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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Learnability of Red-Green Opponency

Lennie, Haake, and Williams found that in the lateral geniculate nucleus (LGN), parvocellular unit responses are consistent with the hypothesis that their input connectivity is blind to the difference between middle-wavelength-sensitive (MWS) and long-wavelength-sensitive (LWS) cones. Most of their cells have a total MWS input weight opposite in sign and similar in magnitude to their total LWS input weight. If these weights are exactly balanced, the construction of a red-green opponent system from such units is simple: such units need only be aligned so the signs of their outputs agree. Ahumada and Mulligan described an associative learning process which can accomplish this alignment. If the units are not balanced (carry some luminance information), the strong overlap between the MWS and LWS spectral responses can cause units to agree more on the basis of luminance, and the associative process fails to produce red-green opponency. The learning theory requires that the LGN units be nearly balanced (more strongly encode chromaticity than luminance) and quantitatively expresses the requirement: the principal component of the LGN outputs must be in the red-green rather than the luminance direction. We show that the cone weights of the monkey LGN cells measured by Derrington, Krauskopf, and Lennie can satisfy this learnability criterion even if the MWS spectral response is close to the LWS spectral response, simulating anomalous trichromacy. The learnability theory provides a source of visual system variation for explaining why different anomalous trichromats may make the same average anomaloscope match (same pigments), but have either narrow (good opponent learning) or wide (poor learning) ranges of acceptable matches.

Ahumada, Albert J., Jr.↗
NASA NTRS1987

Task path planning, scheduling and learning for free-ranging robot systems

The development of robotics applications for space operations is often restricted by the limited movement available to guided robots. Free ranging robots can offer greater flexibility than physically guided robots in these applications. Presented here is an object oriented approach to path planning and task scheduling for free-ranging robots that allows the dynamic determination of paths based on the current environment. The system also provides task learning for repetitive jobs. This approach provides a basis for the design of free-ranging robot systems which are adaptable to various environments and tasks.

Wakefield, G. Steve↗
NASA NTRS1991

Rose garden promises of intelligent tutoring systems: Blossom or thorn

Intelligent tutoring systems (ITS) have been in existence for over a decade. However, few controlled evaluation studies have been conducted comparing the effectiveness of these systems to more traditional instruction methods. Two main promises of ITSs are examined: (1) Engender more effective and efficient learning in relation to traditional formats; and (2) Reduce the range of learning outcome measures where a majority of individuals are elevated to high performance levels. Bloom (1984) has referred to these as the two sigma problem; to achieve two standard deviation improvements with tutoring over traditional instruction methods. Four ITSs are discussed in relation to the two promises. These tutors have undergone systematic, controlled evaluations: (1) The LISP tutor (Anderson Farrell and Sauers, 1984); (2) Smithtown (Shute and Glaser, in press); (3) Sherlock (Lesgold, Lajoie, Bunzo and Eggan, 1990); and (4) The Pascal ITS (Bonar, Cunningham, Beatty and Well, 1988). Results show that these four tutors do accelerate learning with no degradation in final outcome. Suggestions for improvements to the design and evaluation of ITSs are discussed.

Shute, Valerie J.↗
NASA NTRS2000

A Computer Learning Center for Environmental Sciences

In the fall of 1998, MacMillan Hall opened at Brown University to students. In MacMillan Hall was the new Computer Learning Center, since named the EarthLab which was outfitted with high-end workstations and peripherals primarily focused on the use of remotely sensed and other spatial data in the environmental sciences. The NASA grant we received as part of the "Centers of Excellence in Applications of Remote Sensing to Regional and Global Integrated Environmental Assessments" was the primary source of funds to outfit this learning and research center. Since opening, we have expanded the range of learning and research opportunities and integrated a cross-campus network of disciplines who have come together to learn and use spatial data of all kinds. The EarthLab also forms a core of undergraduate, graduate, and faculty research on environmental problems that draw upon the unique perspective of remotely sensed data. Over the last two years, the Earthlab has been a center for research on the environmental impact of water resource use in and regions, impact of the green revolution on forest cover in India, the design of forest preserves in Vietnam, and detailed assessments of the utility of thermal and hyperspectral data for water quality analysis. It has also been used extensively for local environmental activities, in particular studies on the impact of lead on the health of urban children in Rhode Island. Finally, the EarthLab has also served as a key educational and analysis center for activities related to the Brown University Affiliated Research Center that is devoted to transferring university research to the private sector.

Mustard, John F.↗
NASA NTRSDate not supplied

Detecting Satellite Laser Ranging Station Data and Operational Anomalies with Machine Learning Isolation Forests at NASA's CDDIS

The International Laser Ranging Service (ILRS) is currently composed of 45 active satellite laser ranging (SLR) stations with several more set to join the network over the next several years. Station changes and histories are logged to files, but not always in real time. Sometimes these details are not added until long after changes have been made to the station –on occasion, years later. This in addition to unexpected hardware errors and other system issues that are not immediately detected impact the products generated by analysts. The ILRS Central Bureau (CB) and NASA’s Crustal Dynamics Data Information System (CDDIS) have worked to provide tools for station engineers to use. This includes the creation of station plots which contain temperature and pressure information along with LAser GEOdynamic Satellite (LAGEOS) and LAser RElativity Satellite (LARES) tracking information that enable the monitoring of station performance and todetermine whether the station has undergone any changes. As next steps, the CDDIS is working to enhance these station performance monitoring tools through machine learning. Isolation forest is an unsupervised machine learning algorithm commonly applied to anomaly detection. In this poster, the CDDIS details the steps taken to track anomalies within SLR station performance using isolation forest with LAGEOS and LARES satellite data.

Benjamin P Michael↗
NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗
NASA NTRS2012

Antarctic Exploration Parallels for Future Human Planetary Exploration: Science Operations Lessons Learned, Planning, and Equipment Capabilities for Long Range, Long Duration Traverses

The purpose for this workshop can be summed up by the question: Are there relevant analogs to planetary (meaning the Moon and Mars) to be found in polar exploration on Earth? The answer in my opinion is yes or else there would be no reason for this workshop. However, I think some background information would be useful to provide a context for my opinion on this matter. As all of you are probably aware, NASA has been set on a path that, in its current form, will eventually lead to putting human crews on the surface of the Moon and Mars for extended (months to years) in duration. For the past 50 V 60 years, starting not long after the end of World War II, exploration of the Antarctic has accumulated a significant body of experience that is highly analogous to our anticipated activities on the Moon and Mars. This relevant experience base includes: h Long duration (1 year and 2 year) continuous deployments by single crews, h Established a substantial outpost with a single deployment event to support these crews, h Carried out long distance (100 to 1000 kilometer) traverses, with and without intermediate support h Equipment and processes evolved based on lessons learned h International cooperative missions This is not a new or original thought; many people within NASA, including the most recent two NASA Administrators, have commented on the recognizable parallels between exploration in the Antarctic and on the Moon or Mars. But given that level of recognition, relatively little has been done, that I am aware of, to encourage these two exploration communities to collaborate in a significant way. [Slide 4] I will return to NASA s plans and the parallels with Antarctic traverses in a moment, but I want to spend a moment to explain the objective of this workshop and the anticipated products. We have two full days set aside for this workshop. This first day will be taken up with a series of presentations prepared by individuals with experience that extends back as far as the late 1940s and includes contemporary experience. The people presenting bring a variety of points of view, including not only U.S. but international, although most, if not all, have collaborated on international teams. The second day will consist of a series of small focused group interactions centered on those elements likely to be needed for traverse missions, such as mobility, habitation, and extravehicular activity (EVA, aka space suits). Our invited participants will be talking with people that specialize in these elements so that we can foster more direct interaction and exchange of experiences between these two exploration communities. After the workshop we will be preparing a report documenting these presentations and the essence of the focused interactions.

Hoffman, Stephen J.↗
NASA NTRS2012

Dynamic Range and Sensitivity Requirements of Satellite Ocean Color Sensors: Learning from the Past

Sensor design and mission planning for satellite ocean color measurements requires careful consideration of the signal dynamic range and sensitivity (specifically here signal-to-noise ratio or SNR) so that small changes of ocean properties (e.g., surface chlorophyll-a concentrations or Chl) can be quantified while most measurements are not saturated. Past and current sensors used different signal levels, formats, and conventions to specify these critical parameters, making it difficult to make cross-sensor comparisons or to establish standards for future sensor design. The goal of this study is to quantify these parameters under uniform conditions for widely used past and current sensors in order to provide a reference for the design of future ocean color radiometers. Using measurements from the Moderate Resolution Imaging Spectroradiometer onboard the Aqua satellite (MODISA) under various solar zenith angles (SZAs), typical (L(sub typical)) and maximum (L(sub max)) at-sensor radiances from the visible to the shortwave IR were determined. The Ltypical values at an SZA of 45 deg were used as constraints to calculate SNRs of 10 multiband sensors at the same L(sub typical) radiance input and 2 hyperspectral sensors at a similar radiance input. The calculations were based on clear-water scenes with an objective method of selecting pixels with minimal cross-pixel variations to assure target homogeneity. Among the widely used ocean color sensors that have routine global coverage, MODISA ocean bands (1 km) showed 2-4 times higher SNRs than the Sea-viewing Wide Field-of-view Sensor (Sea-WiFS) (1 km) and comparable SNRs to the Medium Resolution Imaging Spectrometer (MERIS)-RR (reduced resolution, 1.2 km), leading to different levels of precision in the retrieved Chl data product. MERIS-FR (full resolution, 300 m) showed SNRs lower than MODISA and MERIS-RR with the gain in spatial resolution. SNRs of all MODISA ocean bands and SeaWiFS bands (except the SeaWiFS near-IR bands) exceeded those from prelaunch sensor specifications after adjusting the input radiance to L(sub typical). The tabulated L(sub typical), L(sub max), and SNRs of the various multiband and hyperspectral sensors under the same or similar radiance input provide references to compare sensor performance in product precision and to help design future missions such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission and the Pre-Aerosol-Clouds-Ecosystems (PACE) mission currently being planned by the U.S. National Aeronautics and Space Administration (NASA).

chlorophyll fluorescence↗
NASA NTRS2005

The Different Wavelengths of Radio Science

Radio Science covers many different avenues. This summer I attempted to work in each of the different avenues to learn the full range of subjects covered by Radio Science. I began my summer by traveling to Greece for the 3rd International Planetary Probe Workshop (IPPW-3). I went as a co-author of the Doppler Wind Experiment (DWE) team paper. My first job when I returned from Greece was to update the Radio Science activities webpage. I then used Fast Fourier Transforms (FFT) to find radio signals in recorded Radio Science experimental data and determine frequencies and powers. I read about and ran Fortran code being used to determine wind measurements on Huygens. I formatted and revised the abstracts and data lengths for the DVD data sets. By performing these tasks, I also learned the Unix operating system as well as a small amount of shell programming.

Malecha, Jessica L.↗
NASA NTRS1994

Instructable autonomous agents

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.

Huffman, Scott Bradley↗
NASA NTRS2001

Strategies Which Foster Broad Use and Deployment of Earth and Space Science Informal and Formal Education Resources

Education projects supported by federal agencies and carried out by a wide range of organizations foster learning about Earth and Space systems science in a wide array of venues. Across these agencies a range of strategies are employed to ensure that effective materials are created for these diverse venues. And that these materials are deployed broadly so that a large spectrum of the American Public, both adults and children alike, can learn and become excited by the Earth and space system science. This session will highlight some of those strategies and will cover representative examples to illustrate the effectiveness of the strategies. Invited speakers from selected formal and informal educational efforts will anchor this session. Speakers with representative examples are encouraged to submit abstracts for the session to showcase the strategies which they use.

Meeson, Blanche W.↗
NASA NTRS1990

Neural controller for adaptive movements with unforeseen payloads

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3 percent of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

Kuperstein, Michael↗
NASA NTRSDate not supplied

Development of Physics-Based Transition Models for Unstructured-Mesh CFD Codes Using Deep Learning Models

Predicting transition locations over a vehicle surface is of fundamental importance for many engineering applications. With the transition information, the Reynolds-averaged Navier-Stokes (RANS) computations can turn on the turbulence model at the right locations so that drag, lift and other aerodynamic quantities can be accurately predicted. In contrast to the popularity of RANS-based transition modeling in which transition onset is governed by the turbulence equations, physics-based transition models that account for instability waves within the boundary layer, thus more compliant to flow physics, only gained more attention in recent years. This paper describes the development of a new physics-based transition model based on either the linear stability theory (LST) or parabolized stability equations (PSE). The model is designed to communicate with a structured or unstructured-mesh RANS solver back and forth in order to more accurately compute transition fronts over a three-dimensional body. In the developed model, the Python suite of interface codes in conjunction with the LASTRAC software can be executed autonomously to produce transition onset locations for a given laminar or RANS-computed transitional state. In addition, as a proof of concept, the tool set consists of a deep learning neural network model that has been designed and trained to predict instability wave evolutions inside the boundary layer for various instability wave mechanisms across a selected speed range. A machine-learned intelligent profile interpolation model has also been devised to enable reliable instability-wave spectra predictions with just a few points in the mean flow profiles.

Transition Modeling↗
NASA NTRS1996

Evaluation of Student Outcomes in Materials Science and Technology

This paper specifies 14 benchmarks and exit standards for the introduction of Materials Science and Technology in a secondary school education. Included is the standard that students should be able to name an example of each category of technological materials including metals, glass/ceramics, polymers (plastics) and composites. Students should know that each type of solid material has specific properties that can be measured. Students will learn that all solid materials have either a long range crystalline structure or a short range amorphous structure (i.e., glassy). They should learn the choice of materials for a particular application depends on the properties of the material, and the properties of the material depends on its crystal structure and microstructure. The microstructure may be modified by the methods by which the material is processed; students should explain this by the example of sintering a ceramic body to reduce its porosity and increase its densification and strength. Students will receive exposure to the world of work, post secondary educational opportunities, and in general a learning that will lead to a technologically literate intelligent citizen.

Piippo, Steven↗
NASA NTRS1998

Neural learning rules for the vestibulo-ocular reflex

Mechanisms for the induction of motor learning in the vestibulo-ocular reflex (VOR) were evaluated by recording the patterns of neural activity elicited in the cerebellum by a range of stimuli that induce learning. Patterns of climbing-fiber, vestibular, and Purkinje cell simple-spike signals were examined during sinusoidal head movement paired with visual image movement at stimulus frequencies from 0.5 to 10 Hz. A comparison of simple-spike and vestibular signals contained the information required to guide learning only at low stimulus frequencies, and a comparison of climbing-fiber and simple-spike signals contained the information required to guide learning only at high stimulus frequencies. Learning could be guided by comparison of climbing-fiber and vestibular signals at all stimulus frequencies tested, but only if climbing fiber responses were compared with the vestibular signals present 100 msec earlier. Computational analysis demonstrated that this conclusion is valid even if there is a broad range of vestibular signals at the site of plasticity. Simulations also indicated that the comparison of vestibular and climbing-fiber signals across the 100 msec delay must be implemented by a subcellular "eligibility" trace rather than by neural circuits that delay the vestibular inputs to the site of plasticity. The results suggest two alternative accounts of learning in the VOR. Either there are multiple mechanisms of learning that use different combinations of neural signals to drive plasticity, or there is a single mechanism tuned to climbing-fiber activity that follows activity in vestibular pathways by approximately 100 msec.

NASA Discipline Neuroscience↗
NASA NTRS2002

Stirring Up an Elastic Fluid: Critical Viscosity of Xenon-2 (CVX-2)

Whipped cream stays in place even when turned upside down. Yet it readily flows through the nozzle of a spray can to reach the dessert plate. This demonstrates the phenomenon of shear thinning that is important to many industrial and physical processes. Paints, film emulsions, and other complex solutions that are highly viscous under normal conditions but become thin and flow easily under shear forces. A simple fluid, such as water, does not exhibit shear thinning under normal conditions. Very close to the liquid-vapor critical point, where the distinction between liquid and vapor disappears, the fluid becomes more complex and is predicted to display shear thinning. At the critical point, xenon atoms interact over long distances in a classical model of cooperative phenomena. Physicists rely on this system to learn how long-range order arises. The Critical Viscosity of Xenon Experiment (CVX-2) will measure the viscous behavior of xenon, a heavy inert gas used in flash lamps and ion rocket engines, at its critical point. Although it does not easily combine with other chemicals, its viscosity at the critical point can be used as a model for a range of fluids. Viscosity originates from the interactions of individual molecules. It is so complicated that, except for the simplest gas, it cannot be calculated accurately from theory. Tests with critical fluids can provide key data, but are limited on Earth because critical fluids are highly compressed by gravity. CVX-2 employs a tiny metal screen vibrating between two electrodes in a bath of critical xenon. The vibrations and how they dampen are used to measure viscosity. CVX flew on STS-85 (1997), where it revealed that, close to the critical point, the xenon is partly elastic: it can 'stretch' as well as flow. For STS-107, the hardware has been enhanced to determine if critical xenon is a shear-thinning fluid.

Berg, Robert F.↗
NASA NTRS2008

Tool Support for Parametric Analysis of Large Software Simulation Systems

The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.

Schumann, Johann↗
NASA NTRS2010

The Process of the Development of an Operator

On the job training is where new employees called operators can start gaining knowledge on what they will be working on during their time in JSC. In these lessons I learned different things that are important ranging from thermal systems to electrical systems. While doing OJT classes the student will learn how to use a portable computer system which has displays that I also helped edit and clean up. The way you can also learn is by reading system briefs which describes the different systems. Due to the fact of a possible change in the ISS I updated a systems brief so that it can be relevant to what is actually on the space station. I was given a task that will help develop my skills and make myself better prepared for my future in the work field. The project that I worked on had me pulling real time data from the International Space Station. The Data I obtained from the space station will be correlated to battery performance. The group I will be working which is called REBA and we will take the telemetry and evaluate the data. I will be working with my mentor Ben Chislom and co-op Tyler along with the Pro team. They then will put this data into a graph so that they can get the discrepancies and find a way to improve the battery performance. The first weeks I read familiarization books that informed me how the ISS works, how it was built, and the systems that are used to keep the station working. This project is going to benefit NASA by finding out how electricity is being used on the ISS and enabling us to see how it can be used more efficiently. This way we can operate the ISS without wasting power. While conducting research that goes on inside the space station knowing all electricity is being used efficiently.

Banks, Terrence↗