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Nearly every professional would like to have a personal assistant to perform the physical and mental work of searching libraries for needed information. This paper describes a tool which performs this function using commonly available Internet services. The promises and difficulties of developing such a tool are described.
The practical application of scripting languages and World Wide Web tools to the support of spacecraft ground system automation, is reported on. The mission activities and the automation tools used at the Goddard Space Flight Center (MD) are reviewed. The use of the Tool Command Language (TCL) and the Practical Extraction and Report Language (PERL) scripting tools for automating mission operations is discussed together with the application of different tools for the Compton Gamma Ray Observatory ground system.
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.
This final report details the contract performance and analysis of research and development results obtained during the contract period. KT-Tech's research and development work results in the areas of registration of remotely sensed data and the test evaluation and porting of the Regional Validation Center software system are presented.
NASA Goddard Space Flight Center is developing a class of satellites called nano-satellites. The technologies developed for these satellites will enable a class of constellation missions for the NASA Space Science Sun-Earth Connections theme and will be of great benefit to other NASA enterprises. A major challenge for these missions is meeting significant scientific- objectives with limited onboard and ground-based resources. Total spacecraft power is limited by the small satellite size. Additionally, it is highly desirable to minimize operational costs by limiting the ground support required to manage the constellation. This paper will describe how these challenges are met in the design of the nanosat power system. We will address the factors considered and tradeoffs made in deriving the nanosat power system architecture. We will discuss how incorporating onboard fault detection and correction capability yields a robust spacecraft power bus without the mass and volume penalties incurred from redundant systems and describe how power system efficiency is maximized throughout the mission duration.
We present metrics for quantifying organizational structures of complex control systems intended for controlling long-lived robotic or other autonomous applications commonly found in space applications. Such advanced control systems are often called integration platforms or agent architectures. Reported metrics span concerns about time, resources, software engineering, and complexities in the world.
This final report details the contract performance and analysis of research and development results obtained during the contract period. KT-TECH's research and development work results in the areas of registration of remotely sensed data and the test evaluation and porting of the Regional Validation Center software system, are presented.
We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.
We are on the path to meet the major challenges ahead for TCAD (technology computer aided design). The emerging computational grid will ultimately solve the challenge of limited computational power. The Modular TCAD Framework will solve the TCAD software challenge once TCAD software developers realize that there is no other way to meet industry's needs. The modular TCAD framework (MTF) also provides the ideal platform for solving the TCAD model challenge by rapid implementation of models in a partial differential solver.
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.
NASA and the Computer Science Research community are poised to enter a critical era. An era in which - it seems - that each needs the other. Market forces, driven by the immediate economic viability of computer science research results, place Computer Science in a relatively novel position. These forces impact how research is done, and could, in worst case, drive the field away from significant innovation opting instead for incremental advances that result in greater stability in the market place. NASA, however, requires significant advances in computer science research in order to accomplish the exploration and science agenda it has set out for itself. NASA may indeed be poised to advance computer science research in this century much the way it advanced aero-based research in the last.
Testing involves commercial radio equipment approved for export and use in Canada. Testing was conducted in the Canadian High Arctic, where hilly terrain provided the worst-case testing. SFU and Canadian governmental agencies made significant technical contributions. The only technical data related to radio testing was exchanged with SFU. Test protocols are standard radio tests performed by communication technicians worldwide. The Joint Fields Operations objectives included the following: (1) to provide Internet communications services for field science work and mobile exploration systems; (2) to evaluate the range and throughput of three different medium-range radio link technologies for providing coverage of the crater area; and (3) to demonstrate collaborative software such as NetMeeting with multi-point video for exchange of scientific information between remote node and base-base camp and science centers as part of communications testing.
In this contract, which is a component of a larger contract that we plan to submit in the coming months, we plan to study the preprocessing issues which arise in applying natural language processing techniques to NASA-KSC problem reports. The goals of this work will be to deal with the issues of: a) automatically obtaining the problem reports from NASA-KSC data bases, b) the format of these reports and c) the conversion of these reports to a format that will be adequate for our natural language software. At the end of this contract, we expect that these problems will be solved and that we will be ready to apply our natural language software to a text database of over 1000 KSC problem reports.
Simulation and visualization of rover behavior are critical capabilities for scientists and rover operators to construct, test, and validate plans for commanding a remote rover. The VIPER system links these capabilities. using a high-fidelity virtual-reality (VR) environment. a kinematically accurate simulator, and a flexible plan executive to allow users to simulate and visualize possible execution outcomes of a plan under development. This work is part of a larger vision of a science-centered rover control environment, where a scientist may inspect and explore the environment via VR tools, specify science goals, and visualize the expected and actual behavior of the remote rover. The VIPER system is constructed from three generic systems, linked together via a minimal amount of customization into the integrated system. The complete system points out the power of combining plan execution, simulation, and visualization for envisioning rover behavior; it also demonstrates the utility of developing generic technologies. which can be combined in novel and useful ways.
One of the most significant new opportunities that the Space Station affords cell biologists is the ability to do long-term cultivation of cells in the space environment. This facility is essential for investigations that are primarily focused on effects requiring a longer timeline of observation than that provided by the STS (Space Transportation System) platform. Such work requires both very strong laboratory skills to properly and quickly interact with the hardware hosting the culture and deep knowledge of the cell biology domain in order to optimally react to unanticipated scientific developments. Such work can be enabled by advanced automation techniques that have recently been used in the STS-based Spacelab, and that are being readied for the Space Station. In this paper, we describe the adaptation of PI-in-a-Box, the first interactive space science assistant system, to the study of the effects of space flight on cell cycle progression and proliferation.
This discussion will include a short review of the challenges that must be overcome if computational physics technology is to have a larger impact on the design cycles of U.S. aerospace companies. Some of the potential solutions to these challenges may come from the information sciences fields. A few examples of potential computational physics/information sciences synergy will be presented, as motivation and inspiration for the Improving The CFD Applications Process Workshop.
Experimental studies were conducted with pilots to investigate the attributes of automation that would be appropriate for aiding pilots in emergencies. The specific focus of this year was on methods of mitigating automation brittleness. Brittleness occurs when the automatic system is used in circumstances it was not designed for, causing it to choose an incorrect action or make an inaccurate decision for the situation. Brittleness is impossible to avoid since it is impossible to predict every potential situation the automatic system will be exposed to over its life. However, operators are always ultimately responsible for the actions and decisions of the automation they are monitoring or using, which means they must evaluate the automation's decisions and actions for accuracy. As has been pointed out, this is a difficult thing for human operators to do. There have been various suggestions as to how to aid operators with this evaluation. In the study described in this report we studied how presentation of contextual information about an automatic system's decision might impact the ability of the human operators to evaluate that decision. This study focused on the planning of emergency descents. Fortunately, emergencies (e.g., mechanical or electrical malfunction, on-board fire, and medical emergency) happen quite rarely. However, they can be catastrophic when they do. For all predictable or conceivable emergencies, pilots have emergency procedures that they are trained on, but those procedures often end with 'determine suitable airport and land as quickly as possible.' Planning an emergency descent to an unplanned airport is a difficult task, particularly under the time pressures of an emergency. Automatic decision aids could be very efficient at the task of determining an appropriate airport and calculating an optimal trajectory to that airport. This information could be conveyed to the pilot through an emergency descent procedure listing all of the actions necessary to safely land the plane. However, there is still the potential problem of brittleness. This study examined the impact of contextual information in presentations of emergency descent procedures to see if they might impact the pilot's evaluation of the feasibility of the presented procedure. The study and its results are described in detail.
Open Sesame! is the first commercial software product that learns user's behavior, and offers automation and coaching suggestions to the user. The neural learning module looks for repetitive patterns that have not been automated; when it finds one, it creates an observation and, upon approval, automates the task. The manufacturer, Charles River Analytics, credits Langley Research Center and Johnson Space Center Small Business Innovation Research grants and the time the president and vice president spent at the two centers in the 1970s as being essential to the development of their product line.
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