114,376 research outputs found

    A methodology for the decomposition of discrete event models for parallel simulation

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    Parallel simulation has presented the possibility of performing high-speed simulation. However, when attempting to make a link between the requirements of parallel simulation and discrete event simulation used in commercial areas such as manufacturing, a major problem arises. This lies in the decomposition of the simulation into a series of concurrently executing objects. Using the activity cycle diagram simulation technique as an illustrative example, this paper suggests a solution to this decomposition problem. This is discussed within the context of providing a conceptually seamless methodology for translating simulation models into a form which can exploit the benefits of parallel computing

    On Maximum Margin Hierarchical Classification

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    We present work in progress towards maximum margin hierarchical classification where the objects are allowed to belong to more than one category at a time. The classification hierarchy is represented as a Markov network equipped with an exponential family defined on the edges. We present a variation of the maximum margin multilabel learning framework, suited to the hierarchical classification task and allows efficient implementation via gradient-based methods. We compare the behaviour of the proposed method to the recently introduced hierarchical regularized least squares classifier as well as two SVM variants in Reuter's news article classification

    Proportional-integral-plus control applications of state-dependent parameter models

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    This paper considers proportional-integral-plus (PIP) control of non-linear systems defined by state-dependent parameter models, with particular emphasis on three practical demonstrators: a microclimate test chamber, a 1/5th-scale laboratory representation of an intelligent excavator, and a full-scale (commercial) vibrolance system used for ground improvement on a construction site. In each case, the system is represented using a quasi-linear state-dependent parameter (SDP) model structure, in which the parameters are functionally dependent on other variables in the system. The approach yields novel SDP-PIP control algorithms with improved performance and robustness in comparison with conventional linear PIP control. In particular, the new approach better handles the large disturbances and other non-linearities typical in the application areas considered

    An Alternative Approach to the Calculation and Analysis of Connectivity in the World City Network

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    Empirical research on world cities often draws on Taylor's (2001) notion of an 'interlocking network model', in which office networks of globalized service firms are assumed to shape the spatialities of urban networks. In spite of its many merits, this approach is limited because the resultant adjacency matrices are not really fit for network-analytic calculations. We therefore propose a fresh analytical approach using a primary linkage algorithm that produces a one-mode directed graph based on Taylor's two-mode city/firm network data. The procedure has the advantage of creating less dense networks when compared to the interlocking network model, while nonetheless retaining the network structure apparent in the initial dataset. We randomize the empirical network with a bootstrapping simulation approach, and compare the simulated parameters of this null-model with our empirical network parameter (i.e. betweenness centrality). We find that our approach produces results that are comparable to those of the standard interlocking network model. However, because our approach is based on an actual graph representation and network analysis, we are able to assess cities' position in the network at large. For instance, we find that cities such as Tokyo, Sydney, Melbourne, Almaty and Karachi hold more strategic and valuable positions than suggested in the interlocking networks as they play a bridging role in connecting cities across regions. In general, we argue that our graph representation allows for further and deeper analysis of the original data, further extending world city network research into a theory-based empirical research approach.Comment: 18 pages, 9 figures, 2 table

    From Small-Scale Dynamo to Isotropic MHD Turbulence

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    We consider the problem of incompressible, forced, nonhelical, homogeneous, isotropic MHD turbulence with no mean magnetic field. This problem is essentially different from the case with externally imposed uniform mean field. There is no scale-by-scale equipartition between magnetic and kinetic energies as would be the case for the Alfven-wave turbulence. The isotropic MHD turbulence is the end state of the turbulent dynamo which generates folded fields with small-scale direction reversals. We propose that the statistics seen in numerical simulations of isotropic MHD turbulence could be explained as a superposition of these folded fields and Alfven-like waves that propagate along the folds.Comment: kluwer latex, 7 pages, 7 figures; Proceedings of the International Workshop "Magnetic Fields and Star Formation: Theory vs. Observations", Madrid, 21-25 April 2003 -- published version (but the e-print is free of numerous typos introduced by the publisher
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