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All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Membership generation using multilayer neural network

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

Kim, Jaeseok↗
NASA NTRS1999

Cross-Sensor Calibration of the GAI Long Range Detection Network

The long range component of the North American Lightning Detection Network has been providing experimental data products since July 1996, offering cloud-to-ground lightning coverage throughout the Atlantic and Western Pacific oceans, as well as south to the Intertropical Convergence Zone. The network experiences a strong decrease in detection efficiency with range, which is also significantly modulated by differential propagation under day, night and terminator-crossing conditions. A climatological comparison of total lightning data observed by the Optical Transient Detector (OTD) and CG lightning observed by the long range network is conducted, with strict quality control and allowance for differential network performance before and after the activation of the Canadian Lightning Detection Network. This yields a first-order geographic estimate of long range network detection efficiency and its spatial variability. Intercomparisons are also performed over the continental US, allowing large scale estimates of the midlatitude climatological IC:CG ratio and its possible dependence on latitude.

Boccippio, Dennis J.↗
NASA NTRS1985

The Structure and Dynamics of an Observed Moist Front

The structure and dynamics of the moist cold front of 25 and 26 April 1979, the third observing day of the Severe Environmental Storms and Mesoscale experiment (SESAME), are investigated through the use of a three-dimensional mesoscale numerical model. This work is one of the first studies in which model results are compared, in a one-to-one manner, with a detailed observational analysis, namely that of Ogura and Portis (1982) as taken from the SESAME observations. In addition, frontogenetical effects, both adiabatic and diabatic, are studied on a vertical cross-section through the front; similarities and differences with the adiabatic analysis of Ogura and Portis are discussed. Many similarities exist between the modeled and analyzed fields, although the analysis tends to have weaker horizontal gradients due to the coarseness of the observational network. Vorticity and convergence near the surface were found to have the same magnitude in both the model solution and the analysis, in contrast to idealized frontogenesis models which predict vorticity to be much larger than convergence.

Orlanski, I.↗
NASA NTRS2002

Neural substrates linking balance control and anxiety

This communication provides an update of our understanding of the neurological bases for the close association between balance control and anxiety. New data suggest that a vestibulo-recipient region of the parabrachial nucleus (PBN) contains cells that respond to body rotation and position relative to gravity. The PBN, with its reciprocal relationships with the extended central amygdaloid nucleus, infralimbic cortex, and hypothalamus, appears to be an important node in a primary network that processes convergent vestibular, somatic, and visceral information processing to mediate avoidance conditioning, anxiety, and conditioned fear responses. Noradrenergic and serotonergic projections to the vestibular nuclei also have parallel connections with anxiety pathways. The coeruleo-vestibular pathway originates in caudal locus coeruleus (LC) and provides regionally specialized noradrenergic input to the vestibular nuclei, which likely mediate effects of alerting and vigilance on the sensitivity of vestibulo-motor circuits. Both serotonergic and nonserotonergic pathways from the dorsal raphe nucleus and the nucleus raphe obscurus also project differentially to the vestibular nuclei, and 5-HT(2A) receptors are expressed in amygdaloid and cortical targets of the PBN. It is proposed that the dorsal raphe nucleus pathway contributes to both (a) a tradeoff between motor and sensory (information gathering) aspects of responses to self-motion and (b) a calibration of the sensitivity of affective responses to aversive aspects of motion. This updated neurologic model continues to be a synthetic schema for investigating the neurological and neurochemical bases for comorbidity of balance disorders and anxiety disorders.

Review↗
NASA NTRS2002

Converging voice and data over mission-critical networks

The U.S. National Aeronautics and Space Administration (NASA) Deep Space Network ?? DSN - is an international network of antennas that supports interplanetary spacecraft missions and radio and radar astronomy observations for the exploration of the solar system and the universe. The network also supports selected Earth-orbiting missions. This paper describes the ground communications network of the DSN and ways network infrastructure costs are being reduced by the introduction of new technology. Fundamentally, the DSN ground network architecture is a star network, and the hub is at JPL in Pasadena, California. Communications to customer sites are designed to minimize NASA costs and may be either shared IP backbone networks or dedicated circuits. One of the primary features of the network is its ability to support real-time data, voice, and video communications among antenna stations, an automated multi-mission operations systems facility at JPL (AMMOS), and mission operations centers (MOCs) at NASA and non-NASA facilities. Funding for advanced engineering to reduce ground network infrastructure costs was provided by the NASA Office of Space Science.

ground communications networks Internet voice VOIP↗
NASA NTRS1990

Electronic neural networks for global optimization

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

Thakoor, A. P.↗
NASA NTRS1987

Short-term forecasting of lightning based on the surface wind field at Kennedy Space Center

Cloud-to-ground lightning is related in time and space to surface convergence for 244 days during the summer over a 790 sqkm network. The method uses surface convergence, particularly the average over the area, to identify the potential for new, local thunderstorm growth, and can be used to specify the likely time and location of lightning during the life cycle of the convection. A threshold of 0.0000075/sec change in divergence is used to define a convergence event, and a separation of 30 min between flashes defines a lightning event. Time intervals are found to be on the order of 1 hr from beginning convergence to first flash, and (CH110) 2 hr from beginning convergence to the end of lightning. Major differences between the convergence-lightning relationships based on low-level mean onshore and offshore flow are noted.

Watson, Andrew I.↗
NASA NTRS1989

Network-Control Algorithm

Algorithm developed for optimal routing of packets of data along links of multilink, multinode digital communication network. Algorithm iterative and converges to cost-optimal assignment independent of initial assignment. Each node connected to other nodes through links, each containing number of two-way channels. Algorithm assigns channels according to message traffic leaving and arriving at each node. Modified to take account of different priorities among packets belonging to different users by using different delay constraints or imposing additional penalties via cost function.

Chan, Hak-Wai↗
NASA NTRS2016

Converging Supergranular Flows and the Formation of Coronal Plumes

Earlier studies have suggested that coronal plumes are energized by magnetic reconnection between unipolar flux concentrations and nearby bipoles, even though magnetograms sometimes show very little minority-polarity flux near the footpoints of plumes. Here we use high-resolution extreme-ultraviolet (EUV) images and magnetograms from the Solar Dynamics Observatory (SDO) to clarify the relationship between plume emission and the underlying photospheric field. We find that plumes form where unipolar network elements inside coronal holes converge to form dense clumps, and fade as the clumps disperse again. The converging flows also carry internetwork fields of both polarities. Although the minority-polarity flux is sometimes barely visible in the magnetograms, the corresponding EUV images almost invariably show loop-like features in the core of the plumes, with the fine structure changing on timescales of minutes or less. We conclude that the SDO observations are consistent with a model in which plume emission originates from interchange reconnection in converging flows, with the plume lifetime being determined by the approximately 1-day evolutionary timescale of the supergranular network. Furthermore, the presence of large EUV bright points and/or ephemeral regions is not a necessary precondition for the formation of plumes, which can be energized even by the weak, mixed-polarity internetwork fields swept up by converging flows.

Sun: corona↗
NASA NTRS2012

Method and apparatus for determining and utilizing a time-expanded decision network

A method, apparatus and computer program for determining and utilizing a time-expanded decision network is presented. A set of potential system configurations is defined. Next, switching costs are quantified to create a "static network" that captures the difficulty of switching among these configurations. A time-expanded decision network is provided by expanding the static network in time, including chance and decision nodes. Minimum cost paths through the network are evaluated under plausible operating scenarios. The set of initial design configurations are iteratively modified to exploit high-leverage switches and the process is repeated to convergence. Time-expanded decision networks are applicable, but not limited to, the design of systems, products, services and contracts.

Silver, Matthew↗
NASA NTRS1992

Sea ice classification using fast learning neural networks

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Dawson, M. S.↗
NASA NTRS2004

Real-Time Adaptive Color Segmentation by Neural Networks

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Duong, Tuan A.↗
NASA NTRS1988

The velocity pattern of weak solar magnetic fields

The proper motion of magnetic elements on the quiet sun has been measured by means of local correlation tracking. The existence of a pattern in the intranetwork (IN) flow is confirmed. This velocity field is consistent with the direct Doppler measurement of the horizontal component of the supergranular velocity field. The IN elements generally move toward the network boundaries. By tracking test points, it is confirmed that the magnetic elements converge in areas corresponding to the magnetic network. But because the IN elements are of random polarity, they cannot contribute to the growth or maintenance of the magnetic network. By calculating the cross correlation between the magnetogram and Dopplergram, it is confirmed that the supergranule boundaries and the magnetic network are roughly correlated.

Wang, H.↗
NASA NTRS1989

Short-term forecasting of thunderstorms at Kennedy Space Center, based on the surface wind field

Techniques incorporating wind convergence that can be used for the short-term prediction of thunderstorm development are described. With these techniques, the convergence signal is sensed by the wind network array 15 to 90 min before actual storm development. Particular attention is given to the convergence cell technique (which has been applied at the Kennedy Space Center) where each convective region is analyzed independently. It is noted that, while the monitoring of areal and cellular convergence can be used to help locate the seeds of developing thunderstorms and pinpoint the lightning threat areas, this forecasting aid cannot be used in isolation.

Watson, Andrew I.↗
NASA NTRS1991

Surface wind convergence as a short-term predictor of cloud-to-ground lightning at Kennedy Space Center

Cloud-to-ground lightning is a significant forecast problem at the Kennedy Space Center (KSC) in Florida. In this study, cloud-to-ground lightning is related in time and space to surface convergence for 244 days during the convective seasons of 1985 and 1986 over a 790 sq-km network at KSC. The method uses surface convergence, particularly the average over the area, to identify the potential for new, local thunderstorm growth, and it can be used to specify the likely time and location of lightning during the life cycle of the convection. A threshold of 75 x 10 to the -6th/s change in divergence is the main criterion used to define a convergence event, and a set of flashes less than 30 min apart defines a lightning event. Time intervals are found from the study to be approximately 1 h from beginning convergence to first flash, and another hour to the end of lightning. The influences of low-level winds and midlevel moisture in determining the location and intensity of convection are discussed. This is the first known dynamically-based forecast method for lightning prediction. The technique, currently in use at KSC, has been shown to be a systematic, quantitative tool for predicting lightning onset in situations where conventional analysis tools such as radar and satellite are limited.

Watson, Andrew I.↗
NASA NTRSDate not supplied

Learning Based Edge Computing in Air-to-Air Communication Network

This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.

Zhe Wang↗