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检索条件"主题词=distributed algorithms"

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Agent-Based Approach to distributed Information and Control System Reconfiguration

Agent-Based Approach to Distributed Information and Control ...

引用

Conference on Computational Methods in Systems and Software (CoMeSySo)

作者： Melnik, Eduard Klimenko, Anna RAS SSC Informat Technol & Control Proc Dept Rostov Na Donu Russia Southern Fed Univ Sci Res Inst Multiprocessor Comp Syst Taganrog Russia

ISBN: (纸本)9783319676180

A problem of distributed information and control system (DICS) dependability is relevant nowadays. The DICSs with the performance redundancy and decentralized dispatching implemented by multiagent system are in the scope of this paper. The peculiarities of this class of DICSs require special approaches to the monitoring, control and reconfiguration procedures. The agent-based approach to the DICS reconfiguration is represented. It takes into account a potential of performance redundancy in the aspect of fault-tolerance. The configuration forming problem is solved when it is required by local interactions between software agents via constraint satisfaction problem solving. The paper contains the brief review of the DICS dependability problem and related works, description of performance redundancy and reconfiguration distributed procedure. Two-step agent-based approach to DICS reconfiguration described in details, algorithms of agent interactions and some simulation results are represented and discussed briefly.

关键词： Agent interaction distributed method distributed algorithms Reconfiguration distributed information and control system Control system

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Resilient Information Architecture Platform for distributed Linear State Estimation

Resilient Information Architecture Platform for Distributed ...

引用

IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Krishnan, V. V. G. Gopal, S. Liu, R. Nie, Z. Srivastava, A. Bakken, D. Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

ISBN: (纸本)9781538677032

Remedial Action Schemes (RAS) provide automatic control action with high impact on system performance. In order to improve the resilience of the RAS against any single node failure and to guarantee the secured and reliable operation of the power systems, distributed implementations of the RAS is researched upon. The input data to such a distributed RAS must be of high quality. In addition, RAS operation must be fast. Traditional centralized state estimation, which feeds data to RAS is slow and cannot meet the requirements of RAS. New approach needs to be developed to provide the fast and accurate data to RAS. In order to solve this problem, distributed state estimation is developed as an alternative strategy to feed data to the RAS. This paper discusses the implementation of distributed Linear State Estimation (DLSE) in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS). The DLSE algorithm is fully implemented in RIAPS platform and validated on a real-time testbed consisting of Real Time Digital Simulator, Phasor Measurement Units and BeagleBones. The effectiveness of the proposed approach is validated through online simulations on IEEE 14-bus test system under various cyber failures.

关键词： distributed algorithms Decentralized Control Smart Grid State Estimation

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Asymptotically Optimal distributed Gateway Load-Balancing for the Internet of Things

Asymptotically Optimal Distributed Gateway Load-Balancing fo...

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International Conference on the Network of the Future (NOF)

作者： Ilai Bistritz Nicholas Bambos Department of Electrical Engineering Stanford University Stanford CA

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

Consider a network of N sensors that collect data. Each sensor transmits this data to one out of G gateways. The gateways perform the preprocessing of the data and store it in the cloud. In order not to lose data, sensors need to route their packets to the gateway that has higher chances of successfully receiving them. However, this might overload some gateways that are the best gateway for many sensors, forcing them to drop packets. We design an asynchronous distributed algorithm where each sensor randomizes its destination, taking into account both the transmission success probabilities and the average input data rate of each gateway. Sensors estimate the transmission success probabilities online by sending pilot sequences and gateways broadcast the input data rates to all sensors. We show that our algorithm converges to a close to optimal solution. Specifically, when the number of sensors N approaches infinity, the ratio of the total throughput of our algorithm to the optimal throughput converges in probability to one.

关键词： Sensors Logic gates Throughput Load management distributed algorithms Bandwidth Internet of Things

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DisTenC: A distributed Algorithm for Scalable Tensor Completion on Spark 34

DisTenC: A Distributed Algorithm for Scalable Tensor Complet...

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34th IEEE International Conference on Data Engineering Workshops (ICDEW)

作者： Ge, Hancheng Zhang, Kai Alfifi, Majid Hu, Xia Caverlee, James Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781538655207

How can we efficiently recover missing values for very large-scale real-world datasets that are multi-dimensional even when the auxiliary information is regularized at certain mode? Tensor completion is a useful tool to recover a low-rank tensor that best approximates partially observed data and further predicts the unobserved data by this low-rank tensor, which has been successfully used for many applications such as location-based recommender systems, link prediction, targeted advertising, social media search, and event detection. Due to the curse of dimensionality, existing algorithms for tensor completion that integrate auxiliary information do not scale for tensors with billions of elements. In this paper, we propose DISTENC, a new distributed large-scale tensor completion algorithm that can be distributed on Spark. Our key insights are to (i) efficiently handle trace-based regularization terms;(ii) update factor matrices with caching;and (iii) optimize the update of the new tensor via residuals. In this way, we can tackle the high computational costs of traditional approaches and minimize intermediate data, leading to order-of-magnitude improvements in tensor completion. Experimental results demonstrate that DISTENC is capable of handling up to 10 similar to 1000x larger tensors than existing methods with much faster convergence rate, shows better linearity on machine scalability, and achieves up to an average improvement of 23.5% in accuracy in applications.

关键词： Tensile stress Matrix decomposition Scalability Sparks distributed algorithms Social network services Motion pictures

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Achieving Acceleration in distributed Optimization via Direct Discretization of the Heavy-Ball ODE

Achieving Acceleration in Distributed Optimization via Direc...

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American Control Conference

作者： Jingzhao Zhang Cesar A. Uribe Aryan Mokhtari Ali Jadbabaie Systems and Society (IDSS) Laboratory for Information and Decision Systems (LIDS) The Institute for Data Massachusetts Institute of Technology 77 Massachusetts Ave Cambridge MA 02139

ISBN: (纸本)9781538679012;9781538679265

We develop a distributed algorithm for convex Empirical Risk Minimization, the problem of minimizing large but finite sum of convex functions over networks. The proposed algorithm is derived from directly discretizing the second-order heavy-ball differential equation and results in an accelerated convergence rate, i.e., faster than distributed gradient descent-based methods for strongly convex objectives that may not be smooth. Notably, we achieve acceleration without resorting to the well-known Nesterov's momentum approach. We provide numerical experiments and contrast the proposed method with recently proposed optimal distributed optimization algorithms.

关键词： acceleration Convexity distributed algorithms Optimization algorithms Distribute Discretization accelerating convergence Resorts Convex function

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distributed Global Optimization by Annealing

Distributed Global Optimization by Annealing

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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Brian Swenson Soummya Kart H. Vincent Poor José M. F. Moura Princeton University Princeton NJ Pittsburgh PA

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

The paper considers distributed global minimization of a nonconvex function. We study a first-order consensus + innovations type algorithm that incorporates decaying additive Gaussian noise for annealing to converge to the set of global minima under certain technical assumptions. The paper presents simple methods for verifying that the required technical assumptions hold and illustrates it with a distributed target-localization application.

关键词： Convergence Optimization Annealing Technological innovation distributed algorithms Space exploration Laplace equations

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distributed Quantized Weight-Balancing and Average Consensus Over Digraphs 57

Distributed Quantized Weight-Balancing and Average Consensus...

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57th IEEE Conference on Decision and Control (CDC)

作者： Lee, Chang-Shen Michelusi, Nicol O. Scutari, Gesualdo Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781538613955

This paper studies distributed quantized weight-balancing and average consensus over fixed digraphs. A digraph with non-negative weights associated to its edges is weight-balanced if, for each node, the sum of the weights of its outgoing edges is equal to that of its incoming edges. We propose and analyze the first distributed algorithm that solves the weight-balancing problem using only quantized (one-bit) information among nodes and simplex communications (compliant to the directed nature of the graph edges). Asymptotic convergence of the scheme is proved and a convergence rate analysis is provided. Building on this result, a novel distributed algorithm is proposed that solves the average consensus problem over digraphs, using, at each iteration, only two-bit simplex communications between adjacent nodes - one bit for the weight-balancing problem, the other for the average consensus. Convergence to the average of the real (i.e., unquantized) node's initial values is proved, both almost surely and in mean square sense. Finally, numerical results validate our theoretical findings.

关键词： Convergence distributed algorithms Optimization Consensus algorithm Buildings Reliability Quantization (signal)

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distributed Set-Theoretic Parameter Estimation in Networks with Ambiguous Measurements 19

Distributed Set-Theoretic Parameter Estimation in Networks w...

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IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Ampeliotis, Dimitris Mavrokefalidis, Christos Berberidis, Kostas Theodoridis, Sergios Univ Patras Dept Comp Engn & Informat Rion 26500 Greece CTI RU 2 Rion 26500 Greece Univ Athens Dept Informat & Telecommun Athens 15784 Greece

ISBN: (纸本)9781538635124

distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. These ambiguities may be due to interference, poor calibration, high noise levels or any other cause. Cooperation among the nodes is required to resolve them. In such a setting, non-convex constraint sets may be required at the nodes, in order to accurately model the local ambiguities. We propose to handle the resulting non-convexity by expressing the involved non-convex sets as unions of convex sets, such that, for each node, only one such convex set is actually relevant to the estimation task. A procedure is employed in which the nodes properly select one of their convex sets with the aim to reach network-wide agreement. In this formulation, agreement can be reached when the nodes select sets with non-empty intersection. To this end, a learning algorithm is proposed and then a sketch of a proof for its convergence under some mild conditions is carried out. Finally, proper numerical results that support the theoretical findings are shown.

关键词： Estimation Task analysis Convergence distributed algorithms Parameter estimation Signal processing algorithms Probability density function"

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distributed Output Formation Control for Linear Swarm Systems with Time-Varying Formation and Directed Topology 14

Distributed Output Formation Control for Linear Swarm System...

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14th IEEE International Conference on Control and Automation (ICCA)

作者： Wang, Rui Dong, Xiwang Xiao, Wei Li, Qingdong Ren, Zhang Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

ISBN: (纸本)9781538660898

distributed time-varying output formation (TVOF) control problems are addressed in this paper using an adaptive output-feedback approach for general linear swarm systems with directed interaction topology. Compared with the previous work on formation control, a desired TVOF can be achieved by the swarm system without using any global information about the interaction topology. Firstly, an adaptive formation controller is constructed via dynamic output feedback information and sequential observers. Then, a distributed algorithm is proposed to determine the parameters in the adaptive formation controller. A feasible TVOF set is given. Moreover, the stability of the proposed distributed algorithm is proved. Finally, a simulation example is presented to demonstrate the effectiveness of the obtained results.

关键词： Topology Observers Adaptive systems distributed algorithms Eigenvalues and eigenfunctions Output feedback Matrix decomposition"

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distributed optimal transport for the deployment of swarms 57

Distributed optimal transport for the deployment of swarms

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57th IEEE Conference on Decision and Control (CDC)

作者： Krishnan, Vishaal Martinez, Sonia Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781538613955

The analysis and design of scalable distributed algorithms for spatial deployment is an important problem in the area of multi-robot systems. For very large swarms, this can be prescribed via macroscopic objectives on the behavior of the swarm, and accomplished by local sensing and communication between agents. In this paper, we address the problem of distributed optimal transport, with the aim of minimizing the cost of deployment of large swarms. Working with a macroscopic PDE model of swarms given by the continuity equation, we first formulate a general deployment objective, and formulate deployment algorithms as convergent gradient flows. Then, we design and analyze a novel Laplacian-based distributed algorithm and a corresponding weighted gradient flow for optimal transport. We conclude the manuscript with simulations that illustrate our results.

关键词： distributed algorithms Probability distribution Measurement Robot sensing systems Mathematical model Multi-robot systems

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