1,614 research outputs found

    Monotone Pieces Analysis for Qualitative Modeling

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    It is a crucial task to build qualitative models of industrial applications for model-based diagnosis. A Model Abstraction procedure is designed to automatically transform a quantitative model into qualitative model. If the data is monotone, the behavior can be easily abstracted using the corners of the bounding rectangle. Hence, many existing model abstraction approaches rely on monotonicity. But it is not a trivial problem to robustly detect monotone pieces from scattered data obtained by numerical simulation or experiments. This paper introduces an approach based on scale-dependent monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. simulation results, can be partitioned into quasi-monotone segments. The end points for the monotone segments are used as the initial set of landmarks for qualitative model abstraction. The qualitative model abstraction works as an iteratively refining process starting from the initial landmarks. The monotonicity analysis presented here can be used in constructing many other kinds of qualitative models; it is robust and computationally efficient

    Rocket engine diagnostics using qualitative modeling techniques

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    Researchers at NASA Lewis Research Center are presently developing qualitative modeling techniques for automated rocket engine diagnostics. A qualitative model of a turbopump interpropellant seal system was created. The qualitative model describes the effects of seal failures on the system steady state behavior. This model is able to diagnose the failure of particular seals in the system based on anomalous temperature and pressure values. The anomalous values input to the qualitative model are generated using numerical simulations. Diagnostic test cases include both single and multiple seal failures

    Qualitative Scientific Modeling and Loop Analysis

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    Loop analysis is a method of qualitative modeling anticipated by Sewall Wright and systematically developed by Richard Levins. In Levins’ (1966) distinctions between modeling strategies, loop analysis sacrifices precision for generality and realism. Besides criticizing the clarity of these distinctions, Orzack and Sober (1993) argued qualitative modeling is conceptually and methodologically problematic. Loop analysis of the stability of ecological communities shows this criticism is unjustified. It presupposes an overly narrow view of qualitative modeling and underestimates the broad role models play in scientific research, especially in helping scientists represent and understand complex systems

    Representing functions/procedures and processes/structures for analysis of effects of failures on functions and operations

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    Current qualitative device and process models represent only the structure and behavior of physical systems. However, systems in the real world include goal-oriented activities that generally cannot be easily represented using current modeling techniques. An extension of a qualitative modeling system, known as functional modeling, which captures goal-oriented activities explicitly is proposed and how they may be used to support intelligent automation and fault management is shown

    Qualitative models for planning: A gentle introduction

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    Qualitative modeling is the study of how the physical world behaves. These physical models accept partial descriptions of the world and output the possible changes. Current systems assume that the model is static and that physical entities do not effect change into the world. An approach to planning in physical domains and a working implementation which integrates qualitative models with a temporal interval-based planner are described. The planner constructs plans involving physical qualities and their behavioral descriptions

    The Utility of Qualitative Modeling in Architectural Evaluation

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    Luminosities of High-Redshift Objects in an Accelerating Universe

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    The results from the Supernova Cosmology Project indicate a relation between cosmic distance and redshift that corresponds to an accelerating Universe, and, as a consequence, the presence of an energy component with negative pressure. This necessitates a re-evaluation of such astrophysical luminosities that have been derived through conventional redshift analyses of, e.g., gamma-ray bursts and quasars. We have calculated corrected luminosity distances within two scenarios; the standard one with a non-zero cosmological constant, and the more recently proposed ``quintessence'', with a slowly evolving energy-density component. We find luminosity corrections from +30 to -40 per cent for redshifts with z=0−10z=0 - 10. This finding implicates that the SCP data do not, by themselves, require a revision of the current, rather qualitative modeling of gamma-ray bursts and quasar properties.Comment: RevTeX, 4 pages, 2 postscript figures, submitted to PR
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