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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps

The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.

Chandler, William S.↗
NASA NTRS2012

Behavioral Mapless Navigation Using Rings

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot.

Monroe, Randall P.↗
NASA NTRS2021

Delay/Disruption Tolerant Networking

Communicating from Earth to any spacecraft is a complex challenge, largely due to the extreme distances involved. When data are transmitted and received across thousands and even millions of miles, the delay and potential for disruption or data loss is significant. Delay/Disruption Tolerant Networking (DTN) is NASA’s solution to reliable internet working for space missions.

delay tolerant networking↗
NASA NTRS2018

Use of Nack Oriented Reliable Multicast (NORM) Protocol for Transport of Spacecraft Telemetry in Ground Networks

Frequently it is necessary to distribute spacecraft telemetry to multiple destinations on Internet Protocol (IP) networks. There are various methods of delivering this data, but when the data is high rate and there are many destinations IP multicast provides the best solution from a network perspective. However, IP multicast only supports the User Datagram Protocol (UDP) which is connectionless and unreliable. This nature of UDP and by extension the low priority treatment of UDP packets by some network hardware creates a difficult environment for distribution of high rate telemetry to a number of recipients over IP networks. NACK (Negative ACKnowledgment) Oriented Reliable Multicast (NORM) is defined by IETF (Internet Engineering Task Force) RFC (Request for Comments) 5740 in 2009 by B. Adamson et al. NORM provides a mechanism of retransmission and or forward error correction for UDP packets, potentially overcoming the limits of UDP for the transmission of spacecraft telemetry. Marshall Space Flight Center’s (MSFC) Huntsville Operations Support Center (HOSC) manages the delivery of the International Space Station (ISS) payload science and health data. The ISS IP Ground Routed (IIGoR) network provides the delivery of the data from the ground stations to the control centers. HOSC personnel have been experimenting with the use of NORM on the link between the ground stations and the front end processor equipment in Huntsville, AL.

Baldwin, Patrick A.↗
NASA NTRS1989

A summary of precise orbit computation for the Geosat Exact Repeat Mission

Recent efforts to recompute orbits for the Geosat Exact Repeat Mission have resulted in a substantial reduction in the satellite's orbit error. The improved orbits are based on Doppler tracking and on the new earth gravity models being developed at the Goddard Space Flight Center in support of requirements for the TOPEX/Poseidon mission. The first set of orbit solutions based on the Goddard Earth Model (GEM T1) gravity model and tracking data from the U.S. Navy's Operational Network system are accurate to about 85 cm root mean square (rms) in height. Preliminary tests of orbits computed with the GEM-T2 model, along with tracking data from an augmented network consisting of the Navy's Operational Network and selected Tracking Network sites, indicate that radial accuracies of 35 cm rms can be achieved.

Haines, Bruce J.↗
NASA NTRS1991

Neural network based expert system for compressor stall monitoring

This research is designed to apply a new information processing technology, artificial neural networks, to monitoring compressor stall. The outputs of neural networks support the dynamic knowledge data base of an expert system. This is the open-loop mode to avoid compressor stall. The integration of a control system with neural networks is the closed-loop mode in stall avoidance. The feasibility of the concept has been demonstrated for the compressor of 16-foot transonic/supersonic propulsion wind tunnels. The construction of a prototpye expert system has been initiated.

Lo, Ching F.↗
NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗
NASA NTRS2000

The Evolution of Networks in Extreme and Isolated Environment

This article reports on the evolution of network structure as it relates to the formal and informal aspects of social roles in well bounded, isolated groups. Research was conducted at the Amundsen-Scott South Pole Station over a 3-year period. Data was collected on crewmembers' networks of social interaction and personal advice over each of the 8.5-month winters during a time of complete isolation. In addition, data was collected on informal social role structure (e.g., instrumental leadership, expressive leadership). It was hypothesized that development and maintenance of a cohesive group structure was related to the presence of and group consensus on various informal social roles. The study found that core-periphery structures (i.e., reflecting cohesion) in winter-over groups were associated with the presence of critically important informal social roles (e.g., expressive leadership) and high group consensus on such informal roles. On the other hand, the evolution of clique structures (i.e., lack of cohesion) were associated with the absence of critical roles and a lack of consensus on these roles, particularly the critically important role of instrumental leader.

Johnson, Jeffrey C.↗
NASA NTRS2008

Mars Interoperability 2008-2015: Options for Relay Orbiter Support to Mars Bound Assets

The current relay orbiter infrastructure at Mars, in support of the user assets presently at Mars and those scheduled for the 2008 to 2015 time frame, only have a need to store-and-forward the returned data collected by an asset to Earth as a single non-prioritized data file. In the forward direction, the relay orbiters are only required to relay the forward link data, i.e., command sequences, software and configuration information assembled on the ground, to the Asset. There are currently no requirements for status/control messages or files to be sent in the return direction from an Asset to an application located on the Relay, nor are there requirements in the forward direction to send status/control messages or files originating on a Relay to an Asset. In addition there are currently no networking requirements at Mars where data originally sent by an Asset is to be delivered to another user asset. However, we foresee the day when standard services for 1) on-board file prioritization of user asset data by a Relay, 2) control/status message transfer between assets and a Relay, and 3) networking between assets i.e., Asset to Asset message/file transfer via a Relay will be required. Each of these services will require the Relay to be capable of understanding the data type and routing needs of the user asset data and be capable of processing it for these purposes. This paper will focus on the innovative enhancements required to the existing communications infrastructure at Mars to enable these future services.

File Transfer↗
NASA NTRS2001

Increasing Density and Reducing Costs of Data Acquisition

There are a number of reasons why it is important to increase the density of data acquisition functions. Sensor fusion seeks to integrate large numbers of sensors into a decision network. Addition of health monitoring functions may incur additional sensor requirements. But at the same time, it is important to reduce the per-channel costs of data acquisition systems. Often the most significant cost is the management of data acquisition networks, which incurs substantial costs associated with transducer installation, configuration, calibration, and maintenance. Alternatives that lower the cost of the transducer system and reduce the data acquisition system channel count will directly impact initial system costs. Other techniques that affect maintenance and operating costs will contribute to reducing life cycle costs. This paper describes work undertaken to explore alternative architectures for lowering the cost per transducer function using a MEMS-based accelerometer as the model.

Schmalzel, J. L.↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS2004

A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators

The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.

Patnaik, Surya N.↗
NASA NTRS1994

Telecommuting (Work-At-Home) at NASA Lewis Research Center

This report presents a study in evaluating the viability of providing a work-at-home (telecommuting) program for Lewis Research Center's corporate employees using Integrated Services Digital Network (ISDN). Case studies have been presented for a range of applications from casual data access to interactive access. The network performance of telemedia applications were studied against future requirements for such level of remote connectivity. Many of the popular ISDN devices were characterized for network and service functionality. A set of recommendations to develop a telecommuting policy have been proposed.

Srinidhi, Saragur M.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-17940-DND

The NASA/GEWEX SRB (Global Energy and Water Exchanges, Surface Radiation Budget) project produces and archives shortwave and longwave radiation budget flux estimates at the top of the atmosphere and at the Earth's surface. The latest version in the archive, Release 3.0, is available as 3-hourly, 3-hourly-monthly, daily and monthly means continuously over the period from July 1983 to December 2007 on a quasi-equal-area grid system of 44016 grid boxes. SRB Release 4 with further improvements in data quality and higher spatial resolution is being developed. The SRB shortwave/longwave fluxes at the Earth's surface from the algorithm GSW(V3.0)/GLW(V3.1) have been extensively validated against high-quality ground-based observations, in particular observed data from the Baseline Surface Radiation Network (BSRN). Comparisons with nearly 6000 site-months of both shortwave and longwave data from 52 BSRN sites show generally good agreement. In addition, the GEWEX SRB data have also been found to compare favorably with the World Radiation Data Centre (WRDC) data and the Global Energy Balance Archive (GEBA) data. In spite of the fact that the BSRN sites are scattered on all seven continents, the validation of the SRB data over the vast oceans had not been done until recently. In this paper, we present comparisons of the GEWEX-SRB data shortwave/longwave data with observations made on arrays moored in tropical oceans. Specifically, we have data from 21 buoys, or moorings, from Predictions and Research Moored Array in the Atlantic (PIRATA), 14 buoys from the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean, 20 buoys from the Tropical Atmosphere Ocean (TAO) array in the Pacific, and 3 buoys from the Woods Hole Oceanographic Institute (WHOI) (2 in the Pacific and 1 in the Atlantic). The data from these buoys span 12 years from 2000 to 2011, though not necessarily continuously. It is found that except for occasional outliers, the majority of the comparable pairs of data points agree well. Further examination of the outliers is necessary to determine whether they can be attributed to wind-driven tilt and/or aerosol accumulation on the instruments. In order to see how the ocean-based validation compares with land-based validation, we made SRB-BSRN monthly mean shortwave/longwave radiation comparisons in the tropical region only, where all the buoys are moored. The results show that in the case of shortwave radiation, the bias error from the ocean-based validation is somewhat larger than its land-based counterpart, but their RMS errors are not significantly different. In the case of longwave radiation, the ocean-based and land-based validations show nearly identical comparison statistics.

Taiping Zhang↗
NASA NTRS2017

Networking in Interstellar Dimensions: Communicating with TRAPPIST-1

The recent discovery of potentially habitable planets orbiting the TRAPPIST-1 system intensified interest in interstellar exploration. In these challenging mission concepts, communication protocols would need to cope with unprecedented signal propagation delays. In this work, we propose and explore Delay Tolerant Networking (DTN) technologies and analyze in a case study based on the TRAPPIST-1. Results suggests that DTN protocols features could become a valuable means to achieve data delivery in future interstellar networks.

Burleigh, Scott C.↗
NASA NTRSDate not supplied

Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.

Nathan Schimpf↗
NASA NTRS2000

KDD Services at the Goddard Earth Sciences Distributed Active Archive Center

NASA's Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) processes, stores and distributes earth science data from a variety of remote sensing satellites. End users of the data range from instrument scientists to global change and climate researchers to federal agencies and foreign governments. Many of these users apply data mining techniques to large volumes of data (up to 1 TB) received from the GES DAAC. However, rapid advances in processing power are enabling increases in data processing that are outpacing tape drive performance and network capacity. As a result, the proportion of data that can be distributed to users continues to decrease. As mitigation, we are migrating more data mining and mining preparation activities into the data center in order to reduce the data volume that needs to be distributed and to offer the users a more useful and manageable product. This migration of activities faces a number of technical and human-factor challenges. As data reduction and mining algorithms are normally quite specific to the user's research needs, the user's algorithm must be integrated virtually unchanged into the archive environment. Also, the archive itself is busy with everyday data archive and distribution activities and cannot be dedicated to, or even impacted by, the mining activities. Therefore, we schedule KDD 'campaigns' (similar to reprocessing campaigns), during which we schedule a wholesale retrieval of specific data products, offering users the opportunity to extract information from the data being retrieved during the campaign.

Lynnes, Christopher↗