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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The network management expert system prototype for Sun Workstations

Networking has become one of the fastest growing areas in the computer industry. The emergence of distributed workstations make networking more popular because they need to have connectivity between themselves as well as with other computer systems to share information and system resources. Making the networks more efficient and expandable by selecting network services and devices that fit to one's need is vital to achieve reliability and fast throughput. Networks are dynamically changing and growing at a rate that outpaces the available human resources. Therefore, there is a need to multiply the expertise rapidly rather than employing more network managers. In addition, setting up and maintaining networks by following the manuals can be tedious and cumbersome even for an experienced network manager. This prototype expert system was developed to experiment on Sun Workstations to assist system and network managers in selecting and configurating network services.

Leigh, Albert↗
NASA NTRS2001

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Niewoehner, Kevin R.↗
NASA NTRS1991

An unpredictable-dynamics approach to neural intelligence

The theoretical basis for a dynamic neural network architecture that takes advantage of the notion of terminal chaos to process information in a way that is phenomenologically similar to brain activity is presented. The architecture exploits the phenomenology of nonlinear dynamic systems as an alternative to the traditional paradigm of finite-state machines. It is based on some effects of non-Lipschitzian dynamics. The nonlinear phenomenon of terminal chaos and its relevance to brain activity are examined.

Zak, Michail↗
NASA NTRS1995

Flight dynamics software in a distributed network environment

As with all NASA facilities, the announcement of reduced budgets, reduced staffing, and the desire to implement smaller/quicker/cheaper missions has required the Agency's organizations to become more efficient in what they do. To accomplish these objectives, the FDD has initiated the development of the Flight Dynamics Distributed System (FDDS). The underlying philosophy of FDDS is to build an integrated system that breaks down the traditional barriers of attitude, mission planning, and navigation support software to provide a uniform approach to flight dynamics applications. Through the application of open systems concepts and state-of-the-art technologies, including object-oriented specification concepts, object-oriented software, and common user interface, communications, data management, and executive services, the FDD will reengineer most of its six million lines of code.

Jeletic, J.↗
NASA NTRS2009

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

Bhaduri, Kanishka↗
NASA NTRS1989

A design philosophy for multi-layer neural networks with applications to robot control

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Vadiee, Nader↗
NASA NTRS1997

Root cytoskeleton: its role in perception of and response to gravity

We have critically evaluated the possible functions of the plant cytoskeleton in root gravisensing and graviresponse and discussed the evidence that microtubules (MTs) and actin microfilaments (MFs) do not control differential cell growth during bending of roots. On the other hand, MF and MT networks are envisaged to participate in gravisensing because of the mechanical properties of the cytoskeletal structures that interconnect plant cell organelles with the plasma membrane. In restrained gravisensing, forces are suggested to be transmitted to membranes because large-scale gravity-dependent repositioning of organelles is effectively prevented due to the cytoskeleton-mediated anchorage of their envelopes at the plasma membrane. From the cytoskeletal point of view, we can also envisage an unrestrained gravity sensing when cytoskeletal tethers are not strong enough to preserve the tight control over distribution of organelles and the latter, if heavy enough, are allowed to sediment towards the physical bottom of cells. This situation obviously occurs in root cap statocytes because these uniquely organized cells are depleted of prominent actin MF bundles, endoplasmic MT arrays, and ER elements in their internal cytoplasm. Nevertheless, indirect evidence clearly indicates that sedimented root cap statoliths are enmeshed within fine but dynamic MF networks and that their behaviour is obviously under, at least partial, cytoskeletal control. The actomyosin-enriched domain among and around amyloplasts is proposed to increase the perception of gravity due to the grouping effect of sedimenting statoliths. Cytoskeletal links between myosin-rich statoliths, and cell peripheries well equipped with dense cortical MTs, membrane-associated cytoskeleton, as well as with ER elements, would allow efficient restrained gravisensing only at the statocyte cell cortex. As a consequence of cytoskeletal depletion in the internal statocyte cytoplasm and bulk sedimentation of large amyloplasts, restrained gravisensing is spatially restricted to the bottom of the statocyte irrespective of whether roots are vertical or horizontal. This spatial aspect allows for efficient gravisensing via amplification of gravity-induced impacts on the cellular architecture, a phenomenon which is unique to root cap statocytes.

NASA Discipline Plant Biology↗
NASA NTRS1987

Channel access schemes and fiber optic configurations for integrated-services local area networks

Local Area Networks are in common use for data communications and have enjoyed great success. Recently, there is a growing interest in using a single network to support many applications in addition to traditional data traffic. These additional applications introduce new requirements in terms of volume of traffic and real-time delivery of data which are not met by existing networks. To satisfy these requirements, a high-bandwidth tranmission medium, such as fiber optics, and a distributed channel access scheme for the efficient sharing of the bandwidth among the various applications are needed. As far as the throughput-delay requirements of the various application are concerned, a network structure along with a distributed channel access are proposed which incorporate appropriate scheduling policies for the transmission of outstanding messages on the network. A dynamic scheduling policy was devised which outperforms all existing policies in terms of minimizing the expected cost per message. A broadcast mechanism was devised for the efficient dissemination of all relevant information. Fiber optic technology is considered for the high-bandwidth transmisison medium.

Nassehi, M. Mehdi↗
NASA NTRS1988

Implementing neural nets with programmable logic

Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

Vidal, Jacques J.↗
NASA NTRS1990

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Rodriguez, Guillermo↗
NASA NTRS1991

Neural networks modeling and control of dynamical systems

The topics are presented in viewgraph form and include: an overview of research; first year research objectives (starting 5/91); background; current work; accomplishments from 5/91-9/91; and second year research objectives.

Donald I Soloway↗
NASA NTRS1992

Wind Measurements from 15 to 50 Km with a Doppler Rayleigh Lidar

The possibility to measure the mean wind in the high stratosphere using a Doppler Rayleigh lidar was demonstrated in 1989, and the Observatory of Haute Province (44 deg N, 6 deg E). It was originally developed to cover the height range 25-60 km, a region where the radars cannot operate; therefore, the system was designed to cover altitudes where the signal is only due to the Rayleigh backscattering. The instrument is currently set up at the Center d'Essais des Landes (44 deg N, 1 deg W) where it operated during the Dynamic Adapted Network for the Atmosphere Campaign in 1990. As the contribution of the Mie scattering was very low during this period above southern France, we have obtained vertical wind profiles in the stratosphere down to 15 km. Since the eruption of the Pinatubo volcano in Jun. 1991, the contribution of Mie scattering has increased between 15 and 30 km and it was very difficult with the original system to obtain wind measurements below 30 km. We will show that by using the same method with slightly different characteristics of the system, it is possible to measure the wind in the presence of Mie scattering. The first experimental results are presented.

Garnier, A.↗
NASA NTRS1992

Wind-induced dynamics of the deep space network antennas

Accurate spacecraft tracking by the NASA Deep Space Network (DSN) antennas must be assured during changing weather conditions. Wind disturbances are the main source of tracking errors. This paper presents the development of a wind force model, and simulations of wind-induced pointing errors of DSN antennas. The antenna model includes the antenna structure, the elevation and azimuth servos, and the tracking controller. Simulation results show that pointing errors due to wind gusts are of the same order as due to wind static pressure, and that (similarly to the static wind pressure) these errors satisfy the velocity quadratic law. The methodology presented is used for wind disturbance estimation, and for design of an antenna controller with wind disturbance rejection properties.

Gawronski, W.↗
NASA NTRS1992

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Hall, Lawrence O.↗
NASA NTRS1992

Neural network based adaptive control of nonlinear plants using random search optimization algorithms

This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

Boussalis, Dhemetrios↗
NASA NTRS1994

Intercomparison of density and temperature profiles obtained by lidar, ionizatoin gauges, falling spheres, datasondes and radiosondes during the DYANA campaign

During the course of the DYnamics Adapted Network for the Atmosphere (DYANA) campaign in early 1990, various techniques to measure densities and temperatures from the ground up to the lower thermosphere were employed. Some of these measurements were performed near simultaneously (maximum allowed time difference: 1 h) and at the same location, and therefore offered the unique chance of intercomparison of different techniques. In this study, we will report on intercomparisons of data from ground-based instruments (Rayleigh- and sodium-lidar), balloon-borne methods (datasondes and radiosondes) and rocket-borne techniques (falling spheres and ionization gauges). The main result is that there is good agreement between the various measurements when considering the error bars. Only occasionally did we notice small but systematic differences (e.g. for the datasondes above 65 km). The most extensive intercomparison was possible between the Rayleigh lidar and the falling sphere technique, both employed in Biscarrosse (44 deg N, 1 deg W). Concerning densities, excellent agreement was found below 63 km: the mean of the deviations is less than 1% and the root mean square (RMS) is approximately 3%. Systematic differences of the order of 5% were noticed around 67 km and above 80 km. The former can be accounted for by an instrumental effect of the falling sphere (Ma = 1 transition; Ma = Mach number), whereas the latter is tentatively explained by the presence of Mie scatterers in the upper mesosphere. Concerning temperatures, the agreement is excellent between 35 and 65 km: the mean of the deviations is less than +/- 3 K and the variability is +/- 5 K. The two systematic density differences mentioned above also affect the temperatures: between 65 and 80 km, the Rayleigh lidar temperatures are systematically lower than the falling sphere values by approximately 5 K.

Lubken, F.-J.↗
NASA NTRS1994

Intercomparisons of sumultaneous remote and in situ wind measurements

A large number of ground based, balloon and rocket borne experiments was performed at various stations during DYnamics Adapted Network for the Atmosphere (DYANA). This allows the comparisons of simultaneous wind profiles determined by different techniques. This paper briefly describes each technique and discusses the comparisons between: (1) foil chaff at Andoya (69 deg N, 16 deg E) and EISCAT winds data at Tromso (70 deg N, 19 deg E); (2) foil chaff or falling sphere at Andoya and MF radar winds data at Tromso; (3) MF radar at Juliusruh (54 deg N, 13 deg E), meteorological radar at Kuehlungsborn (54 deg N, 11 deg E), meteorological rockets at Zingst (54 deg N, 12.5 deg E) and LF drift winds at Collm (51.3 deg N, 13 deg E); (4) falling sphere, balloons and, for the first time, a Rayleigh Doppler Lidar at the Centre d'Essais des Landes (C.E.L. 44 deg N, 1 deg W). These methods have widely varying altitude, spatial and temporal resolutions. Despite these differences, the comparisons show a generally good agreement.

Gonzales, N.↗
NASA NTRS2005

Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

Lerner, Uri↗