We study a hybrid non-equilibrium pattern formation model combining short-scale Ising model with a continuous slow or long scale inhibitor. The computation combines alternates monte-carlo algorithm with updating of th...
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We study a hybrid non-equilibrium pattern formation model combining short-scale Ising model with a continuous slow or long scale inhibitor. The computation combines alternates monte-carlo algorithm with updating of the inhibitor field. It is very fast, and allows us to study the influence of various factors, such as scale ratios, coupling strength, bias, temperature (level of noise), anisotropy, etc., on the pattern formation and behavior of emerging non-equilibrium structures. (C) 2004 Elsevier B.V. All rights reserved.
In this paper, integrated operation management of cooperative microgrids is formulated in the framework of stochastic predictive control. In the proposed scheme, a joint probabilistic constraint on the microgrids powe...
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In this paper, integrated operation management of cooperative microgrids is formulated in the framework of stochastic predictive control. In the proposed scheme, a joint probabilistic constraint on the microgrids power exchange with the main grid couples operation of individual microgrids. In order to tackle the coupling constraint, a cooperative energy management strategy is proposed in which based on the statistical characteristics of uncertain parameters, the deterministic counterpart of the problem is derived and an efficient solution strategy is achieved. The proposed strategy is evaluated for an illustrative test case including two microgrids based on modified CIGRE benchmark. Moreover, statistical analysis is conducted to evaluate robustness characteristics of the solution strategy.
A computational system for reasoning about dynamic time-sliced systems using Bayesian networks is presented. The system, called dHugin, may be viewed as a generalization of the inference methods of classical discrete ...
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A computational system for reasoning about dynamic time-sliced systems using Bayesian networks is presented. The system, called dHugin, may be viewed as a generalization of the inference methods of classical discrete time-series analysis in the sense that it allows description of non-linear, discrete multivariate dynamic systems with complex conditional independence structures. The paper introduces the notions of dynamic time-sliced Bayesian networks, a dynamic time window, and common operations on the time window. Inference, pertaining to the time window and time slices preceding it, are formulated in terms of the well-known message passing scheme in junction trees. Backward smoothing, for example, is performed efficiently through inter-tree message passing. Further, the system provides an efficient monte-carlo algorithm for forecasting;i.e. inference pertaining to time slices succeeding the time window. The system has been implemented on top of the Hugin shell.
In many applications, it is of interest to approximate data, given by m x n matrix A, by a matrix B of at most rank k, which is much smaller than m and n. The best approximation is given by singular value decompositio...
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ISBN:
(纸本)9781424401871
In many applications, it is of interest to approximate data, given by m x n matrix A, by a matrix B of at most rank k, which is much smaller than m and n. The best approximation is given by singular value decomposition, which is too time consuming for very large m and n. We present here a montecarloalgorithm for iteratively computing a k-rank approximation to the data consisting of m x n matrix A. Each iteration involves the reading of O(k) of columns or rows of A. The complexity of our algorithm is O(kmn). Our algorithm, distinguished from other known algorithms, guarantees that each iteration is a better k-rank approximation than the previous iteration. We believe that this algorithm will have many applications in data mining, data storage and data analysis.
Due to the urgent demand for the monitoring of overhead transmission lines in smart grid, this paper proposes an optimal inspection strategy for overhead transmission line considering time-varying failure rate. Based ...
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Due to the urgent demand for the monitoring of overhead transmission lines in smart grid, this paper proposes an optimal inspection strategy for overhead transmission line considering time-varying failure rate. Based on the goal benefit function of inspection cost, failure electric cost and the maintaining reliability cost, modal of the inspection strategy is established with genetic algorithm. The reliability index could contribute to a dynamic and variable inspection cycle of transmission line via genetic algorithm. According to the calculation results of the middle area of China, it is presented that the algorithm can work out inspection strategy according to the actual failure rate, and get a rational and economical, regional, seasonal, dynamic inspection cycle plan. Thus, it can avoid the traditional over-inspection and lack-inspection problems. The state of transmission line can be guaranteed in more economical way, satisfying the demand of real-time capability and reliability (C) 2018 The Authors. Published by Elsevier B.V.
We study a hybrid non-equilibrium pattern formation model combining short-scale Ising model with a continuous slow or long scale inhibitor. The computation combines alternates monte-carlo algorithm with updating of th...
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We study a hybrid non-equilibrium pattern formation model combining short-scale Ising model with a continuous slow or long scale inhibitor. The computation combines alternates monte-carlo algorithm with updating of the inhibitor field. It is very fast, and allows us to study the influence of various factors, such as scale ratios, coupling strength, bias, temperature (level of noise), anisotropy, etc., on the pattern formation and behavior of emerging non-equilibrium structures. (C) 2004 Elsevier B.V. All rights reserved.
We study linear bilevel programming problems whose lower-level objective is given by a random cost vector with known distribution. We consider the case where this distribution is nonatomic, allowing to pose the proble...
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ISBN:
(纸本)9783031327254;9783031327261
We study linear bilevel programming problems whose lower-level objective is given by a random cost vector with known distribution. We consider the case where this distribution is nonatomic, allowing to pose the problem of the leader using vertex-supported beliefs in the sense of [29]. We prove that, under suitable assumptions, this formulation turns out to be piecewise affine over the so-called chamber complex of the feasible set of the high point relaxation. We propose two algorithmic approaches to solve general problems enjoying this last property. The first one is based on enumerating the vertices of the chamber complex. The second one is a monte-carlo approximation scheme based on the fact that randomly drawn points of the domain lie, with probability 1, in the interior of full-dimensional chambers, where the problem (restricted to this chamber) can be reduced to a linear program.
The inductance gradient is one of the most important indexes to evaluate the performance of the electromagnetic railgun. It has important theoretical and practical value to research the inductance gradient and its inf...
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ISBN:
(纸本)9781510813465
The inductance gradient is one of the most important indexes to evaluate the performance of the electromagnetic railgun. It has important theoretical and practical value to research the inductance gradient and its influencing parameters. According to the Biot-Savat's law and the principle of virtual work, the expression of the inductance gradient considered the skin effect of current has been given, and by using monte-carlo algorithm the numerical solution has been calculated. In order to verify the correctness and accuracy of the model, the finite element simulation software is used to compare with the numerical solution. Based on the model, the influencing parameters of inductance gradient has been analyzed, and the variation of the inductance gradient has been summarized. The results show that, the inductance gradient decrease with the separation distance between the inside and outside rail, decrease with the height and width of the rail, increase with the displacement of the armature then tend to constant. The research can provide a reference for the design of series-connected augmented railgun.
Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed-matter physics. In this paper basic properties and recent devel...
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Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed-matter physics. In this paper basic properties and recent developments of Chebyshev expansion based algorithms and the kernel polynomial method are reviewed. Characterized by a resource consumption that scales linearly with the problem dimension these methods enjoyed growing popularity over the last decade and found broad application not only in physics. Representative examples from the fields of disordered systems, strongly correlated electrons, electron-phonon interaction, and quantum spin systems are discussed in detail. In addition, an illustration on how the kernel polynomial method is successfully embedded into other numerical techniques, such as cluster perturbation theory or montecarlo simulation, is provided.
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