Status and Future of the FACT Network in the Northern Indian River Lagoon
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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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For over 20 years, the Cryogenics Test Laboratory at NASA Kennedy Space Center (KSC) (CSA CSM) has stood as a one-of-a kind capability for research, development, and application of cross-cutting technologies to meet both government and industry needs. With four overarching technology focus areas—Thermal insulation systems, Integrated refrigeration systems, Advanced propellant transfer systems, and Low-temperature materials and applications—and a theme of Energy Efficient Cryogenics, the Cryo Test Lab has pioneered technologies such as Integrated Refrigeration and Storage (IRAS) for advanced liquid hydrogen (LH2) operations; aerogel blanket insulation, the world’s best insulation material in ambient pressure and soft-vacuum; and the patented-pending Cryogenic Flux Capacitor3 for high-density storage, and on-demand supply of cryogenic fluids.
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This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.
In work done jointly with Toby Walsh, the author has provided a sound theoretical foundation to the process of reasoning with abstraction (GW90c, GWS9, GW9Ob, GW90a). The notion of abstraction formalized in this work can be informally described as: (property 1), the process of mapping a representation of a problem, called (following historical convention (Sac74)) the 'ground' representation, onto a new representation, called the 'abstract' representation, which, (property 2) helps deal with the problem in the original search space by preserving certain desirable properties and (property 3) is simpler to handle as it is constructed from the ground representation by "throwing away details". One desirable property preserved by an abstraction is provability; often there is a relationship between provability in the ground representation and provability in the abstract representation. Another can be deduction or, possibly inconsistency. By 'throwing away details' we usually mean that the problem is described in a language with a smaller search space (for instance a propositional language or a language without variables) in which formulae of the abstract representation are obtained from the formulae of the ground representation by the use of some terminating rewriting technique. Often we require that the use of abstraction results in more efficient .reasoning. However, it might simply increase the number of facts asserted (eg. by allowing, in practice, the exploration of deeper search spaces or by implementing some form of learning). Among all abstractions, three very important classes have been identified. They relate the set of facts provable in the ground space to those provable in the abstract space. We call: TI abstractions all those abstractions where the abstractions of all the provable facts of the ground space are provable in the abstract space; TD abstractions all those abstractions wllere the 'unabstractions' of all the provable facts of the abstract space are provable in the ground space; and TC abstractions all those abstractions where a fact is provable in the ground space if and only if its abstraction is provable in the abstract space.
The Flight Awareness Collaboration Tool (FACT) is a web-based software tool that provides important information about winter weather operations to airline dispatchers and airport personnel. This document provides instructions on how to operate FACT. It reviews FACT goals, features, functions, controls, and data displays. The manual uses text and screen shots of the screens to guide new users on how to access FACT features. This manual is required for FACT distribution to airlines and airports and is needed as part of the NASA patent process.
This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. This reduced complexity improves performance but without undo impacts during load and unload cycles. The second objective is to produce highly reliable intelligent systems. This requires simple and automated approaches to the KB verification & validation task. Partitioning the KB reduces rule interaction complexity overall. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. The practical approach is to cluster rules and facts into associated 'blocks'. A simple approach has been designed to control the addition and deletion of 'blocks' of rules and facts, while allowing real-time operations to run freely. Timing tests for real-time performance for specific machines under R/T operating systems have not been completed but are planned as part of the analysis process to validate the design.
Despite the fact that supercritical fluids occur both in nature and in industrial situations, the fundamentals of their behavior is poorly understood because supercritical fluids combine the characteristics of both liquids and gases, and therefore their behavior is not intuitive. There are several specific reasons for the lack of understanding: First, data from (mostly optical) measurements can be very misleading because regions of high density thus observed are frequently identified with liquids. A common misconception is that if in an experiment one can optically identify "drops" and "ligaments", the observed fluid must be in a liquid state. This inference is incorrect because in fact optical measurements detect any large change (i.e. gradients) in density. Thus, the density ratio may be well below Omicron(10(exp 3)) that characterizes its liquid/gas value, but the measurement will still identify a change in the index of refraction providing that the change is sudden (steep gradients). As shown by simulations of supercritical fluids, under certain conditions the density gradients may remain large during the supercritical binary fluids mixing, thus making them optically identifiable. Therefore, there is no inconsistency between the optical observation of high density regions and the fluids being in a supercritical state. A second misconception is that because a fluid has a liquid-like density, it is appropriate to model it as a liquid. However, such fluids may have liquid-like densities while their transport properties differ from those of a liquid. Considering that the critical pressure of most fuel hydrocarbons used in Diesel and gas turbine engines is in the range of 1.5 - 3 MPa, and the fact that the maximum pressure attained in these engines is about 6 Mps, it is clear that the fuel in the combustion chamber will experience both subcritical and supercritical conditions. Studies of drop behavior over a wide range of pressures were performed in the past, however none of these studies identified the crucial differences between the subcritical and supercritical behavior. In fact, in two of these studies, it was found that the subcritical and supercritical behavior is similar as the drop diameter decreased according to the classical d(exp 2)-law over a wide range of pressures and drop diameters. The present study is devoted to the exploration of differences in fluid-behavior characteristics under subcritical and supercritical conditions in the particular case of heptane fluid drops in nitrogen; these substances were selected because of the availability of experimental observations for model validation.
Nearly three decades ago as Voyager 2 spacecraft raced out of the Solar System. NASA engineers turned its camera arm around (at the request of the American astronomer Carl Sagan) to take a parting snapshot of Earth. Earth's image was a single pale blue pixel, its color caused by the Rayleigh scattering of sunlight in the water of our oceans. Earth is a water planet, and this is the color of life. No matter how far we travel on our planet, no matter how high or deep, if we find liquid water, we find some form of life that manages to survive there. And yet there is a cruel irony. Water in its solid crystalline form is hostile to life. Organisms can roost in geysers, wallow in brine and gulp down acid, but they cowered from ice. The rigid ordering of water molecules in ice crystals expels impurities and tears organic tissue beyond repair. In fact, about the only good thing you can say about ice is that it gets out of the way: Its low density ensures that it floats and leaves the water dwelling creatures in peace. Recent discoveries have caused us to rethink this basic premise. New lines of evidence both observational and experimental - suggest that prebiotic organic compounds are not only comfortable in, but in fact had their origin in a peculiar form of solid water ice that is ubiquitous in interstellar space, but completely absent from Earth. Only recently have we been able to create even submicroscopic quantities of this ice in terrestrial laboratories, yet it constitutes the most abundant form of water in the universe. Interstellar ice is a far cry from the ice we are so familiar with on Earth. This interstellar ice has no crystalline structure, and despite the fact that its temperature is a scant few degrees above absolute zero (where all molecular motion ceases), it is highly reactive and can flow like water when exposed to radiation. It is in fact this ice's similarity to liquid water that allows it to participate in the creation of the very first organic compounds.
Predicting the occurrence of human cancer following exposure to any agent causing genetic damage is a difficult task. This is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within any given clinically normal individual. The radiation health research priorities for enabling long-duration human exploration of space were established in the 1996 NRC Report entitled "Radiation Hazards to Crews of Interplanetary Missions: Biological Issues and Research Strategies". This report emphasized that a 15-fold uncertainty in predicting radiation-induced cancer incidence must be reduced before NASA can commit humans to extended interplanetary missions. That report concluded that the great majority of this uncertainty is biologically based, while a minority is physically based due to uncertainties in radiation dosimetry and radiation transport codes. Since that report, the biologically based uncertainty has remained large, and the relatively small uncertainty associated with radiation dosimetry has increased due to the considerations raised by concepts of microdosimetry. In a practical sense, however, the additional uncertainties introduced by microdosimetry are encouraging since they are in a direction of lowered effective dose absorbed through infrequent interactions of any given cell with the high energy particle component of space radiation. The biological uncertainty in predicting cancer risk for space radiation derives from two primary facts. 1) One animal tumor study has been reported that includes a relevant spectrum of particle radiation energies, and that is the Harderian gland model in mice. Fact #1: Extension of cancer risk from animal models, and especially from a single study in an animal model, to humans is inherently uncertain. 2) One human database is predominantly used for assessing cancer risk caused by space radiation, and that is the Japanese atomic bomb survivors. Fact #2: The atomic-bomb-survivor database, itself a remarkable achievement, contains uncertainties. These include the actual exposure to each individual, the radiation quality of that exposure, and the fact that the exposure was to acute doses of predominantly low-LET radiation, not to chronic exposures of high-LET radiation expected on long-duration interplanetary manned missions.
FACT is a software program that provides important information about winter weather operations to airline dispatchers and airport personnel. FACT has a "quad" design and shows various maps, text, and tabular information. It also has a team messaging capability. It is meant to be used by airline dispatchers and airport personnel to manage winter storms. This slide set provides a description of FACT that will be released to airline and airport partners who are helping to evaluate FACT. It was most recently presented at a Southwest Airlines meeting at NASA Ames on April 3, 2018.
FACT is a software program that provides important information about winter weather operations to airline dispatchers and airport personnel. FACT has a "quad" design and shows various maps, text, and tabular information. It also has a team messaging capability. It is meant to be used by airline dispatchers and airport personnel to manage winter storms. This slide set provides a description of FACT that will be released to airline and airport partners who are helping to evaluate FACT. It was most recently presented at a Southwest Airlines meeting at NASA Ames on April 3, 2018.
NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. We have formed two user teams from affected airports to guide the design and evaluate the web-based prototype. Future work includes adding more automated capabilities and conducting a simulation to evaluate FACT in a realistic environment.
NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. We have formed two user teams from affected airports to guide the design and evaluate the web-based prototype. Future work includes adding more automated capabilities and conducting a simulation to evaluate FACT in a realistic environment.
The feature selection problem is considered resulting from the transformation x = Bz where B is a k by n matrix of rank k and k is or = to n. Such a transformation can be considered to reduce the dimension of each observation vector z, and in general, such a transformation results in a loss of information. In terms of the divergence, this information loss is expressed by the fact that the average divergence D sub B computed using variable x is less than or equal to the average divergence D computed using variable z. If D sub B = D, then B is said to be a sufficient statistic for the average divergence D. If B is a sufficient statistic for the average divergence, then it can be shown that the probability of misclassification computed using variable x (of dimension k is or = to n) is equal to the probability of misclassification computed using variable z. Also included is what is believed to be a new proof of the well known fact that D is or = to D sub B. Using the techniques necessary to prove the above fact, it is shown that the Brattacharyya distance as measured by variable x is less than or equal to the Brattacharyya distance as measured by variable z.