A typical microgrid consists of local power plants, renewable sources, energy storage systems, loads and communication units. It is designed to be operated autonomously or attached to a main utility grid, recognized a...
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ISBN:
(纸本)9781538614280
A typical microgrid consists of local power plants, renewable sources, energy storage systems, loads and communication units. It is designed to be operated autonomously or attached to a main utility grid, recognized as a promising application for energy system integration. This paper studies the optimal energy scheduling problem in microgrids which minimizes the total operational cost, subjecting to a number of operational and technical constraints. The proposed simulation framework is compacted to a mixed logical dynamical system which has advantages in terms of computational efficiency, compared with heuristic-based models. A case study of the optimal scheduling is performed on a centralized microgrid, and result shows the effectiveness of the proposed model in both grid-connected and islanded modes.
The difficulty in allocating virtual machines (VMs) on servers stems from the requirement that sufficient resources (such as CPU capacity and network bandwidth) must be available for each VM in the event of a failure ...
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ISBN:
(纸本)9781467359443
The difficulty in allocating virtual machines (VMs) on servers stems from the requirement that sufficient resources (such as CPU capacity and network bandwidth) must be available for each VM in the event of a failure or maintenance work as well as for temporal fluctuations of resource demands, which often exhibit periodic patterns. We propose a mixed integer programming approach that considers the fluctuations of the resource demands for optimal and dependable allocation of VMs. At the heart of the approach are techniques for optimally partitioning the time-horizon into intervals of variable lengths and for reliably estimating the resource demands in each interval. We show that our new approach allocates VMs successfully in a cloud computing environment in a financial company, where the dependability requirement is strict and there are various types of VMs exist.
We consider optimal filter-and-forward (FF) beam-forming (BF) in multi-user peer-to-peer (MUP2P) relay networks with frequency selective fading in which the signals received at a destination are subject to severe inte...
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ISBN:
(纸本)9781467355766
We consider optimal filter-and-forward (FF) beam-forming (BF) in multi-user peer-to-peer (MUP2P) relay networks with frequency selective fading in which the signals received at a destination are subject to severe inter-symbol interference (ISI) and multi-user interference (MUI). In the FF protocol, the amount of interference not only depends on the choice of the filter weights at the relays but also on the choice of a decoding delay at the destinations. State-of-the-art methods assume the network to be perfectly synchronized and select the same fixed decoding delay for all destinations. In a practical MUP2P scenario, however, users experience different path delays not only with respect to different relays but also with respect to other users. Hence, unlike in a conventional cellular network, perfect synchronization is impossible in practice and a common decoding delay for all users is not a good'choice. Therefore, we propose to jointly optimize the filter weights at the relays and the individual decoding delays at the destinations to effectively mitigate ISI and MUI. The proposed method outperforms the existing approaches most significantly in asynchronous networks.
Graph-based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems, particularly in agnostic settings when no parametric information or other prior knowledge i...
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Graph-based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems, particularly in agnostic settings when no parametric information or other prior knowledge is available about the data distribution. Given the constructed graph represented by a weight matrix, transductive inference is used to propagate known labels to predict the values of all unlabeled vertices. Designing a robust label diffusion algorithm for such graphs is a widely studied problem and various methods have recently been suggested. Many of these can be formalized as regularized function estimation through the minimization of a quadratic cost. However, most existing label diffusion methods minimize a univariate cost with the classification function as the only variable of interest. Since the observed labels seed the diffusion process, such univariate frameworks are extremely sensitive to the initial label choice and any label noise. To alleviate the dependency on the initial observed labels, this article proposes a bivariate formulation for graph-based SSL, where both the binary label information and a continuous classification function are arguments of the optimization. This bivariate formulation is shown to be equivalent to a linearly constrained Max-Cut problem. Finally an efficient solution via greedy gradient Max-Cut (GGMC) is derived which gradually assigns unlabeled vertices to each class with minimum connectivity. Once convergence guarantees are established, this greedy Max-Cut based SSL is applied on both artificial and standard benchmark data sets where it obtains superior classification accuracy compared to existing state-of-the-art SSL methods. Moreover, GGMC shows robustness with respect to the graph construction method and maintains high accuracy over extensive experiments with various edge linking and weighting schemes.
In this article we consider a difficult combinatorial optimization problem arising from the operation of a system for testing electronic circuit boards (ECB). This problem was proposed to us by a company that makes a ...
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In this article we consider a difficult combinatorial optimization problem arising from the operation of a system for testing electronic circuit boards (ECB). This problem was proposed to us by a company that makes a system for testing ECBs and is looking for an efficient way of planning the tests on any given ECB. Because of its difficulty, we first split the problem into a covering subproblem and a sequencing subproblem. We also give a global formulation of the test planning problem. Then we present and discuss results pertaining to the covering and sequencing subproblems. These results demonstrate that their solution yields testing plans that are much better than those currently used by the company. Finally we conclude our article by outlining avenues for future research.
In this paper, we consider a fuzzy multi-choice linear programming problem where some of the parameters and decision variables are trapezoidal type fuzzy numbers. In order to defuzzify a general fuzzy quantity the con...
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In this paper, we consider a fuzzy multi-choice linear programming problem where some of the parameters and decision variables are trapezoidal type fuzzy numbers. In order to defuzzify a general fuzzy quantity the concept of nearest trapezoidal fuzzy number is introduced. By assuming all the decision variables as trapezoidal fuzzy number, the objective function and the left hand side of constraints are approximated to their nearest trapezoidal fuzzy number. Interpolating polynomials are formulated for all multi-choice type parameters. Multi-choice type parameters are replaced by polynomials with integer variables. Then an equivalent multi-objective non linear programming problem is established. First and second objective functions represent left and right modal values of the trapezoidal type fuzzy number, where as the third and fourth objective functions represent the left and right spreads of the trapezoidal type fuzzy number. By applying lexicographic method optimal solution is obtained. In addition, a case study on a garment manufacture company is presented to demonstrate the solution procedure.
Optimization software provides better design and development of optimization solutions for real-life problems. The software generates different solutions under different constraints. An attempt has been made to compar...
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Optimization software provides better design and development of optimization solutions for real-life problems. The software generates different solutions under different constraints. An attempt has been made to compare three well-known optimization solvers. The comparisons have been done in terms of capabilities and problem domain. The results reveal that CPLEX and GuRoBi provide competitive optimization solutions. However, CPLEX performs better than GuRoBi under high dimensionality problems. Besides this, CPLEX is able to solve Non-convex mixedinteger quadratic problem.
In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem...
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In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem to a linear model or others. The numerical results show that the new algorithm gives a satisfactory performance in which it works of high speed and accuracy in IP/MIP problems.
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