4,015 research outputs found

    Isolated Horizon, Killing Horizon and Event Horizon

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    We consider space-times which in addition to admitting an isolated horizon also admit Killing horizons with or without an event horizon. We show that an isolated horizon is a Killing horizon provided either (1) it admits a stationary neighbourhood or (2) it admits a neighbourhood with two independent, commuting Killing vectors. A Killing horizon is always an isolated horizon. For the case when an event horizon is definable, all conceivable relative locations of isolated horizon and event horizons are possible. Corresponding conditions are given.Comment: 14 pages, Latex, no figures. Some arguments tightened. To appear in Class. Quant. Gra

    Reflectionless analytic difference operators I. algebraic framework

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    We introduce and study a class of analytic difference operators admitting reflectionless eigenfunctions. Our construction of the class is patterned after the Inverse Scattering Transform for the reflectionless self-adjoint Schr\"odinger and Jacobi operators corresponding to KdV and Toda lattice solitons

    Fundamental Cycle of a Periodic Box-Ball System

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    We investigate a soliton cellular automaton (Box-Ball system) with periodic boundary conditions. Since the cellular automaton is a deterministic dynamical system that takes only a finite number of states, it will exhibit periodic motion. We determine its fundamental cycle for a given initial state.Comment: 28 pages, 6 figure

    Exploiting structure in piecewise affine identification of LFT systems

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    Identification of interconnected systems is a challenging problem in which it is crucial to exploit the available knowledge about the interconnection structure. In this paper, identification of discrete-time nonlinear systems composed by interconnected linear and nonlinear systems, is addressed. An iterative identification procedure is proposed, which alternates the estimation of the linear and the nonlinear components. Standard identification techniques are applied to the linear subsystem, whereas recently developed piecewise affine (PWA) identification techniques are employed for modelling the nonlinearity. A numerical example analyzes the benefits of the proposed structure-exploiting identification algorithm compared to applying black-box PWA identification techniques to the overall system

    A new algorithm for continuous-discrete filtering with randomly delayed measurements

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    This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory & Applications and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.The filtering of nonlinear continuous-discrete systems is widely applicable in real-life and extensive literature is available to deal with such problems. However, all of these approaches are constrained with the assumption that the current measurement is available at every time step, although delay in measurement is natural in real-life applications. To deal with this problem, we re-derive the conventional Bayesian approximation framework for solving the continuous-discrete filtering problems. In practice, the delay is often smaller than one sampling time, which is the main case considered here. During the filtering of such systems, the actual time of correspondence should be known for a measurement received at the kth time instant. In this paper, a simple and intuitively justified cost function is used to decide the time to which the measurement at kth time instant actually corresponds. The performance of the proposed filter is compared with a conventional filter based on numerical integration which ignores random delays for a continuousdiscrete tracking problem. We show that the conventional filter fails to track the target while the modification proposed in this paper successfully deals with random delays. The proposed method may be seen as a valuable addition to the tools available for continuous-discrete filtering in nonlinear systems

    Dual Resonance Model Solves the Yang-Baxter Equation

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    The duality of dual resonance models is shown to imply that the four point string correlation function solves the Yang-Baxter equation. A reduction of transfer matrices to AlA_l symmetry is described by a restriction of the KP τ\tau function to Toda molecules.Comment: 10 pages, LaTe

    Basic Representations of A_{2l}^(2) and D_{l+1}^(2) and the Polynomial Solutions to the Reduced BKP Hierarchies

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    Basic representations of A_{2l}^(2) and D_{l+1}^(2) are studied. The weight vectors are represented in terms of Schur's QQ-functions. The method to get the polynomial solutions to the reduced BKP hierarchies is shown to be equivalent to a certain rule in Maya game.Comment: January 1994, 11 page

    Novel classical ground state of a many body system in arbitrary dimensions

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    The classical ground state of a D- dimensional many body system with two and three body interactions is studied as a function of the strength of the three body interaction. We prove exactly that beyond a critical strength of the three body interaction, the classical ground state of the system is one in which all the particles are on a line. The positions of the particles in this string configuration are uniquely determined by the zeros of the Hermite polynomials.Comment: 4 pages, RevTeX, no figure; version to appear in Physical Review Letter

    A minimum variance filter for discrete time linear systems with parametric uncertainty

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    A minimum variance filter for a class of discrete time systems with additive as well as multiplicative noise is investigated in this paper. We extend the results from recent work by Ponomareva and Date to account for multiplicative noise in the measurement equation. More importantly, we provide an interpretation of the multiplicative noise in both transition and measurement equations in terms of parameter perturbations in a linear additive model. The utility of the proposed filtering algorithm is demonstrated through numerical simulation experiments using models from academic literature where the parameters are estimated from real data
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