Results for “complex network” — Page 49

NASA NTRS1987

Integration of symbolic and algorithmic hardware and software for the automation of space station subsystems

Expert systems that require access to data bases, complex simulations and real time instrumentation have both symbolic as well as algorithmic computing needs. These needs could both be met using a general computing workstation running both symbolic and algorithmic code, or separate, specialized computers networked together. The later approach was chosen to implement TEXSYS, the thermal expert system, developed to demonstrate the ability of an expert system to autonomously control the thermal control system of the space station. TEXSYS has been implemented on a Symbolics workstation, and will be linked to a microVAX computer that will control a thermal test bed. Integration options are explored and several possible solutions are presented.

Gregg, Hugh↗

NASA NTRS1988

Development of an integrated set of research facilities for the support of research flight test

The Ames-Dryden Flight Research Facility (DFRF) serves as the site for high-risk flight research on many one-of-a-kind test vehicles like the X-29A advanced technology demonstrator, F-16 advanced fighter technology integration (AFTI), AFTI F-111 mission adaptive wing, and F-18 high-alpha research vehicle (HARV). Ames-Dryden is on a section of the historic Muroc Range. The facility is oriented toward the testing of high-performance aircraft, as shown by its part in the development of the X-series aircraft. Given the cost of research flight tests and the complexity of today's systems-driven aircraft, an integrated set of ground support experimental facilities is a necessity. In support of the research flight test of highly advanced test beds, the DFRF is developing a network of facilities to expedite the acquisition and distribution of flight research data to the researcher. The network consists of an array of experimental ground-based facilities and systems as nodes and the necessary telecommunications paths to pass research data and information between these facilities. This paper presents the status of the current network, an overview of current developments, and a prospectus on future major enhancements.

Moore, Archie L.↗

NASA NTRS1988

Development of an integrated set of research facilities for the support of research flight test

The Ames-Dryden Flight Research Facility (DFRF) serves as the site for the conduct of high-risk flight research on many one-of-a-kind test vehicles like the X-29A advanced technology demonstrator, F-16 advanced fighter technology integration (AFTI), AFTI F-111 mission adaptive wing, and F-18 high-alpha research vehicle (HARV). Ames-Dryden is on a section of the historic Muroc Range. The facility is oriented toward the testing of high-performance aircraft, as shown by its part in the development of the X-series aircraft. Given the cost of research flight test and the complexity of today's systems-driven aircraft, an integrated set of ground support experimental facilities is a necessity. In support of the research flight test of highly advanced test beds, the DFRF is developing a network of facilities to expedite the acquisition and distribution of flight research data to the researcher. This network consists of an array of experimental ground-based facilities and systems as nodes and the necessary telecommunications paths to pass research data and information between these facilities. This paper presents a status of the current network, an overview of current developments, and a prospectus on future major enhancements.

Moore, Archie L.↗

NASA NTRS1990

A real time neural net estimator of fatigue life

A neural net architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the Intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopump blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines. This neural net estimator of fatigue life is seen as the enabling technology to achieve component life prognosis, and therefore would be an important part of life extending control for reusable rocket engines.

Troudet, T.↗

NASA NTRS1990

Computer aided mechanogenesis of skeletal muscle organs from single cells in vitro

Complex mechanical forces generated in the growing embryo play an important role in organogenesis. Computerized application of similar forces to differentiating skeletal muscle myoblasts in vitro generate three dimensional artificial muscle organs. These organs contain parallel networks of long unbranched myofibers organized into fascicle-like structures. Tendon development is initiated and the muscles are capable of performing directed, functional work. Kinetically engineered organs provide a new method for studying the growth and development of normal and diseased skeletal muscle.

Vandenburgh, Herman H.↗

NASA NTRS1990

Performance analysis of Integrated Communication and Control System networks

This paper presents statistical analysis of delays in Integrated Communication and Control System (ICCS) networks that are based on asynchronous time-division multiplexing. The models are obtained in closed form for analyzing control systems with randomly varying delays. The results of this research are applicable to ICCS design for complex dynamical processes like advanced aircraft and spacecraft, autonomous manufacturing plants, and chemical and processing plants.

Halevi, Y.↗

NASA NTRS1991

Testing interconnected VLSI circuits in the Big Viterbi Decoder

The Big Viterbi Decoder (BVD) is a powerful error-correcting hardware device for the Deep Space Network (DSN), in support of the Galileo and Comet Rendezvous Asteroid Flyby (CRAF)/Cassini Missions. Recently, a prototype was completed and run successfully at 400,000 or more decoded bits per second. This prototype is a complex digital system whose core arithmetic unit consists of 256 identical very large scale integration (VLSI) gate-array chips, 16 on each of 16 identical boards which are connected through a 28-layer, printed-circuit backplane using 4416 wires. Special techniques were developed for debugging, testing, and locating faults inside individual chips, on boards, and within the entire decoder. The methods are based upon hierarchical structure in the decoder, and require that chips or boards be wired themselves as Viterbi decoders. The basic procedure consists of sending a small set of known, very noisy channel symbols through a decoder, and matching observables against values computed by a software simulation. Also, tests were devised for finding open and short-circuited wires which connect VLSI chips on the boards and through the backplane.

Onyszchuk, I. M.↗

NASA NTRS1991

A theoretical analysis of the electromagnetic environment of the AS330 super Puma helicopter external and internal coupling

Numerical techniques such as Finite Difference Time Domain (FDTD) computer programs, which were first developed to analyze the external electromagnetic environment of an aircraft during a wave illumination, a lightning event, or any kind of current injection, are now very powerful investigative tools. The program called GORFF-VE, was extended to compute the inner electromagnetic fields that are generated by the penetration of the outer fields through large apertures made in the all metallic body. Then, the internal fields can drive the electrical response of a cable network. The coupling between the inside and the outside of the helicopter is implemented using Huygen's principle. Moreover, the spectacular increase of computer resources, as calculations speed and memory capacity, allows the modellization structures as complex as these of helicopters with accuracy. This numerical model was exploited, first, to analyze the electromagnetic environment of an in-flight helicopter for several injection configurations, and second, to design a coaxial return path to simulate the lightning aircraft interaction with a strong current injection. The E field and current mappings are the result of these calculations.

Flourens, F.↗

NASA NTRS1992

Analysis and testing of propellant feed system priming process

This paper presents the analytical and the experimental results pertaining to the priming process of the propellant feed system with initial line pressures starting from 0 psia and greater. The analytical methods employ the method of characteristics to solve for the one-dimensional liquid transients in the liquid-full segments and the lumped inertia technique to model the dynamics of the partially filled segments or the two-phase segments. The advantages of these methods are (1) fluid compressibility and piping flexibility are accounted for in the solution; and (2) the characteristics method can be used in the solution of a complex system. The analytical results were compared with test results. Excellent correlation was obtained between predictions and test results verifying the essential aspects of the analytical modeling techniques and providing the foundation for analyzing a complicated network system.

Lin, T. Y.↗

NASA NTRS1991

Computer-aided mechanogenesis of skeletal muscle organs from single cells in vitro

Complex mechanical forces generated in the growing embryo play an important role in organogenesis. Computerized application of similar forces to differentiating skeletal muscle myoblasts in vitro generate three dimensional artificial muscle organs. These organs contain parallel networks of long unbranched myofibers organized into fascicle-like structures. Tendon development is initiated and the muscles are capable of performing directed, functional work. Kinetically engineered organs provide a new method for studying the growth and development of normal and diseased skeletal muscle.

Vanderburgh, Herman H.↗

NASA NTRS1998

Neural Network Control of a Magnetically Suspended Rotor System

Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

Choi, Benjamin B.↗

NASA NTRS2006

A Framework for Managing Inter-Site Storage Area Networks using Grid Technologies

The NASA Goddard Space Flight Center and the University of Maryland Institute for Advanced Computer Studies are studying mechanisms for installing and managing Storage Area Networks (SANs) that span multiple independent collaborating institutions using Storage Area Network Routers (SAN Routers). We present a framework for managing inter-site distributed SANs that uses Grid Technologies to balance the competing needs to control local resources, share information, delegate administrative access, and manage the complex trust relationships between the participating sites.

Kobler, Ben↗

NASA NTRS2006

A Framework for Managing Inter-Site Storage Area Networks using Grid Technologies

The NASA Goddard Space Flight Center and the University of Maryland Institute for Advanced Computer Studies are studying mechanisms for installing and managing Storage Area Networks (SANs) that span multiple independent collaborating institutions using Storage Area Network Routers (SAN Routers). We present a framework for managing inter-site distributed SANs that uses Grid Technologies to balance the competing needs to control local resources, share information, delegate administrative access, and manage the complex trust relationships between the participating sites.

McCall, Fritz↗

NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle S.↗

NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle↗

NASA NTRS1995

Performance modeling & simulation of complex systems (A systems engineering design & analysis approach)

Modeling of the Multi-mission Image Processing System (MIPS) will be described as an example of the use of a modeling tool to design a distributed system that supports multiple application scenarios. This paper examines: (a) modeling tool selection, capabilities, and operation (namely NETWORK 2.5 by CACl), (b) pointers for building or constructing a model and how the MIPS model was developed, (c) the importance of benchmarking or testing the performance of equipment/subsystems being considered for incorporation the design/architecture, (d) the essential step of model validation and/or calibration using the benchmark results, (e) sample simulation results from the MIPS model, and (f) how modeling and simulation analysis affected the MIPS design process by having a supportive and informative impact.

Hall, Laverne↗

NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗

NASA NTRS2012

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Deep Space Network↗