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NASA NTRS1994

NSSDC provides network access to key data via NDADS

The National Space Science Data Center (NSSDC) is making a growing fraction of its most customer-desirable data electronically accessible via both the local and wide area networks. NSSDC is witnessing a great increase in its data dissemination owing to this network accessibility. To provide its customers the best data accessibility, the NSSDC makes data available from a nearline, mass storage system, the NSSDC Data Archive and Dissemination Service (NDADS). The NDADS, the initial version was made available in January 1992, is a customized system of hardware and software that provides users access to the nearline data via ANONYMOUS FTP, an e-mail interface (ARMS), and a C-based software library. In January 1992, the NDADS registered 416 requests for 1,957 files. By December of 1994, NDADS had been populated with 800 gigabytes of electronically accessible data and had registered 1458 requests for 20,887 files. In this report we describe the NDADS system, both hardware and software. Later in the report, we discuss some of the lessons that were learned as a result of operating NDADS, particularly in the area of ingest and dissemination.

Behnke, Jeanne↗

NASA NTRS1994

Application of neural networks to unsteady aerodynamic control

The problem under consideration in this viewgraph presentation is to understand, predict, and control the fluid mechanics of dynamic maneuvers, unsteady boundary layers, and vortex dominated flows. One solution is the application of neural networks demonstrating closed-loop control. Neural networks offer unique opportunities: simplify modeling of three dimensional, vortex dominated, unsteady separated flow fields; are effective means for controlling unsteady aerodynamics; and address integration of sensors, controllers, and time lags into adaptive control systems.

Faller, William E.↗

NASA NTRS1994

Learning to train neural networks for real-world control problems

Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics.

Feldkamp, Lee A.↗

NASA NTRS1994

Neural network applications in telecommunications

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Alspector, Joshua↗

NASA NTRS1995

Reconstruction of an infrared band of meteorological satellite imagery with abductive networks

As the current fleet of meteorological satellites age, the accuracy of the imagery sensed on a spectral channel of the image scanning system is continually and progressively degraded by noise. In time, that data may even become unusable. We describe a novel approach to the reconstruction of the noisy satellite imagery according to empirical functional relationships that tie the spectral channels together. Abductive networks are applied to automatically learn the empirical functional relationships between the data sensed on the other spectral channels to calculate the data that should have been sensed on the corrupted channel. Using imagery unaffected by noise, it is demonstrated that abductive networks correctly predict the noise-free observed data.

Singer, Harvey A.↗

NASA NTRS1995

Parallel CFD design on network-based computer

Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.

Cheung, Samson↗

NASA NTRS1995

Improved Adjoint-Operator Learning For A Neural Network

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Toomarian, Nikzad↗

NASA NTRS1995

Neural Networks Analyze Data In Particle-Impact-Noise Tests

Electronic neural networks and computers put to use in analyzing data acquired in particle-impact-noise-detection (PIND) tests of packaged electronic components. PIND tests detect loose particles in packages that cause failures during subsequent operation of packages in presence of accelerations or other effects - for example, loose electrically conductive particles that bounce into positions in which they cause short circuits. Interpretation of test data more objective and accurate. Preliminary results suggest use of neural networks result in significant improvement in quality and reliability and decrease in cost of PIND testing.

Scaglione, Lois J.↗

NASA NTRS1995

Neural-Network Controller For Vibration Suppression

Neural-network-based adaptive-control system proposed for vibration suppression of flexible space structures. Controller features three-layer neural network and utilizes output feedback. Measurements generated by various sensors on structure. Feed forward path also included to speed up response in case plant exhibits predominantly linear dynamic behavior. System applicable to single-input single-output systems. Work extended to multiple-input multiple-output systems as well.

Boussalis, Dhemetrios↗

NASA NTRS1995

Neural Networks For Demodulation Of Phase-Modulated Signals

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

Altes, Richard A.↗

NASA NTRS1995

Searching for patterns in remote sensing image databases using neural networks

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

Paola, Justin D.↗

NASA NTRS1996

Higher-Order Neural Networks Recognize Patterns

Networks of higher order have enhanced capabilities to distinguish between different two-dimensional patterns and to recognize those patterns. Also enhanced capabilities to "learn" patterns to be recognized: "trained" with far fewer examples and, therefore, in less time than necessary to train comparable first-order neural networks.

Reid, Max B.↗

NASA NTRS1995

Positron annihilation spectroscopic study of high performance semi-interpenetrating network polyimids

Semi-interpenetrating (S-IPN) network polyimids were made from different proportions of LaRC RP46 (a thermosetpolyimid) and LaRC BDTA-ODA (a thermoplastic polyimid). The ultimate goal of this networking is to improve the mechanical properties of the thermoset polyimid. Positron lifetime study was made to calculate lifetime based on second component of the life time spectra and the free volume & microvoid size. All these properties tend to decrease steadily with increasing thermoset content except at the 50 percent thermoset level where these properties show sudden drop. This result contradicts with the initial expectation that the blend properties should change gradually if it were a solid solution of thermoset (TSP) and thermoplastic (TPP) components. Thermal analyses (TMA, DSC, DMA & TGA) were run to complement the positron life time studies. The TMA and DSC studies confirm the contradiction mentioned above. Further experimentation with S-IPN polymers made at TSP/TTP content around 50/50 level are being conducted to explain this anomaly. Scanning electron microscope study of the S-IPN polyimid samples is under way in order to detect morphological differences which might help explain the phenomenon mentioned above.

Ray, Asit K.↗

NASA NTRS1995

Aircraft Emission Inventories Projected in Year 2015 for a High Speed Civil Transport (HSCT) Universal Airline Network

This report describes the development of a three-dimensional database of aircraft fuel burn and emissions (fuel burned, NOx, CO, and hydrocarbons) from projected fleets of high speed civil transports (HSCT's) on a universal airline network.Inventories for 500 and 1000 HSCT fleets, as well as the concurrent subsonic fleets, were calculated. The objective of this work was to evaluate the changes in geographical distribution of the HSCT emissions as the fleet size grew from 500 to 1000 HSCT's. For this work, a new expanded HSCT network was used and flights projected using a market penetration analysis rather than assuming equal penetration as was done in the earlier studies. Emission inventories on this network were calculated for both Mach 2.0 and Mach 2.4 HSCT fleets with NOx cruise emission indices of approximately 5 and 15 grams NOx/kg fuel. These emissions inventories are available for use by atmospheric scientists conducting the Atmospheric Effects of Stratospheric Aircraft (AESA) modeling studies. Fuel burned and emissions of nitrogen oxides (NOx as NO2), carbon monoxide, and hydrocarbons have been calculated on a 1 degree latitude x 1 degree longitude x 1 kilometer attitude grid and delivered to NASA as electronic files.

Baughcum, Steven L.↗

NASA NTRS1996

Sequence-of-events-driven automation of the deep space network

In February 1995, sequence-of-events (SOE)-driven automation technology was demonstrated for a Voyager telemetry downlink track at DSS 13. This demonstration entailed automated generation of an operations procedure (in the form of a temporal dependency network) from project SOE information using artificial intelligence planning technology and automated execution of the temporal dependency network using the link monitor and control operator assistant system. This article describes the overall approach to SOE-driven automation that was demonstrated, identifies gaps in SOE definitions and project profiles that hamper automation, and provides detailed measurements of the knowledge engineering effort required for automation.

Hill, R., Jr.↗

NASA NTRS1995

Traffic sharing algorithms for hybrid mobile networks

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

Arcand, S.↗

NASA NTRS1995

Mobile satellite business networks: A part of the European mobile system

The European Space Agency (ESA) is presently procuring an L-band payload EMS, to be embarked on the ITALSAT-2 satellite due for launch in early 1996, in order to promote a regional European mobile system. One of the Land Mobile Communication systems supported by EMS is the MSBN (Mobile Satellite Business Network) voice and data system which will offer the services of a business network on a seamless European coverage. This paper will first recall the characteristics of the MSBN system, which is based on quasi-synchronized CDMA (Code Division Multiple Access) techniques in both directions, and then describe the CDMA receivers implementation. Main validation test results will also be reported confirming predicted performances.

deMateo, M. L.↗

NASA NTRS1994

A feasibility study for long-path multiple detection using a neural network

Least-squares inverse filters have found widespread use in the deconvolution of seismograms and the removal of multiples. The use of least-squares prediction filters with prediction distances greater than unity leads to the method of predictive deconvolution which can be used for the removal of long path multiples. The predictive technique allows one to control the length of the desired output wavelet by control of the predictive distance, and hence to specify the desired degree of resolution. Events which are periodic within given repetition ranges can be attenuated selectively. The method is thus effective in the suppression of rather complex reverberation patterns. A back propagation(BP) neural network is constructed to perform the detection of first arrivals of the multiples and therefore aid in the more accurate determination of the predictive distance of the multiples. The neural detector is applied to synthetic reflection coefficients and synthetic seismic traces. The processing results show that the neural detector is accurate and should lead to an automated fast method for determining predictive distances across vast amounts of data such as seismic field records. The neural network system used in this study was the NASA Software Technology Branch's NETS system.

Feuerbacher, G. A.↗