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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 1,117 records · Page 62

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NASA NTRS1994

The development of a fiber optics communication network for controlling a Multidegree-Of-Freedom Serpentine Truss

The problem addressed by this report is the large size and heavy weight of the cable bundle, used for controlling a Multidegree-Of-Freedom Serpentine Truss Manipulator arm, which imposes limitations on the manipulator arm maneuverability. This report covers a design of an optical fiber network to replace the existing copper wire network of the Serpentine Truss Manipulator. This report proposes a fiber network design which significantly reduces the bundle size into two phases. The first phase does not require any modifications for the manipulator architecture, while the other requires major modifications. Design philosophy, hardware details and schematic diagrams are presented.

Andrawis, Alfred S.↗

NASA NTRS1994

An expert system for configuring a network for a Milstar terminal

This paper describes a rule-based expert system which assists the user in configuring a network for Air Force terminals using the Milstar satellite system. The network configuration expert system approach uses CLIPS. The complexity of network configuration is discussed, and the methods used to model it are described.

Mahoney, Melissa J.↗

NASA NTRS1994

MIRO: A debugging tool for CLIPS incorporating historical Rete networks

At the last CLIPS conference, we discussed our ideas for adding a temporal dimension to the Rete network used to implement CLIPS. The resulting historical Rete network could then be used to store 'historical' information about a run of a CLIPS program, to aid in debugging. MIRO, a debugging tool for CLIPS built on top of CLIPS, incorporates such a historical Rete network and uses it to support its prototype question-answering capability. By enabling CLIPS users to directly ask debugging-related questions about the history of a program run, we hope to reduce the amount of single-stepping and program tracing required to debug a CLIPS program. In this paper, we briefly describe MIRO's architecture and implementation, and the current question-types that MIRO supports. These question-types are further illustrated using an example, and the benefits of the debugging tool are discussed. We also present empirical results that measure the run-time and partial storage overhead of MIRO, and discuss how MIRO may also be used to study various efficiency aspects of CLIPS programs.

Tuttle, Sharon M.↗

NASA NTRS1994

Method and system for pattern analysis using a coarse-coded neural network

A method and system for performing pattern analysis with a neural network coarse-coding a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of pattern data. The neural network includes a plurality fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system.

Spirkovska, Liljana↗

NASA NTRS1994

Hidden Markov models and neural networks for fault detection in dynamic systems

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Smyth, Padhraic↗

NASA NTRS1994

Neural networks: Application to medical imaging

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Clarke, Laurence P.↗

NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗

NASA NTRS1994

A neural network controller for automated composite manufacturing

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Lichtenwalner, Peter F.↗

NASA NTRS1993

Attitude control of spacecraft using neural networks

This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

Vadali, Srinivas R.↗

NASA NTRS1995

Analyzing rocket plume spectral data with neural networks

The Optical Plume Anomaly Detection (OPAD) system is under development to provide early-warning failure detection in support of ground-level testing of the Space Shuttle Main Engine (SSME). Failure detection is to be achieved through the acquisition of spectrally resolved plume emissions and subsequent identification of abnormal levels indicative of engine corrosion or component failure. Two computer codes (one linear and the other non-linear) are used by the OPAD system to iteratively determine specific element concentrations in the SSME plume, given emission intensity and wavelength information. Since this analysis is extremely labor intensive, a study was initiated to develop neural networks that would model the 'inverse' of these computer codes. Optimally connected feed-forward networks with imperceptible prediction error have been developed for each element modeled by the linear code, SPECTRA4. Radial basis function networks were developed for the non-linear code, SPECTRA5, and predict combustion temperature in addition to element concentrations.

Whitaker, Kevin W.↗

NASA NTRS1995

Pseudogradient Training For A Class Of Neural Networks

Developmental second-order recurrent neural networks of special type modified to enhance stability in face of inputs beyond range of inputs on which trained. Second-order recurrent neural networks contain product feedback units and can be trained, by use of example inputs and outputs, to act as finite-state automatons. Particular second-order recurrent neural networks in question learn grammars in sense they are trained to generate binary responses to input training sequences of ones and zeros, each sequence being marked "legal" or "illegal" according to grammar to be learned.

Zeng, Zheng↗

NASA NTRS1993

Network and user interface for PAT DOME virtual motion environment system

The Device for Orientation and Motion Environments Preflight Adaptation Trainer (DOME PAT) provides astronauts a virtual microgravity sensory environment designed to help alleviate tye symptoms of space motion sickness (SMS). The system consists of four microcomputers networked to provide real time control, and an image generator (IG) driving a wide angle video display inside a dome structure. The spherical display demands distortion correction. The system is currently being modified with a new graphical user interface (GUI) and a new Silicon Graphics IG. This paper will concentrate on the new GUI and the networking scheme. The new GUI eliminates proprietary graphics hardware and software, and instead makes use of standard and low cost PC video (CGA) and off the shelf software (Microsoft's Quick C). Mouse selection for user input is supported. The new Silicon Graphics IG requires an Ethernet interface. The microcomputer known as the Real Time Controller (RTC), which has overall control of the system and is written in Ada, was modified to use the free public domain NCSA Telnet software for Ethernet communications with the Silicon Graphics IG. The RTC also maintains the original ARCNET communications through Novell Netware IPX with the rest of the system. The Telnet TCP/IP protocol was first used for real-time communication, but because of buffering problems the Telnet datagram (UDP) protocol needed to be implemented. Since the Telnet modules are written in C, the Adap pragma 'Interface' was used to interface with the network calls.

Worthington, J. W.↗

NASA NTRS1995

Experiences with the Bay Area Gigabit Network Testbed

The Bay Area Gigabit Network Testbed (BAGNet) is a high-performance ATM (155 Mbps) testbed located within the San Francisco Bay Area in northern California. BAGNet is a metropolitan-area network, spanning an area of approximately 50 square miles. There are fifteen sites participating in the testbed, with up to four hosts per site. Although BAGNet is an applications-oriented testbed, much of our effort has been directed towards getting the testbed running and understanding the factors that impact performance of an ATM network. We present some of our experiences in this paper.

Johnson, Marjory J.↗

NASA NTRS1993

Neural networks analysis on SSME vibration simulation data

The neural networks method is applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME to supplement the statistical method utilized in the prototype system. The investigation of neural networks analysis is conducted using SSME vibration data from a NASA developed numerical simulator. The limited application of neural networks to the HPFTP has also shown the effectiveness in diagnosing the anomalies of turbopump vibrations.

Lo, Ching F.↗

NASA NTRS1995

Pole-equator difference and the variability of the brightness of the chromospheric CaII-K-network elements in quiet regions over the solar cycle

The dependence of the brightness of chromospheric network elements on latitude was investigated for quiet solar regions. Calibrated photographic CaII K-spectroheliograms were used to compare the variation in brightness at the center of the disk with higher latitude of chromospheric network elements in a quiet region as a function of solar activity. It was found that there was no significant difference in brightness between the center of the solar disk and higher latitude. It is concluded that the brightness of the chromospheric network elements in a quiet region does not depend on the latitude, but that the variation in the intensity enhancement is related to the level of solar activity.

Kariyappa, R.↗

NASA NTRS1995

Kennedy Space Center network documentation system

The Kennedy Space Center Network Documentation System (KSC NDS) is being designed and implemented by NASA and the KSC contractor organizations to provide a means of network tracking, configuration, and control. Currently, a variety of host and client platforms are in use as a result of each organization having established its own network documentation system. The solution is to incorporate as many existing 'systems' as possible in the effort to consolidate and standardize KSC-wide documentation.

Lohne, William E.↗

NASA NTRS1996

Algorithm For A Self-Growing Neural Network

CID3 algorithm simulates self-growing neural network. Constructs decision trees equivalent to hidden layers of neural network. Based on ID3 algorithm, which dynamically generates decision tree while minimizing entropy of information. CID3 algorithm generates feedforward neural network by use of either crisp or fuzzy measure of entropy.

Cios, Krzysztof J.↗

NASA NTRS1994

A Feasibility Study for Perioperative Ventricular Tachycardia Prognosis and Detection and Noise Detection Using a Neural Network and Predictive Linear Operators

To locate the accessory pathway(s) in preexicitation syndromes, epicardial and endocardial ventricular mapping is performed during anterograde ventricular activation via accessory pathway(s) from data originally received in signal form. As the number of channels increases, it is pertinent that more automated detection of coherent/incoherent signals is achieved as well as the prediction and prognosis of ventricular tachywardia (VT). Today's computers and computer program algorithms are not good in simple perceptual tasks such as recognizing a pattern or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. Neural net paradigms have proven to be effective at pattern recognition tasks. In signal processing, the picking of coherent/incoherent signals represents a pattern recognition task for computer systems. The picking of signals representing the onset ot VT also represents such a computer task. We attacked this problem by defining four signal attributes for each potential first maximal arrival peak and one signal attribute over the entire signal as input to a back propagation neural network. One attribute was the predicted amplitude value after the maximum amplitude over a data window. Then, by using a set of known (user selected) coherent/incoherent signals, and signals representing the onset of VT, we trained the back propagation network to recognize coherent/incoherent signals, and signals indicating the onset of VT. Since our output scheme involves a true or false decision, and since the output unit computes values between 0 and 1, we used a Fuzzy Arithmetic approach to classify data as coherent/incoherent signals. Furthermore, a Mean-Square Error Analysis was used to determine system stability. The neural net based picking coherent/incoherent signal system achieved high accuracy on picking coherent/incoherent signals on different patients. The system also achieved a high accuracy of picking signals which represent the onset of VT, that is, VT immediately followed these signals. A special binary representation of the input and output data allowed the neural network to train very rapidly as compared to another standard decimal or normalized representations of the data.

Moebes, T. A.↗