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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 829 records · Page 46

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Parameter Estimation for Decoding Sensor Signals

This paper introduces a parameter estimation approach for decoding digital sensor signals in a cyber-physical system. For unknown or not fully characterized digital sensor data, it can be difficult to decipher a desired signal from background or noise. In a cyber-physical system with networked sensors, we can leverage knowledge of the physical system to inform the decoding of the digital signals. This work in progress is a case study on deciphering commercial vehicle on-board sensor networks that communicate through the Controller Area Network (CAN). By understanding the stock vehicle sensor network, a vehicle can be extended into a scalable research platform with minimal instrumentation. Our challenge was to localize desired sensor signals encoded in network traffic that included other sensor data, control messages, as well as encoding and security overhead. Due to the vehicle’s unknown sensor network, our approach developed methods to efficiently analyze and identify key signals despite the large state-space for potential signal embeddings.

Nice, Matthew↗

NASA NTRS2003

NASA Langley WINN System Operational Assessment

An operational assessment of the NASA Langley Weather Information Network (WINN) System is presented. The objectives of this program include: 1) Determine if near real-time weather information presented on the flight deck improves pilot situational awareness of weather; and 2) Identify pilot interface issues related to the use of WINN system during test flights. This paper is in viewgraph form.

Jonsson, Jon↗

DOE OSTI2020

I/O routing in a multidimensional torus network

A method, system and computer program product are disclosed for routing data packet in a computing system comprising a multidimensional torus compute node network including a multitude of compute nodes, and an I/O node network including a plurality of I/O nodes. In one embodiment, the method comprises assigning to each of the data packets a destination address identifying one of the compute nodes; providing each of the data packets with a toio value; routing the data packets through the compute node network to the destination addresses of the data packets; and when each of the data packets reaches the destination address assigned to said each data packet, routing said each data packet to one of the I/O nodes if the toio value of said each data packet is a specified value. In one embodiment, each of the data packets is also provided with an ioreturn value used to route the data packets through the compute node network.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

I/O routing in a multidimensional torus network

Chen, Dong↗

NASA NTRS1990

CLIPS on the NeXT computer

This paper discusses the integration of CLIPS into a hybrid expert system neural network AI tool for the NeXT computer. The main discussion is devoted to the joining of these two AI paradigms in a mutually beneficial relationship. We conclude that expert systems and neural networks should not be considered as competing AI implementation methods, but rather as complimentary components of a whole.

Charnock, Elizabeth↗

NASA NTRS1982

The role of the deep space network's frequency and timing system in the detection of gravitational waves

A review of the Deep Space Network's (DSN) use of precision Doppler-tracking of deep space vehicles is presented. The review emphasizes operational and configurational aspects and considers: the projected configuration of the DSN's frequency and timing system; the environment within the DSN provided by the precision atomic standards within the frequency and timing system--both current and projected; and the general requirements placed on the DSN and the frequency and timing system for both the baseline and the nominal gravitational wave experiments. A comment is made concerning the current probability that such an experiment will be carried out in the foreseeable future.

Mankins, J. C.↗

NASA NTRS1991

Computer graphics testbed to simulate and test vision systems for space applications

Artificial intelligence concepts are applied to robotics. Artificial neural networks, expert systems and laser imaging techniques for autonomous space robots are being studied. A computer graphics laser range finder simulator developed by Wu has been used by Weiland and Norwood to study use of artificial neural networks for path planning and obstacle avoidance. Interest is expressed in applications of CLIPS, NETS, and Fuzzy Control. These applications are applied to robot navigation.

Cheatham, John B.↗

NASA NTRS2002

Investigation of RF Emissions From Wireless Networks as a Threat to Avionic Systems

The paper focuses on understanding and obtaining preliminary measurements of radiated field (RF) emissions of laptop/wireless local area network (WLAN) systems. This work is part of a larger research project to measure radiated emissions of wireless devices to provide a better understanding for potential interference with crucial aircraft avionics systems. A reverberation chamber data collection process is included, as well as recommendations for additional tests. Analysis of measurements from devices under test (DUTs) proved inconclusive for addressing potential interference issues. Continued effort is expected to result in a complete easily reproducible test protocol. The data and protocol presented here are considered preliminary.

Salud, Maria Theresa P.↗

DOE OSTI2021

Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks

We show that explainable neural networks can identify regions of oceanic variability that contribute predictability on decadal timescales in a fully coupled Earth-system model. The neural networks learn to use sea-surface temperature anomalies to predict future continental surface temperature anomalies. We then use a neural-network explainability method called layerwise relevance propagation to infer which oceanic patterns lead to accurate predictions made by the neural networks. In particular, regions within the North Atlantic Ocean and North Pacific Ocean lend the most predictability for surface temperature across continental North America. We apply the proposed methodology to decadal variability, although the concept is generalizable to other timescales of predictability. Furthermore, while our approach focuses on predictable patterns of internal variability within climate models, it should be generalizable to observational data as well. Our study contributes to the growing evidence that explainable neural networks are important tools for advancing geoscientific knowledge.

58 GEOSCIENCES↗

DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

High performance interconnection between high data rate networks

The bridge/gateway system needed to interconnect a wide range of computer networks to support a wide range of user quality-of-service requirements is discussed. The bridge/gateway must handle a wide range of message types including synchronous and asynchronous traffic, large, bursty messages, short, self-contained messages, time critical messages, etc. It is shown that messages can be classified into three basic classes, synchronous and large and small asynchronous messages. The first two require call setup so that packet identification, buffer handling, etc. can be supported in the bridge/gateway. Identification enables resequences in packet size. The third class is for messages which do not require call setup. Resequencing hardware based to handle two types of resequencing problems is presented. The first is for a virtual parallel circuit which can scramble channel bytes. The second system is effective in handling both synchronous and asynchronous traffic between networks with highly differing packet sizes and data rates. The two other major needs for the bridge/gateway are congestion and error control. A dynamic, lossless congestion control scheme which can easily support effective error correction is presented. Results indicate that the congestion control scheme provides close to optimal capacity under congested conditions. Under conditions where error may develop due to intervening networks which are not lossless, intermediate error recovery and correction takes 1/3 less time than equivalent end-to-end error correction under similar conditions.

Foudriat, E. C.↗

NASA NTRS1979

Phase-shift array, arbitrary and continuous through 360 deg

Langley Research Center's Phase-shift array satisfies requirement to phase shift common source signal into array of branch circuits thus simplifying multiple phase control. With system, wiring and assembly are uncomplicated, requiring no switching networks and system can be operated at any desired frequency. Design provides effective solution to problem of multiple shift control.

Dibble, A. C., Jr.↗

NASA NTRS1984

Configuration study for a 30 GHz monolithic receive array, volume 1

Gregorian, Cassegrain, and single reflector systems were analyzed in configuration studies for communications satellite receive antennas. Parametric design and performance curves were generated. A preliminary design of each reflector/feed system was derived including radiating elements, beam-former network, beamsteering system, and MMIC module architecture. Performance estimates and component requirements were developed for each design. A recommended design was selected for both the scanning beam and the fixed beam case. Detailed design and performance analysis results are presented for the selected Cassegrain configurations. The final design point is characterized in detail and performance measures evaluated in terms of gain, sidelobe level, noise figure, carrier-to-interference ratio, prime power, and beamsteering. The effects of mutual coupling and excitation errors (including phase and amplitude quantization errors) are evaluated. Mechanical assembly drawings are given for the final design point. Thermal design requirements are addressed in the mechanical design.

Nester, W. H.↗

NASA NTRS1990

Queueing models for token and slotted ring networks

Currently the end-to-end delay characteristics of very high speed local area networks are not well understood. The transmission speed of computer networks is increasing, and local area networks especially are finding increasing use in real time systems. Ring networks operation is generally well understood for both token rings and slotted rings. There is, however, a severe lack of queueing models for high layer operation. There are several factors which contribute to the processing delay of a packet, as opposed to the transmission delay, e.g., packet priority, its length, the user load, the processor load, the use of priority preemption, the use of preemption at packet reception, the number of processors, the number of protocol processing layers, the speed of each processor, and queue length limitations. Currently existing medium access queueing models are extended by adding modeling techniques which will handle exhaustive limited service both with and without priority traffic, and modeling capabilities are extended into the upper layers of the OSI model. Some of the model are parameterized solution methods, since it is shown that certain models do not exist as parameterized solutions, but rather as solution methods.

Peden, Jeffery H.↗

NASA NTRS1991

Investigation of air transportation technology at Princeton University, 1990-1991

The Air Transportation Technology Program at Princeton University is a program that emphasizes graduate and undergraduate student research. The program proceeded along six avenues during the past year: microburst hazards to aircraft, intelligent failure tolerant control, computer-aided heuristics for piloted flight, stochastic robustness of flight control systems, neural networks for flight control, and computer-aided control system design.

Stengel, Robert F.↗

NASA NTRS2004

Model based verification of the Secure Socket Layer (SSL) Protocol for NASA systems

The National Aeronautics and Space Administration (NASA) has tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information theft, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach' offers formal verification of information technology (IT), through the creation of a Software Security Assessment Instrument (SSAI), to address software security risks.

software security↗

DOE OSTI2022

Online Power System Event Detection via Bidirectional Generative Adversarial Networks

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. Here, the accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.

24 POWER TRANSMISSION AND DISTRIBUTION↗