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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Berenji, Hamid R.↗
NASA NTRS1974

Research in the Aloha system

The Aloha system was studied and developed and extended to advanced forms of computer communications networks. Theoretical and simulation studies of Aloha type radio channels for use in packet switched communications networks were performed. Improved versions of the Aloha communications techniques and their extensions were tested experimentally. A packet radio repeater suitable for use with the Aloha system operational network was developed. General studies of the organization of multiprocessor systems centered on the development of the BCC 500 computer were concluded.

Abramson, N.↗
NASA NTRS1998

A Flexible System for Simulating Aeronautical Telecommunication Network

At Old Dominion University, we have built Aeronautical Telecommunication Network (ATN) Simulator with NASA being the fund provider. It provides a means to evaluate the impact of modified router scheduling algorithms on the network efficiency, to perform capacity studies on various network topologies and to monitor and study various aspects of ATN through graphical user interface (GUI). In this paper we describe briefly about the proposed ATN model and our abstraction of this model. Later we describe our simulator architecture highlighting some of the design specifications, scheduling algorithms and user interface. At the end, we have provided the results of performance studies on this simulator.

Maly, Kurt↗
NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗
NASA NTRS1978

A model of human decisionmaking in a fault diagnosis task

Utilizing elementary concepts from the theory of fuzzy sets as well as several nonfuzzy heuristics, a model is presented of human decisionmaking in the task of troubleshooting graphically displayed networks. The performance of the model is compared to the results of two previously reported experimental studies. The ability of the model to represent human decisionmaking as a function of network size, forced-pacing, and computer aiding is considered.

Rouse, W. B.↗
NASA NTRS1994

Analysis of space network loading

The NASA Space Network (SN) consists of several geosynchronous communications satellites, in addition to ground support facilities. Space Network management must predict years in advance what network resources are necessary to adequately satisfy all SN users. Similarly, users of the Space Network must know throughout all stages of mission planning and operations to what extent their communication support requirements can be met. NASA, at the Goddard Space Flight Center, performs Space Network and Mission Modeling using The Network Planning and Analysis System (NPAS), to determine the answers to these questions.

Simons, Mark↗
NASA NTRS2000

Implementing Journaling in a Linux Shared Disk File System

In computer systems today, speed and responsiveness is often determined by network and storage subsystem performance. Faster, more scalable networking interfaces like Fibre Channel and Gigabit Ethernet provide the scaffolding from which higher performance computer systems implementations may be constructed, but new thinking is required about how machines interact with network-enabled storage devices. In this paper we describe how we implemented journaling in the Global File System (GFS), a shared-disk, cluster file system for Linux. Our previous three papers on GFS at the Mass Storage Symposium discussed our first three GFS implementations, their performance, and the lessons learned. Our fourth paper describes, appropriately enough, the evolution of GFS version 3 to version 4, which supports journaling and recovery from client failures. In addition, GFS scalability tests extending to 8 machines accessing 8 4-disk enclosures were conducted: these tests showed good scaling. We describe the GFS cluster infrastructure, which is necessary for proper recovery from machine and disk failures in a collection of machines sharing disks using GFS. Finally, we discuss the suitability of Linux for handling the big data requirements of supercomputing centers.

Preslan, Kenneth W.↗
NASA NTRSDate not supplied

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.

CNN↗
NASA NTRS1993

Performance of a distributed superscalar storage server

The RS/6000 performed well in our test environment. The potential exists for the RS/6000 to act as a departmental server for a small number of users, rather than as a high speed archival server. Multiple UniTree Disk Server's utilizing one UniTree Disk Server's utilizing one UniTree Name Server could be developed that would allow for a cost effective archival system. Our performance tests were clearly limited by the network bandwidth. The performance gathered by the LibUnix testing shows that UniTree is capable of exceeding ethernet speeds on an RS/6000 Model 550. The performance of FTP might be significantly faster if asked to perform across a higher bandwidth network. The UniTree Name Server also showed signs of being a potential bottleneck. UniTree sites that would require a high ratio of file creations and deletions to reads and writes would run into this bottleneck. It is possible to improve the UniTree Name Server performance by bypassing the UniTree LibUnix Library altogether and communicating directly with the UniTree Name Server and optimizing creations. Although testing was performed in a less than ideal environment, hopefully the performance statistics stated in this paper will give end-users a realistic idea as to what performance they can expect in this type of setup.

Finestead, Arlan↗
NASA NTRS1992

Beam-waveguide antenna performance predictions with comparisons to experimental results

An overview of a NASA/JPL antenna project is presented, with specific focus on the methodology used to predict the microwave performance of a 34-m-diameter beam-waveguide (BWG) reflector antenna, designated DSS 13 (Deep Space Station 13). DSS 13 is the R&D facility serving the NASA/JPL Deep Space Network. Microwave performance predictions as well as a summary of test results for the antenna are given. The antenna has Cassegrain and centerline BWG operating modes at X-band (8.450-GHz) and Ka-band (32-GHz) frequencies. The performance predictions regarding antenna area efficiencies, corresponding beampeak gains, and for several (but not all) operating noise temperatures are found to agree reasonably well with the corresponding experimental results.

Bathker, Dan A.↗
NASA NTRS2005

Developing a Framework for Effective Network Capacity Planning

As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

Yaprak, Ece↗
NASA NTRSDate not supplied

Environment Adversarial Reinforcement Learning

This paper presents a training method for increasing performance of reinforcement learning agents. The method is named Environment Adversarial Reinforcement Learning. The method requires the reinforcement learning environment to be parameterizeable. Over the course of training, environment parameters are updated in a direction of increasing difficulty for the agent. The direction for these updates is found using a performance prediction network trained on data from tests of the agent under varying environment parameters. The method was tested on a CartPole environment. A 28-58\% improvement in mean return was found when comparing performance to a baseline reinforcement learning algorithm on both easy and hard versions of the task.

machine learning↗
NASA NTRS2003

Power, data latency, and radio frequnecy interference issues in Mars Realy Network scheduling

At different time periods in the future, missions to Mars will overlap. Previous studies indicate that during such periods, existing deep space communication infrastructure cannot handle all Mars communication needs. A plausible solution is to take into account the end-to-end communication performances of network along with operational constraints, and optimize the resource usage by scheduling communication at highest possible data throughputs. As a result, shorter communication time is required and more missions can be accommodated. This principle is demonstrated in this paper for a Mars relay communication network; a network consisting of multiple surface units and orbiters on Mars and the Deep Space Stations.

power latency RFI Mars Relay Network scheduling↗
NASA NTRS1993

Photodetector Array For Neural Network

Integrated circuit performs 1,024 threshold operations in parallel. Designed for use in postprocessing output of optical correlator in optoelectronic neural network. Overall function of device to put out digital signals indicative of location(s) of bright spot(s) on detector plane. Response time less than commercial charge-coupled-device (CCD) imaging detector operating at standard television frame rate.

Langenbacher, Harry↗
NASA NTRS1996

Performance Evaluation of Communication Software Systems for Distributed Computing

In recent years there has been an increasing interest in object-oriented distributed computing since it is better quipped to deal with complex systems while providing extensibility, maintainability, and reusability. At the same time, several new high-speed network technologies have emerged for local and wide area networks. However, the performance of networking software is not improving as fast as the networking hardware and the workstation microprocessors. This paper gives an overview and evaluates the performance of the Common Object Request Broker Architecture (CORBA) standard in a distributed computing environment at NASA Ames Research Center. The environment consists of two testbeds of SGI workstations connected by four networks: Ethernet, FDDI, HiPPI, and ATM. The performance results for three communication software systems are presented, analyzed and compared. These systems are: BSD socket programming interface, IONA's Orbix, an implementation of the CORBA specification, and the PVM message passing library. The results show that high-level communication interfaces, such as CORBA and PVM, can achieve reasonable performance under certain conditions.

Fatoohi, Rod↗
NASA NTRS2008

Interfacing Space Communications and Navigation Network Simulation with Distributed System Integration Laboratories (DSIL)

NASA's planned Lunar missions will involve multiple NASA centers where each participating center has a specific role and specialization. In this vision, the Constellation program (CxP)'s Distributed System Integration Laboratories (DSIL) architecture consist of multiple System Integration Labs (SILs), with simulators, emulators, testlabs and control centers interacting with each other over a broadband network to perform test and verification for mission scenarios. To support the end-to-end simulation and emulation effort of NASA' exploration initiatives, different NASA centers are interconnected to participate in distributed simulations. Currently, DSIL has interconnections among the following NASA centers: Johnson Space Center (JSC), Kennedy Space Center (KSC), Marshall Space Flight Center (MSFC) and Jet Propulsion Laboratory (JPL). Through interconnections and interactions among different NASA centers, critical resources and data can be shared, while independent simulations can be performed simultaneously at different NASA locations, to effectively utilize the simulation and emulation capabilities at each center. Furthermore, the development of DSIL can maximally leverage the existing project simulation and testing plans. In this work, we describe the specific role and development activities at JPL for Space Communications and Navigation Network (SCaN) simulator using the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to simulate communications effects among mission assets. Using MACHETE, different space network configurations among spacecrafts and ground systems of various parameter sets can be simulated. Data that is necessary for tracking, navigation, and guidance of spacecrafts such as Crew Exploration Vehicle (CEV), Crew Launch Vehicle (CLV), and Lunar Relay Satellite (LRS) and orbit calculation data are disseminated to different NASA centers and updated periodically using the High Level Architecture (HLA). In addition, the performance of DSIL under different traffic loads with different mix of data and priorities are evaluated.

Traffic Measuring and Monitoring↗
NASA NTRS1990

Improving processing and toughness of a high performance composite matrix through an interpenetrating polymer network. VI

The use of a semiinterpenetrating polymer network (SIPN) of the high-performance polyimide NR-150B2 to reduce brittleness and improve processability in the highly crosslinked acetylene-terminated polyimides Thermid LR-600, AL-600, MC-600, and FA-700 is described. The theoretical basis of the SIPN process is reviewed; the preparation and characterization of the neat SIPN resins and unidirectional graphite-fiber composites are explained; and the results are presented in extensive tables, graphs, and micrographs and discussed in detail. Significant increases in fracture energy were observed with SIPN, from 93 J/sq m for unmodified LR-600 to 283-603 J/sq m for the SIPN materials; the room-temperature flexural strength of the unidirectional composites also increased, from 1344 MPa for an unmodified MC-600 composite to 2020-1751 MPa for the SIPN composites. The potential applicability of SIPN-based composites to aerospace structures and electronic components is indicated.

Pater, Ruth H.↗
NASA NTRS2005

Multiangle Imaging Spectroradiometer (MISR) Global Aerosol Optical Depth Validation Based on 2 Years of Coincident Aerosol Robotic Network (AERONET) Observations

Performance of the Multiangle Imaging Spectroradiometer (MISR) early postlaunch aerosol optical thickness (AOT) retrieval algorithm is assessed quantitatively over land and ocean by comparison with a 2-year measurement record of globally distributed AERONET Sun photometers. There are sufficient coincident observations to stratify the data set by season and expected aerosol type. In addition to reporting uncertainty envelopes, we identify trends and outliers, and investigate their likely causes, with the aim of refining algorithm performance. Overall, about 2/3 of the MISR-retrieved AOT values fall within [0.05 or 20% x AOT] of Aerosol Robotic Network (AERONET). More than a third are within [0.03 or 10% x AOT]. Correlation coefficients are highest for maritime stations (approx.0.9), and lowest for dusty sites (more than approx.0.7). Retrieved spectral slopes closely match Sun photometer values for Biomass burning and continental aerosol types. Detailed comparisons suggest that adding to the algorithm climatology more absorbing spherical particles, more realistic dust analogs, and a richer selection of multimodal aerosol mixtures would reduce the remaining discrepancies for MISR retrievals over land; in addition, refining instrument low-light-level calibration could reduce or eliminate a small but systematic offset in maritime AOT values. On the basis of cases for which current particle models are representative, a second-generation MISR aerosol retrieval algorithm incorporating these improvements could provide AOT accuracy unprecedented for a spaceborne technique.

global meaurement↗