Timing problem is an important application of distributed algorithm, but it still has many shortcomings in practical engineering, such as traffic flow information cannot be effectively transmitted. The purpose of this...
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The situation of multi-region problem may often appear when boundary element method (BEM) is applied in practical problems especially in *** is difficult to deal with this problem if traditional methods are used. Part...
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The situation of multi-region problem may often appear when boundary element method (BEM) is applied in practical problems especially in *** is difficult to deal with this problem if traditional methods are used. Particularly,when the problem to be solved contains a lot of materials, the advantages of usingBEM such as simplicity) convenience and rapidity will be weakened due to the complexity of solving complex boundary element equation. In this paper a distributedalgorithm for multi-region problem in BEM is presented. This algorithm has beenimplemented in a distributed system consisting of 3 workstations to extract VLSIlayout parameters. The results show that the calculation time of this distributedalgorithm is less than that of the traditional methods. The results also demonstratethat this algorithm can speed up the computation and has the features of parallelismand high efficiency.
Regulating capacity shortage will impose negative impact on the accommodation of renewable energy in a power system. Especially in off-peak load periods, the accommodation of wind power is highly dependent on the down...
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Regulating capacity shortage will impose negative impact on the accommodation of renewable energy in a power system. Especially in off-peak load periods, the accommodation of wind power is highly dependent on the downward regulation capability of the power system. To encourage the provision of the downward regulation services and reasonably evaluate their market value, this paper presents a downward regulation market where power generators and demand response resources compete by submitting bids to meet the demand of downward regulation capacity in off-peak load periods. To investigate the bidding strategies in the downward regulation market, a game-theoretic equilibrium model is developed, in which supply function bid is considered. The equilibrium model can be transformed into a convex optimization model based on the equivalence of the optimality condition. Then, a distributed algorithm is applied to solve the convex optimization model and achieve equilibrium results. Numerical results illustrate the effectiveness of the proposed model and algorithm.
Recent developments in mobile networks have looked at placing more autonomy and intelligence at base stations to encourage cooperation between them using a distributed approach in order to try and develop a network mo...
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Recent developments in mobile networks have looked at placing more autonomy and intelligence at base stations to encourage cooperation between them using a distributed approach in order to try and develop a network more robust to attacks and failures. A real time fully distributed algorithm that would allow base stations in cellular networks to autonomously and cooperatively find the wireless coverage pattern to optimize network performance is described. The semi-smart antenna system is assumed at each base station. Simulations based on Mobile WiMAX technology show the effectiveness and robustness of the approach.
In wireless sensor networks, barrier coverage is a fundamental category of coverage problems and its surveillance capability can be reinforced when utilizing camera sensors. In this paper, we propose a distributed alg...
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ISBN:
(纸本)9781479959532
In wireless sensor networks, barrier coverage is a fundamental category of coverage problems and its surveillance capability can be reinforced when utilizing camera sensors. In this paper, we propose a distributed algorithm to solve the problem of Full-View Barrier Coverage with rotatable camera sensors (FBR). Our goal is to minimize the active sensor number when guaranteeing the surveillance capabilities. Correspondingly, we propose a distributed Proliferation algorithm (DPA) which is the first distributed algorithm to deal with such problem. In DPA, each sensor has five possible phases: Initial Phase, Update Phase, Expand Phase, Wait Phase, and Terminate Phase. The main idea of DPA is spreading the sensor direction along the barrier with the help of auxiliary conflicting graph. We design a greedy strategy based on DFS. Moreover, local Dijkstra algorithm is utilized to select the shortest path to reduce active sensor number from global view. The mass number of numerical experiments validate the efficiency of DPA which can construct a full-view barrier line with fewer sensors compared with previous work.
We consider the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at minimum total cost. We assume that each generator has a piece...
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We consider the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at minimum total cost. We assume that each generator has a piece-wise linear cost function. We first present a polynomial time algorithm that achieves optimal dispatch. We then present a decentralized algorithm where, each generator independently adjusts its power output using only the aggregate power imbalance in the network, which can be observed by each generator through local measurements of the frequency deviation on the grid. The algorithm we propose exponentially erases the power imbalance, while eventually minimizing the generation cost.
Hyperspectral image (HSI) is often disturbed by various kinds of noise, which brings great challenges to subsequent applications. Many of the existing restoration algorithms do not scale well for HSI with large size. ...
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Hyperspectral image (HSI) is often disturbed by various kinds of noise, which brings great challenges to subsequent applications. Many of the existing restoration algorithms do not scale well for HSI with large size. This article proposes a novel mixed-noise removal method for HSI with large size, by leveraging the superpixel segmentation-based technology and distributed algorithm based on graph signal processing (GSP). First, the underlying structure of the HSI is modeled by a two-layer architecture graph. The upper layer, called skeleton graph, is a rough graph constructed using the modified $k$ -nearest-neighborhood algorithm and its nodes correspond to a series of superpixels formed by HSI segmentation. The skeleton graph can efficiently characterize the intercorrelations between superpixels, while preserving the boundary information and reducing the computational complexity. The lower layer, called detailed graph consisting of a series of local graphs which are constructed to model the similarities between pixels. Second, based on the two-layer graph architecture, the HSI restoration problem is formulated as a series of optimization problems each of which resides on a subgraph. In each optimization problem, a graph Laplacian regularization (GLR) is defined and incorporated into a low-rank (LR)-based model. Third, a novel distributed algorithm is tailored for the restoration problem, using the information interaction between the nodes of skeleton graph and subgraphs. Numerical experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of the proposed restoration algorithm compared with existing methods.
With ridesourcing services gaining popularity in the past few years, there has been growing interest in algorithms that could enable real-time operation of these systems. As ridesourcing systems rely on independent en...
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With ridesourcing services gaining popularity in the past few years, there has been growing interest in algorithms that could enable real-time operation of these systems. As ridesourcing systems rely on independent entities to build the supply and demand sides of the market, they have been shown to operate more successfully in metropolitan areas where there is a high level of demand for rides as well as a high number of drivers, and a large volume of trips occurring within a geographically constrained region. Despite the suitable ecosystem that metropolitan areas offer for ridesourcing operations, there is a lack of methods that can provide high-quality matching solutions in real-time. To fill this gap, this paper introduces a framework that allows for solving the large-scale matching problems by means of solving smaller problems in a distributed fashion. The proposed methodology is based on constructing approximately uniform clusters of trip requests, where vehicle tours form cluster centers. Using the New York Taxi dataset, we compare the performance of the proposed methodology against three benchmark methods to showcase its advantages in terms of solution quality and solution time.
A distributed algorithm for finding a set of fundamental cycles is presented in this paper. The output of the algorithm is available in a distributed manner, i.e. when the algorithm terminates each node knows the list...
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A distributed algorithm for finding a set of fundamental cycles is presented in this paper. The output of the algorithm is available in a distributed manner, i.e. when the algorithm terminates each node knows the list of all the fundamental cycles passing through itself. The algorithm requires O(n) messages and O(n) units of time, where n is the number of nodes of the graph. It is shown that the algorithm is optimal in communication complexity to within a constant factor.
The generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where the data a...
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The generalized eigenvalue problem (GEP) plays a significant role in signal processing and machine learning. This paper proposes a consensus-based distributed algorithm for GEP in multi-agent systems, where the data are distributively stored across agents. The distributed GEP is reformulated as a consensus optimization, but the presence of its quadratic inseparable constraint makes the considered problem more challenging. To deal with it, a sequential method combined with the alternating direction method of multipliers is proposed, which requires communication between multiple pairs of nodes. Theoretical analysis shows the proposed algorithm will converge to the set of stationary solutions. And the numerical experiments on synthetic and real-world datasets validate that the approximated solution is competitive to the centered results.
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