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NASA NTRS2000

2000 NASA-HU American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

Since 1964, the National Aeronautics and Space Administration (NASA) has supported a program of summer faculty fellowships for engineering and science educators. In a series of collaborations between NASA research and development centers and nearby universities, engineering faculty members spend ten weeks working with professional peers on research. The Summer Faculty Program Committee of the American Society for Engineering Education supervises the programs. The objectives are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objectives of the NASA center. College or university faculty members will be appointed as Research Fellows to spend ten weeks in cooperative research and study at the NASA Langley Research Center. The Fellow will devote approximately 90 percent of the time to a research problem and the remaining time to a study program. The study program will consist of lectures and seminars on topics of interest or that are directly relevant to the Fellows' research topics. The lecture and seminar leaders will be distinguished scientists and engineers from NASA, education, and industry. A list of the abstracts of the presentations is provided.

Marable, William P.↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2000

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗
NASA NTRS2000

The Pascal Discovery Mission: A Mars Climate Network Mission

The climate of Mars is a major focus of Mars exploration. With the loss of MCO, however, it remains uncertain how it will be achieved. We argue that a truly dedicated climate mission to Mars should have both orbital and landed components, and that these should operate simultaneously for at least 1 Mars year if not longer. Pascal is a Discovery mission that emphasizes the landed component. Its principal goal is to establish a network of 24 small weather stations on the surface of Mars that will operate for 2 Mars years, with an extended mission option for an additional 8 Mars years bringing the total mission lifetime up to 10 Mars years. The stations will collect hourly measurements of pressure, temperature, and optical depth. After delivering the probes to Mars, Pascal's carrier spacecraft will go into an elliptical orbit which will serve as a relay for the landers, and a platform for synoptic imaging. These simultaneous measurements from the surface and from orbit will allow us to characterize the planet's general circulation and its interaction with the dust, water, and CO2 cycles. During entry, descent, and landing, each of Pascal's 24 probes will also measure the temperature structure of the atmosphere and acquire images of the surface. These data will allow us to determine the global structure of the atmosphere between 15 and 130 km, and characterize the local terrain to help interpret the landed data. The descent images are part of Pascal's outreach program, as the probe camera system will be developed by faculty-supervised student project. The intent is to generate enthusiasm for the Pascal mission by directly involving students. Pascal will be launched on a Delta II-7925 in August of 2005. A type I trajectory will deliver Pascal to Mars in January of 2006. On approach, the three-axis stabilized carrier spacecraft will spring deploy the Pascal probes in 4 separate salvo's of 6 each. Global coverage is achieved with small time-of-arrival adjustments in between each salvo. Pascal's probes utilize an aeroshell, parachute, and crushable material for entry, descent and landing. On the surface, their long life and global coverage is enabled by a Micro Thermal Power Source with demonstrated heritage. After all probes are released, the carrier spacecraft will execute a small burn for insertion into an elliptical orbit. The long lifetime of the Pascal network was chosen in part to maximize the chances that orbital sounding, like that planned with MCO, would occur at some point during the mission. If Pascal is selected for launch in '05, this could occur if MCO-like science is reflown in the '05 opportunity or, if it is reflown in '03, the mission is extended to overlap with Pascal. The combination of temperature sounding from orbit, and surface pressure mapping from the surface will allow a direct determination of the full 3-D wind field for the first time.

Haberle, R. M.↗
NASA NTRS2000

Signal Processing and Fault Detection with Application to CH-46 Helicopter Data

Central to Qualtech Systems' mission is its testability and maintenance software (TEAMS) and derivatives. Many systems comprise components equipped with self-testing capability; but, if the system is complex (and involves feedback and if the self-testing itself may occasionally be faulty) tracing faults to a single or multiple causes is difficult. However, even for systems involving many thousands of components the PC-based TEAMS provides essentially real-time system-state diagnosis. Until recently TEAMS operation was passive: its diagnoses were based on whatever data sensors could provide. Now, however, a signal-processing (SP) "frontend" matched to inference needs is available. Many standard signal processing primitives, such as filtering, spectrum analysis and multi-resolution decomposition are available; the SP toolbox is also equipped with a (supervised) classification capability based on a number of decision-making paradigms. This paper is about the SP toolbox. We show its capabilities, and demonstrate its performance on the CH-46 "Westland" data set.

Wen, Fang↗
NASA NTRS2000

Atmosphere of Freedom: Sixty Years at the NASA Ames Research Center

Throughout Ames History, four themes prevail: a commitment to hiring the best people; cutting-edge research tools; project management that gets things done faster, better and cheaper; and outstanding research efforts that serve the scientific professions and the nation. More than any other NASA Center, Ames remains shaped by its origins in the NACA (National Advisory Committee for Aeronautics). Not that its missions remain the same. Sure, Ames still houses the world's greatest collection of wind tunnels and simulation facilities, its aerodynamicists remain among the best in the world, and pilots and engineers still come for advice on how to build better aircraft. But that is increasingly part of Ames' past. Ames people have embraced two other missions for its future. First, intelligent systems and information science will help NASA use new tools in supercomputing, networking, telepresence and robotics. Second, astrobiology will explore lore the prospects for life on Earth and beyond. Both new missions leverage Ames long-standing expertise in computation and in the life sciences, as well as its relations with the computing and biotechnology firms working in the Silicon Valley community that has sprung up around the Center. Rather than the NACA missions, it is the NACA culture that still permeates Ames. The Ames way of research management privileges the scientists and engineers working in the laboratories. They work in an atmosphere of freedom, laced with the expectation of integrity and responsibility. Ames researchers are free to define their research goals and define how they contribute to the national good. They are expected to keep their fingers on the pulse of their disciplines, to be ambitious yet frugal in organizing their efforts, and to always test their theories in the laboratory or in the field. Ames' leadership ranks, traditionally, are cultivated within this scientific community. Rather than manage and supervise these researchers, Ames leadership merely guided them, represents them to NASA headquarters and the world outside, then steps out of the way before they get run over.

Bugos, Glenn E.↗
NASA NTRS1994

Goal Directed Model Inversion: A Study of Dynamic Behavior

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome 0 "would have been right if the outcome had been the desired one." The algorithm then proceeds as follows: (1) store the action that produced the wrong outcome as a "target" (2) redefine the wrong outcome as a desired goal (3) submit the new desired goal to the system (4) compare the new action with the target action and modify the system by using a suitable algorithm for credit assignment (Back propagation in our example) (5) resubmit the original goal. Prior publications by our group in this area focused on demonstrating empirical results based on the inverse kinematic problem for a simulated robotic arm. In this paper we apply the inversion process to much simpler analytic functions in order to elucidate the dynamic behavior of the system and to determine the sensitivity of the learning process to various parameters. This understanding will be necessary for the acceptance of GDMI as a practical tool.

Colombano, Silvano P.↗
NASA NTRS2000

Any Two Learning Algorithms Are (Almost) Exactly Identical

This paper shows that if one is provided with a loss function, it can be used in a natural way to specify a distance measure quantifying the similarity of any two supervised learning algorithms, even non-parametric algorithms. Intuitively, this measure gives the fraction of targets and training sets for which the expected performance of the two algorithms differs significantly. Bounds on the value of this distance are calculated for the case of binary outputs and 0-1 loss, indicating that any two learning algorithms are almost exactly identical for such scenarios. As an example, for any two algorithms A and B, even for small input spaces and training sets, for less than 2e(-50) of all targets will the difference between A's and B's generalization performance of exceed 1%. In particular, this is true if B is bagging applied to A, or boosting applied to A. These bounds can be viewed alternatively as telling us, for example, that the simple English phrase 'I expect that algorithm A will generalize from the training set with an accuracy of at least 75% on the rest of the target' conveys 20,000 bytes of information concerning the target. The paper ends by discussing some of the subtleties of extending the distance measure to give a full (non-parametric) differential geometry of the manifold of learning algorithms.

Wolpert, David H.↗
NASA NTRS2000

The Pascal Discovery Mission: A Mars Climate Network Mission

The climate of Mars is a major focus of Mars exploration. With the loss of MCO, however, it remains uncertain how it will be achieved. We argue that a truly dedicated climate mission to Mars should have both orbital and landed components, and that these should operate simultaneously for at least I Mars year if not longer. Pascal is Discovery mission that emphasizes the landed component. Its principal goal is to establish a network of 24 small weather stations on the surface of Mars that will operate for 2 Mars years, with an extended mission option for an additional 8 Mars years bringing the total mission lifetime up to 10 Mars years. The stations will collect hourly measurements of pressure, temperature, and optical depth. After delivering the probes to Mars, Pascal's carrier spacecraft will go into an elliptical orbit which will serve as a relay for the landers, and a platform for synoptic imaging. These simultaneous measurements from the surface and from orbit will allow us to characterize the planet's general circulation and its interaction with the dust, water, and CO2 cycles. During entry, descent, and landing, each of Pascal's 24 probes will also measure the temperature structure of the atmosphere and acquire images of the surface. These data will allow us to determine the global structure of the atmosphere between 15 and 130 km, and characterize the local terrain to help interpret the landed data. The descent images are part of Pascal's outreach program, as the probe camera system will be developed by faculty-supervised student project. The intent is to generate enthusiasm for the Pascal mission by directly involving students. Pascal will be launched on a Delta 11-7925 in August of 2005. A type I trajectory will deliver Pascal to Mars in January of 2006. On approach, the three-axis stabilized carrier spacecraft will spring deploy the Pascal probes in 4 separate salvo's of 6 each. Global coverage is achieved with small time-of-arrival adjustments in between each salvo. Pascal's probes utilize an aeroshell, parachute, and crushable material for entry, descent and landing. On the surface, their long life and global coverage is enabled by a Micro Thermal Power Source with demonstrated heritage. After all probes are released, the carrier spacecraft will execute a small bum for insertion into an elliptical orbit. The long lifetime of the Pascal network was chosen in part to maximize the chances that orbital sounding, like that planned with MCO, would occur at some point during the mission. If Pascal is selected for launch in -05, this could occur if MCO-like science is reflown in the '05 opportunity or, if it is reflown in '03, the mission is extended to overlap with Pascal. The combination of temperature sounding from orbit, and surface pressure mapping from the surface will allow a direct determination of the full 3-D wind field for the first time.

Haberle, Robert M.↗
NASA NTRS2000

Overview of Space Science and Information Research Opportunities at NASA

It is not possible to review all the opportunities that NASA provides to support the Space Science Enterprise, in the short amount of time allotted for this presentation. Therefore, only a few key programs will be discussed. The programs that I will discuss will concentrate on research opportunities for faculty, graduate and postdoctoral candidates in Space Science research and information technologies at NASA. One of the most important programs for research opportunities is the NASA Research Announcement or NRA. NASA Headquarters issues NRA's on a regular basis and these cover space science and computer science activities relating to NASA missions and programs. In the Space Sciences, the most important NRA is called the "Research Opportunities in Space Science or the ROSS NRA. The ROSS NRA is composed of multiple announcements in the areas of structure and evolution of the Universe, Solar System exploration, Sun-Earth connections, and applied information systems. Another important opportunity is the Graduate Student Research Program (GSRP). The GSRP is designed to cultivate research ties between a NASA Center and the academic community through the award of fellowships to promising students in science and engineering. This program is unique since it matches the student's area of research interest with existing work being carried out at NASA. This program is for U.S. citizens who are full-time graduate students. Students who are successful have made the match between their research and the NASA employee who will act as their NASA Advisor/ Mentor. In this program, the student's research is primarily accomplished under the supervision of his faculty advisor with periodic or frequent interactions with the NASA Mentor. These interactions typically involve travel to the sponsoring NASA Center on a regular basis. The one-year fellowships are renewable for up to three years and over $20,000 per year. These and other important opportunities will be discussed.

Green, James L.↗
NASA NTRS1996

Refining Linear Fuzzy Rules by Reinforcement Learning

Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.

Berenji, Hamid R.↗
NASA NTRS1998

Trace-Driven Debugging of Message Passing Programs

In this paper we report on features added to a parallel debugger to simplify the debugging of parallel message passing programs. These features include replay, setting consistent breakpoints based on interprocess event causality, a parallel undo operation, and communication supervision. These features all use trace information collected during the execution of the program being debugged. We used a number of different instrumentation techniques to collect traces. We also implemented trace displays using two different trace visualization systems. The implementation was tested on an SGI Power Challenge cluster and a network of SGI workstations.

Frumkin, Michael↗
NASA NTRS2002

Spectral Data Reduction via Wavelet Decomposition

The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

Kaewpijit, S.↗
NASA NTRS1998

Model Based Autonomy for Robust Mars Operations

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.

Kurien, James A.↗
NASA NTRS2002

Hybrid Active-Passive Systems for Control of Aircraft Interior Noise

It was proposed to continue with development and application in the two active-passive areas of Active Tuned Vibration Absorbers (ATVA) and smart foam applied to the reduction of interior noise in aircraft. In general the work was focused on making both techniques more efficient, practical and robust thus increasing their application potential. The work was also concerned with demonstrating the potential of these two technologies under realistic implementations as well as understanding the fundamental physics of the systems. The proposed work consisted of a three-year program and was tightly coordinated with related work being carried out in the Structural Acoustics Branch at NASA LaRC. The work was supervised and coordinated through all phases by Prof Chris Fuller of Va Tech.

Fuller, Chris R.↗
NASA NTRS1983

Skiing Simulation

Arvin/Calspan Advanced Technology Center's Dr. Michael Holden is an aerodynamicist who normally works on the reentry characteristics of space vehicles. Calspan donated the use of its wind tunnel for a program aimed at improving the performance of members of the U.S. Ski Team. Dr. Holden, a certified ski instructor, offered to apply his aerodynamics expertise to the design and supervision of the tests. Wind tunnel simulations have become an annual part of the U.S. Ski Team's training for U.S. and international events. Downhill ski racers learn from these simulations which body positions are most effective in which situations.

Source record↗
NASA NTRS2002

Fatigue Crack and Porosity Measurement in Composite Materials by Thermographic and Ultrasonic Methods

Many nondestructive methods exist for the detection of localized material anomalies in an otherwise good composite structure. The problem arises when the material system as a whole has degraded during service or was improperly manufactured. Porosity and intra-ply microcracking are two such conditions that in unlined composite pressure vessels can be very troublesome to detect and when linked through the thickness can be critical to mission success. These leak paths may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. Research sought nondestructive methods for quantifying porosity and microcracking in composite tankage. Both thermographic and resonance ultrasound methods have been utilized with artificial neural network and statistical approaches to analyze the data. Resonant ultrasound spectroscopy provides measurements, which are sensitive to fine details in the materials character, such as micro-cracking and porosity. Here, the higher frequency (shorter wavelength) components of the signal train provide more significant interaction with the defects causing the spectral characteristics to shift toward lower amplitudes at the higher frequencies. As the density of the defects increases more interactions occur and more drastic amplitude changes are observed. From a thermal perspective, the higher the defect density the lower the through thickness thermal diffusivity will be. Utilizing a point heat source, and thermographically recording the heat profile with time, diffusivity calculations can be made which in turn can be related to the relative quality of the material. Preliminary experiments to verify the measurable effect on the resonance spectrum of the ultrasonic data to detect microcracking and for porosity detection thermographically are presented. Methods involving supervised and unsupervised artificial neural networks as well as other clustering algorithms are developed for signal identification.

Walker, James L.↗
NASA NTRS2002

2001 NASA-ODU American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

Since 1964, the National Aeronautics and Space Administration (NASA) has supported a program of summer faculty fellowships for engineering and science educators. In a series of collaborations between NASA research and development centers and nearby universities, engineering faculty members spend 10 weeks working with professional peers on research. The Summer Faculty Program Committee of the American Society for Engineering Education supervises these programs. Objectives: (1) To further the professional knowledge of qualified engineering and science faculty members; (2) To stimulate and exchange ideas between participants and NASA; (3) To enrich and refresh the research and teaching activities of participants' institutions; (4 To contribute to the research objectives of the NASA center. Program Description: College or university faculty members will be appointed as Research Fellows to spend 10 weeks in cooperative research and study at the NASA Langley Research Center. The Fellow will devote approximately 90 percent of the time to a research problem and the remaining time to a study program. The study program will consist of lectures and seminars on topics of interest or that are directly relevant to the Fellow's research topics. The lecture and seminar leaders wil be distinguished scientists and engineers from NASA, education and industry.

Tiwari, Surendra N.↗