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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Dynamic Control of Plans with Temporal Uncertainty

Certain planning systems that deal with quantitative time constraints have used an underlying Simple Temporal Problem solver to ensure temporal consistency of plans. However, many applications involve processes of uncertain duration whose timing cannot be controlled by the execution agent. These cases require more complex notions of temporal feasibility. In previous work, various "controllability" properties such as Weak, Strong, and Dynamic Controllability have been defined. The most interesting and useful Controllability property, the Dynamic one, has ironically proved to be the most difficult to analyze. In this paper, we resolve the complexity issue for Dynamic Controllability. Unexpectedly, the problem turns out to be tractable. We also show how to efficiently execute networks whose status has been verified.

Morris, Paul↗
NASA NTRS2007

An Overview of the Distributed Space Exploration Simulation (DSES) Project

This paper describes the Distributed Space Exploration Simulation (DSES) Project, a research and development collaboration between NASA centers which investigates technologies, and processes related to integrated, distributed simulation of complex space systems in support of NASA's Exploration Initiative. In particular, it describes the three major components of DSES: network infrastructure, software infrastructure and simulation development. With regard to network infrastructure, DSES is developing a Distributed Simulation Network for use by all NASA centers. With regard to software, DSES is developing software models, tools and procedures that streamline distributed simulation development and provide an interoperable infrastructure for agency-wide integrated simulation. Finally, with regard to simulation development, DSES is developing an integrated end-to-end simulation capability to support NASA development of new exploration spacecraft and missions. This paper presents the current status and plans for these three areas, including examples of specific simulations.

Crues, Edwin Z.↗
NASA NTRS2019

Utilization of Unsupervised Anomalies Detector as a Tool for Managing the TDRS Constellation at GSFC

NASA’s Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The mission of the TDRS constellation is to provide relay communications from low-earth orbiting spacecraft to the primary ground station at the White Sands Complex in Las Cruces, New Mexico. Major customers include the International Space Station and Hubble Space Telescope. The NASA Space Network project office at GSFC manages the constellation of spacecraft. The constellation is over 30 years old, and a wide range of technologies and manufacturing techniques are represented on-orbit. Since 1983, the TDRS constellation has recorded thousands of gigabytes of telemetry data. Spacecraft telemetry data has changed throughout the three generations of TDRS spacecraft, however each spacecraft has the same basic functions with some generational enhancements. The constellation includes several spacecraft that have significantly outlived the manufacturer's projected lifetime. This has provided NASA with a significant benefit in terms of return on investment, however it places a burden on efficient management of the assets for maximum life without permitting a TDRS spacecraft to become stranded in its geosynchronous orbital slot. Consequently, the highest level of attention is paid to systems whose failure could strand a TDRS spacecraft in orbit. In this paper, we proposed two stages of analyzing spacecraft anomalies using data mining (DM) to enhance on-going predictions of spacecraft life, subsystem performance, and analysis of subsystem anomalies. The first stage conducts the unsupervised anomaly detector to detect potential anomalies in real-time telemetry data. The second stage introduced telemetry weight (TW) to each telemetry parameter to determine which parameter caused the strongest anomaly. We will present case studies of some of these analyses and how the data can impact decisions on the management of the constellation.

Ma, Kenneth Y.↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS1997

Neural Network Research on Tilt-Rotor Hover Performance

Results from a neural network study on XV-15 tilt-rotor hover performance are presented. Two XV-15 test data bases, acquired during separate tests conducted in the National Full-Scale Aerodynamics Complex at NASA Ames, were used in this study. The two XV-15 test data bases used were: the 1997 80- by 120-Foot Wind Tunnel test data base and the 1984 Outdoor Aerodynamic Research Facility (OARF) test data base. The objectives associated with the 80- by 120-Foot Wind Tunnel test data were to conduct data quality checks, obtain neural network representations, and demonstrate sensitivity to test conditions. The objectives associated with the OARF outdoor test data were to obtain a 'zero wind' neural network representation, and formulate and implement a neural network based wind correction procedure. An additional objective, common to both test data bases, was to conduct error comparisons. The conclusions from the present study were as follows: Compared to measured parameters (i.e., collective pitch angle), derived parameters (i.e., thrust coefficient) were found preferable as neural network inputs. For the 80- by 120-Foot Wind Tunnel test data base, the neural network based figure of merit representations did not extrapolate rotor stall. Using the OARF outdoor test database, a neural network based wind correction procedure was formulated and successfully implemented. Based on an error comparison, the present neural network based wind correction procedure was found to be more accurate compared to the existing, momentum-theory-based wind correction procedure. Also, a separate error comparison showed that the 80- by 120-Foot Wind Tunnel hover performance data were comparable to non-wind-corrected outdoor test data.

Kozapalli, S.↗
NASA NTRS1988

Networks Of Executive Controllers For Automation

Paper discusses principal issues to be resolved in development of autonomous executive-controller shell for Space Station. Shell represents major increase in complexity of automated systems. More-complex control tasks require system that deals with different goals requiring sequences of tasks that change state of system world in complex ways. Requires integration of all functions. Applications include space station communications, tracking, life support, data processing support, navigation, and control of thermal and structural subsystems.

Erickson, William K.↗
NASA NTRS1993

Cognitive engineering models: A prerequisite to the design of human-computer interaction in complex dynamic systems

This chapter examines a class of human-computer interaction applications, specifically the design of human-computer interaction for the operators of complex systems. Such systems include space systems (e.g., manned systems such as the Shuttle or space station, and unmanned systems such as NASA scientific satellites), aviation systems (e.g., the flight deck of 'glass cockpit' airplanes or air traffic control) and industrial systems (e.g., power plants, telephone networks, and sophisticated, e.g., 'lights out,' manufacturing facilities). The main body of human-computer interaction (HCI) research complements but does not directly address the primary issues involved in human-computer interaction design for operators of complex systems. Interfaces to complex systems are somewhat special. The 'user' in such systems - i.e., the human operator responsible for safe and effective system operation - is highly skilled, someone who in human-machine systems engineering is sometimes characterized as 'well trained, well motivated'. The 'job' or task context is paramount and, thus, human-computer interaction is subordinate to human job interaction. The design of human interaction with complex systems, i.e., the design of human job interaction, is sometimes called cognitive engineering.

Mitchell, Christine M.↗
NASA NTRS2023

High-Rate Delay Tolerant Networking (HDTN) User Guide Version 1.0

Delay Tolerant Networking (DTN) has been identified as a key technology to enable and facilitate the development and growth of future space networks. Classically, space communications networks are collections of disparate links that are manually managed either point-to-point or use space relays. The accelerating accessibility of space enables a new scaling of space nodes, yet both the manual management of configurations and scheduling and the lack of structure connecting links precisely prohibit scaling. This challenge gives rise to newer and larger classes of communications needs that are met by DTN, which must overcome the disconnection, disruption, latency, and mobility featured in space communications systems. DTN joins the underlying links as an overlay, and can be made to communicate over any protocol stack. The core actions of DTN are store, carry, and forward, where data are stored instead of dropped if there is no immediately available outduct. It does this by taking the DTN unit of data, bundles, and providing necessary layers to adapt these bundles to the underlying transport protocols of choice; these are called convergence layers. DTN's Bundle Protocol (BP) can then be used on top of terrestrial protocol stacks, such as TCP/IP, as well as protocols for space, such as LTP/AOS, all in the same network. For emphasis it is noted that bundles can be of essentially any size, and hence this convergence to lower layers of choice is necessary. Existing DTN implementations have operated in constrained environments with limited resources, resulting in low data speeds. However, as various technologies have advanced, data transfer rates and efficiency have advanced, which has pushed the need for a DTN implementation for ground systems and for spacecraft that is performance-oriented in order to not impose an unnecessary bottleneck. High-rate Delay Tolerant Networking (HDTN) takes advantage of modern hardware platforms to substantially reduce latency and improve throughput compared to today’s DTN operations. The HDTN implementation maintains interoperability with existing deployments of DTN that conform to IETF RFCs 4838, 5050, and 9171. At the same time, HDTN defines a new data format better suited to higher-rate operation. It defines and adopts a massively parallel pipelined and message-oriented architecture, allowing the system to scale gracefully as its resources increase. HDTN’s architecture also supports hooks to replace various processing pipeline elements with specialized hardware accelerators. This offers improved Size, Weight, and Power (SWaP) characteristics while reducing development complexity and cost.

Delay Tolerant Networking↗
NASA NTRS1998

Lightning Radio Source Retrieval Using Advanced Lightning Direction Finder (ALDF) Networks

A linear algebraic solution is provided for the problem of retrieving the location and time of occurrence of lightning ground strikes from an Advanced Lightning Direction Finder (ALDF) network. The ALDF network measures field strength, magnetic bearing and arrival time of lightning radio emissions. Solutions for the plane (i.e., no Earth curvature) are provided that implement all of tile measurements mentioned above. Tests of the retrieval method are provided using computer-simulated data sets. We also introduce a quadratic planar solution that is useful when only three arrival time measurements are available. The algebra of the quadratic root results are examined in detail to clarify what portions of the analysis region lead to fundamental ambiguities in source location. Complex root results are shown to be associated with the presence of measurement errors when the lightning source lies near an outer sensor baseline of the ALDF network. In the absence of measurement errors, quadratic root degeneracy (no source location ambiguity) is shown to exist exactly on the outer sensor baselines for arbitrary non-collinear network geometries. The accuracy of the quadratic planar method is tested with computer generated data sets. The results are generally better than those obtained from the three station linear planar method when bearing errors are about 2 deg. We also note some of the advantages and disadvantages of these methods over the nonlinear method of chi(sup 2) minimization employed by the National Lightning Detection Network (NLDN) and discussed in Cummins et al.(1993, 1995, 1998).

Koshak, William J.↗
NASA NTRS1993

Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

Parker, B. Eugene, Jr.↗
NASA NTRS1992

Robustness of a distributed neural network controller for locomotion in a hexapod robot

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Chiel, Hillel J.↗
NASA NTRS1984

Mark IVA DSN 26-meter Subnet

The Office of Space Tracking and Data Systems' Networks Consolidation Program (NCP), managed by the Jet Propulsion Laboratory (JPL), includes the implementation of a 26-meter Tracking and Communications Subnet as a part of the Mark IV A Deep Space Network (DSN). The incorporatin of this subnet into the DSN will contribute to the NCP goal of consolidating the two NASA ground tracking networks into one tracking network. The 26-meter Tracking and Communication Subnet was designed to provide the capability to support, at each Deep Space Communication Complex, the tracking and data communication requirements of the earth-orbital missions that cannot be supported by the Tracking and Data Relay Satellite System when it becomes operational. Implementation activities and planned capabilities of the subnet are discussed.

Gordon, D. D.↗
NASA NTRS2000

Evaluation of Fieldbus and OPC for Advanced Life Support

FOUNDATION(Tm) Fieldbus and OP(TM) (OLE(TM)for Process Control) technologies were integrated into an existing control system for a crop growth chamber at NASA Ames Research Center. FOUNDATION(TM) Fieldbus is a digital, bi-directional, multi-drop, serial communications network which functions essentially as a LAN for sensors. FOUNDATION(TM) Fieldbus is heterarchical, with publishers and subscribers of data performing complex control functions at low levels without centralized control and its associated overhead. OPC(TM) is a set of interfaces which replace proprietary drivers with a transparent means of exchanging data between the fieldbus and applications. The objectives were: (1) to integrate FOUNDATION(TM) Fieldbus into existing ALS hardware and determine its overall effectiveness and reliability and, (2) to quantify any savings produced by using fieldbus and OPC technologies. We encountered several problems with the FOUNDATION(TM) Fieldbus hardware chosen. Our hardware exposed 100 data for each channel of the fieldbus. The fieldbus configurator software used to program the fieldbus was simply not adequate. The fieldbus was also not inherently reliable. It lost its settings twice during our tests for unknown reasons. OPC also had issues. It did not function at all as supplied, requiring substitution of some of its components with those from other vendors. It would stop working after a fixed period of time. Certain database calls eventually lock the machine. Overall, we would not recommend FOUNDATION(TM) Fieldbus: it was too difficult to implement with little overall added value. It also seems unlikely that FOUNDATION(TM) Fieldbus will gain sufficient penetration into the laboratory instrument market to ever be cost effective for the ALS community. OPC had good reliability and performance once a stable installation was achieved. It allowed a rapid change to an alternative software strategy when our first strategy failed. It is a cost effective solution to distributed control systems development.

Boulanger, Richard P.↗
NASA NTRS1991

Development of an UltraNet Based Distributed Visualization Application

The example application is a distributed visualization involving a supercomputer and a graphics workstation. The visualization computation is performed on a Connection Machine, end the results are rendered using a Silicon Graphics Workstations The UltraNet network installed at NAB allows high-bandwidth communication between the computers. Ideally, taking advantage of the UltraNet is no more complex than developing TCP/IP and Unix BSD socket-type applications on a single machine. In practice, there are several problems in developing an Application using the UltraNet. This paper identifies potential problems and discusses techniques for overcoming them. Performance of UltraNet communication is measured and found to be 10 MB/sec for SGI VGX workstations.

Krystynak, John↗
NASA NTRS2023

A Hyperspectral Inversion Framework for Estimating Absorbing Inherent Optical Properties and Biogeochemical Parameters in Inland and Coastal Waters

The simultaneous remote estimation of biogeochemical parameters (BPs) and inherent optical properties (IOPs) from hyperspectral satellite imagery of globally distributed optically distinct inland and coastal waters is a complex, unsolved, non-unique inverse problem. To tackle this problem, we leverage a machine-learning model termed Mixture Density Networks (MDNs). MDNs outperform operational algorithms by calculating the covariance between the simultaneously estimated products. We train the MDNs on a large ( N = 8237) dataset of co-aligned, in situ measured, hyperspectral remote sensing reflectance (R rs ), BPs, and absorbing IOPs from globally representative optically distinct inland and coastal waters. The estimated IOPs include absorption due to phytoplankton (a ph ), chromophoric dissolved organic matter (a cdom ), and non-algal particles (a nap ). The estimated BPs include chlorophyll-a, total suspended solids, and phycocyanin (PC). MDNs dramatically reduce uncertainty in the retrievals, relative to operational algorithms, when using a 50/50 dataset split, where the MDNs are trained on a randomly selected half of the in situ dataset and validated on the other half. Our model is shown to have higher, or equivalent, generalization performance than the calculated operational algorithms available for all BPs and IOPs (except PC) via a leave-one-out cross-validation assessment. The MDNs are sensitive to uncertainties in the hyperspectral satellite R rs , resulting from instrument noise and atmospheric correction; there is a difference of ~37.4–62.8% (using median symmetric accuracy) between the MDNs’ estimates derived from co-located satellite-derived R rs and in situ R rs . Of the IOPs, a cdom and a nap are less sensitive to uncertainties in hyperspectral satellite imagery relative to a ph , with remote estimates of a ph exhibiting incorrect spectral shape and magnitude relative to in situ measured IOPs. Despite the uncertainties in satellite derived R rs , the spatial distributions of BPs and IOPs in MDN-derived product maps of Lake Erie and the Curonian Lagoon, based on imagery taken with the Hyperspectral Imager for the Coastal Ocean (HICO) and PRecursore Iper-Spettrale della Missione Applicativa (PRISMA), are confirmed via co-aligned in situ measurements and agree with the literature’s understanding of these well-studied regions. The consistency and accuracy of the model on HICO and PRISMA imagery, despite radiometric uncertainties, demonstrate its applicability to future hyperspectral missions, such as the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, where the simultaneous estimation model will serve as a key part of phytoplankton community composition analysis.

Ryan E. O'Shea↗
NASA NTRSDate not supplied

Usage-based Lifing of Lithium-Ion Battery with HybridPhysics-Informed Neural Networks

Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs).The ability to model and forecast the remaining useful life of these batteries enables UAV reliability assurance. Building accurate models for battery state of charge and state of health based on first principles is challenging due to the complex electrochemistry that governs battery operations and computational complexity required to solve them. Therefore, reduced order models are often used due to their ability to capture the overall battery discharge. Un-fortunately, these simplifications lead to residual discrepancy between model predictions and observed data. In this paper, we present a hybrid modeling approach merging reduced-order models and neural networks. In this approach, while most of the input-output relationship is captured by Nernst and Butler-Volmer equations, data-driven kernels reduce the gap between predictions and observations. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence repository. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.

Lithium-ion Battery↗
NASA NTRS2017

DVB-S2 Experiment over NASA's Space Network

The commercial DVB-S2 standard was successfully demonstrated over NASAs Space Network (SN) and the Tracking Data and Relay Satellite System (TDRSS) during testing conducted September 20-22nd, 2016. This test was a joint effort between NASA Glenn Research Center (GRC) and Goddard Space Flight Center (GSFC) to evaluate the performance of DVB-S2 as an alternative to traditional NASA SN waveforms. Two distinct sets of tests were conducted: one was sourced from the Space Communication and Navigation (SCaN) Testbed, an external payload on the International Space Station, and the other was sourced from GRCs S-band ground station to emulate a Space Network user through TDRSS. In both cases, a commercial off-the-shelf (COTS) receiver made by Newtec was used to receive the signal at White Sands Complex. Using SCaN Testbed, peak data rates of 5.7 Mbps were demonstrated. Peak data rates of 33 Mbps were demonstrated over the GRC S-band ground station through a 10MHz channel over TDRSS, using 32-amplitude phase shift keying (APSK) and a rate 89 low density parity check (LDPC) code. Advanced features of the DVB-S2 standard were evaluated, including variable and adaptive coding and modulation (VCMACM), as well as an adaptive digital pre-distortion (DPD) algorithm. These features provided additional data throughput and increased link performance reliability. This testing has shown that commercial standards are a viable, low-cost alternative for future Space Network users.

waveforms↗
NASA NTRS1995

Comparison Of Full-Spectrum And Complex-Symbol Combining

Report presents theoretical study of anticipated performances of two methods of combining S-band signals from Galileo spacecraft received in multiple antennas of NASA's Deep Space Network (DSN). Signal transmitted by spacecraft consists of binary data signal phase-modulated onto square-wave subcarrier that is, in turn, amplitude-modulated onto S-band carrier.

Million, Samson↗