets in such a way that the discrete and continuous portions may affect each other. Following this definition we provide equations for their transient and steady-state behavior. We present several examples showing the utility of the construct in communication network modeling and reliability analysis, and discuss important special cases. We then discuss numerical methods for computing the transient behavior of such nets. Finally, some numerical examples are presented.

Horton, Graham↗

NASA NTRS1997

Improvements for Image Compression Using Adaptive Principal Component Extraction (APEX)

The issues of image compression and pattern classification have been a primary focus of researchers among a variety of fields including signal and image processing, pattern recognition, data classification, etc. These issues depend on finding an efficient representation of the source data. In this paper we collate our earlier results where we introduced the application of the. Hilbe.rt scan to a principal component algorithm (PCA) with Adaptive Principal Component Extraction (APEX) neural network model. We apply these technique to medical imaging, particularly image representation and compression. We apply the Hilbert scan to the APEX algorithm to improve results

Ziyad, Nigel A.↗

NASA NTRS2001

Nonlinear Stability and Response of Lifting Surfaces Via Volterra Series

This investigation concerns the time and frequency formulations of non-linear two-dimensional lifting surfaces exposed to an incompressible flow field and subjected to an external pressure pulse. In order to address this problem, Volterra series approach in conjunction with the multidimensional Laplace transform is used. This methodology enabling one to solve the aeroelastic governing equations of lifting surfaces opens the way to connect this methodology with that based on neural networks and NARMAX/NARX networks models. Moreover, this extended way to address this problem constitutes a good basis for treatment of the theory of 3D lifting surfaces.

Marzocca, Piergiovanni↗

NASA NTRS2001

Earth Science System of the Future: Observing, Processing, and Delivering Data Products Directly to Users

Advancement of our predictive capabilities will require new scientific knowledge, improvement of our modeling capabilities, and new observation strategies to generate the complex data sets needed by coupled modeling networks. New observation strategies must support remote sensing from a variety of vantage points and will include "sensorwebs" of small satellites in low Earth orbit, large aperture sensors in Geostationary orbits, and sentinel satellites at L1 and L2 to provide day/night views of the entire globe. Onboard data processing and high speed computing and communications will enable near real-time tailoring and delivery of information products (i.e., predictions) directly to users.

Crisp, David↗

NASA NTRS2000

Heat Transfer in a Complex Trailing Edge Passage for a High Pressure Turbine Blade - Part 1: Experimental Measurements: Experimental Measurements - Part 1

A combined experimental and computational study has been performed to investigate the detailed heat transfer coefficient distributions within a complex blade trailing edge passage. The experimental measurements are made using a steady liquid crystal thermography technique applied to one major side of the passage. The geometry of the trailing edge passage is that of a two-pass serpentine circuit with a sharp 180-degree turning region at the tip. The upflow channel is split by interrupted ribs into two major subchannels, one of which is turbulated. This channel has an average aspect ratio of roughly 14:1. The spanwise extent of the channel geometry includes both area convergence from root to tip, as well as taper towards the trailing edge apex. The average section Reynolds numbers tested in this upflow channel range from 55,000 to 98,000. The tip section contains a turning vane near the extreme comer. The downflow channel has an aspect ratio of about 5:1, and also includes convergence and taper. Turbulators of varying sizes are included in this channel also. Both detailed heat transfer and pressure distribution measurements are presented. The pressure measurements are incorporated into a flow network model illustrating the major loss contributors.

Bunker, Ronald S.↗

NASA NTRS2001

A Closed-Loop Optimal Neural-Network Controller to Optimize Rotorcraft Aeromechanical Behaviour: Theory and Methodology - Volume 1

Given the predicted growth in air transportation, the potential exists for significant market niches for rotary wing subsonic vehicles. Technological advances which optimise rotorcraft aeromechanical behaviour can contribute significantly to both their commercial and military development, acceptance, and sales. Examples of the optimisation of rotorcraft aeromechanical behaviour which are of interest include the minimisation of vibration and/or loads. The reduction of rotorcraft vibration and loads is an important means to extend the useful life of the vehicle and to improve its ride quality. Although vibration reduction can be accomplished by using passive dampers and/or tuned masses, active closed-loop control has the potential to reduce vibration and loads throughout a.wider flight regime whilst requiring less additional weight to the aircraft man that obtained by using passive methads. It is ernphasised that the analysis described herein is applicable to all those rotorcraft aeromechanical behaviour optimisation problems for which the relationship between the harmonic control vector and the measurement vector can be adequately described by a neural-network model.

Leyland, Jane Anne↗

NASA NTRS2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells. Copyright 2000 Academic Press.

Review↗

NASA NTRS2006

Classification and Prediction of RF Coupling inside A-320 and A-319 Airplanes using Feed Forward Neural Networks

Neural Network Modeling is introduced in this paper to classify and predict Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data and a plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Jafri, Madiha↗

NASA NTRS2008

Using Piezoelectric Devices to Transmit Power through Walls

A method denoted wireless acoustic-electric feed-through (WAEF) has been conceived for transmitting power and/or data signals through walls or other solid objects made of a variety of elastic materials that could be electrically conductive or nonconductive. WAEF would make it unnecessary to use wires, optical fibers, tubes, or other discrete wall-penetrating signal-transmitting components, thereby eliminating the potential for structural weakening or leakage at such penetrations. Avoidance of such penetrations could be essential in some applications in which maintenance of pressure, vacuum, or chemical or biological isolation is required. In a basic WAEF setup, a transmitting piezoelectric transducer on one side of a wall would be driven at resonance to excite ultrasonic vibrations in the wall. A receiving piezoelectric transducer on the opposite side of the wall would convert the vibrations back to an ultrasonic AC electric signal, which would then be detected and otherwise processed in a manner that would depend on the modulation (if any) applied to the signal and whether the signal was used to transmit power, data, or both. An electromechanical-network model has been derived as a computationally efficient means of analyzing and designing a WAEF system. This model is a variant of a prior model, known in the piezoelectric-transducer art as Mason's equivalent-circuit model, in which the electrical and mechanical dynamics, including electromechanical couplings, are expressed as electrical circuit elements that can include inductors, capacitors, and lumped-parameter complex impedances. The real parts of the complex impedances are used to account for dielectric, mechanical, and coupling losses in all components (including all piezoelectric-transducer, wall, and intermediate material layers). In an application to a three-layer piezoelectric structure, this model was shown to yield the same results as do solutions of the wave equations of piezoelectricity and acoustic propagation in their full complexity.

Sherrit, Stewart↗

NASA NTRS2010

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.

Hanold, Gregg T.↗

NASA NTRS2009

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Ricks, Brian W.↗