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PROCEEDINGS OF THE IEEE 1989年第12期77卷 1829-1841页

作者： HAYES, JP MUDGE, T Dept. of Electr. Eng. & Comput. Sci. Michigan Univ. Ann Arbor MI

The architecture and applications of the class of highly parallel distributed-memory multiprocessors based on the hypercube interconnection structure are surveyed. The history of hypercube computers from their conceptual origins in the 1960s to the recent introduction of commercial machines is briefly reviewed. The properties of hypercube graphs relevant to their use in supercomputers, including connectivity, routing, and embedding, are examined. The hardware and software characteristics of current hypercubes are discussed, with emphasis on the unique aspects of their operating systems and programming languages. A sample C program is presented to illustrate the single-code, multiple-data programming style typical of distributed-memory machines in general, and hypercube applications in particular. Two contrasting hypercube applications are presented and analyzed: image processing and branch-and-bound optimization. Current trends are discussed

关键词： C listings C program SCMD program branch-and-bound optimization computerised picture processing connectivity embedding hypercube graphs hypercube interconnection structure hypercube supercomputers image processing multiple-data programming operating systems optimisation parallel architectures parallel distributed-memory multiprocessors parallel machines parallel programming programming languages routing

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Cardinality Constraint Non-Uniform Sampling for Maximizing Reconstruction Accuracy of Time-Varying Signals

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第9期70卷 6746-6756页

作者： Pandey, Sakshi Banerjee, Amit South Asian Univ Comp Sci Dept New Delhi 110068 India

Non-uniform sampling selects samples at irregular intervals for concise signal representation. Prior works on non-uniform sampling predominantly focused on maximizing reconstruction accuracy or optimizing sample size. However, a trade-off exists between the two factors, as increasing the sample size can improve the reconstruction accuracy, but it decreases the bandwidth efficiency. The reverse is true for under-sampling. Thus, it is important to balance the two factors. This motivates us to consider CAISOS, a CArdinalIty conStraint nOn-uniform Sampling problem that aims to select at most k sample points of a given time-varying signal such that the regenerated signal has the maximum reconstruction accuracy. Applications of CAISOS include fixed-rate sampling and signal compression in the time domain, such as in robotics and fixed-rate speech encoders. It proposes an integer linear programming model to address CAISOS and shows that the time complexity of the same increases exponentially with increasing signal size. It further proves that CAISOS is an NP-complete problem by showing that it is both NP and NP-hard by using a polynomial-time reduction from the 0/1 knapsack problem. Thus, the paper proposes a polynomial time heuristic based on the least-cost branch-and-bound approximation to solve CAISOS. Finally, it demonstrates the effectiveness of the proposed approaches through simulation by comparing them with the existing counterparts using various time-varying signals available in multiple databases.

关键词： Non-uniform sampling branch-and-bound optimization set cardinality integer linear programming NP-complete Non-uniform sampling branch-and-bound optimization set cardinality integer linear programming NP-complete

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Joint improvements for capacity and power-efficiency of mobile WiMAX by relaying schemes

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2013年第4期13卷 361-376页

作者： Liu, Bao Yuan Chang, Shih Yu Wang, Tai Chi Natl Tsing Hua Univ Dept Comp Sci Inst Commun Engn Hsinchu 30043 Taiwan

The mobile WiMAX standard promises to enable low-cost mobile Internet applications over extensive areas and to meet the capacity requirements by combining advanced multiple input multiple output (MIMO) and relay transmission techniques. In this paper, we propose a solution to increase the channel capacity between wireless links and to conserve the average required uplink transmission power consumption simultaneously through deploying relay stations' (RSs) locations judiciously. Two relaying schemes, analogue (amplify and forward) relaying and digital (decode and forward) relaying from a mobile device to the base station (BS) through a relay node, are adopted with weighting filters to increase the channel capacity. Based on the enhanced channel capacity, a new manipulation way to save power is introduced by deploying RSs strategically where the branch-and-bound (BB) algorithm is applied to determine the placements of RSs. Our simulation results demonstrate the significant improvement of network capacity by applying the weighting filter techniques and the great power saving of the average total network power by utilizing the BB algorithm to arrange RSs locations. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： mobility relay deployment branch-and-bound optimization MIMO capacity WiMAX

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Optimal Priority Assignment to Control Tasks

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2014年第5-Sup期13卷 1-17页

作者： Mancuso, Giulio M. Bini, Enrico Pannocchia, Gabriele Scuola Super Sant Anna Pisa Italy Lund Univ Lund Sweden Univ Pisa I-56100 Pisa Italy

In embedded real-time systems, task priorities are often assigned to meet deadlines. However, in control tasks, a late completion of a task has no catastrophic consequence;rather, it has a quantifiable impact in the control performance achieved by the task. In this article, we address the problem of determining the optimal assignment of priorities and periods of sampled-data control tasks that run over a shared computation unit. We show that the minimization of the overall cost can be performed efficiently using a branch and bound algorithm that can be further speeded up by allowing for a small degree of suboptimality. Detailed numerical simulations are presented to show the advantages of various branching alternatives, the overall algorithm effectiveness, and its scalability with the number of tasks.

关键词： Algorithms Design Performance Verification Real-time systems control systems priority assignment optimal design branch-and-bound optimization

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optimization problems in multivariable fuzzy predictive control

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2004年第3期36卷 199-221页

作者： Mendonça, LF Sousa, JM da Costa, JMGS Univ Tecn Lisboa Inst Super Tecn IDMEC GCARDept Mech Engn P-1049001 Lisbon Portugal

The application of model predictive control (MPC) to complex, nonlinear processes results in a non-convex optimization problem for computing the optimal control actions. This optimization problem can be solved by discrete search techniques such as the branch-and-bound method (B&B), which has been successfully applied to MPC. However, the discretization induced by B&B introduces a tradeoff between the number of discrete actions and the performance. This paper proposes a solution for non-convex optimization problems in multiple-input multiple-output (MIMO) systems. Fuzzy predictive filters, which are represented as an adaptive set of control actions multiplied by gain factors, are extended for MIMO systems. This solution keeps the number of necessary alternatives low and increases the performance. The proposed MPC method using fuzzy predictive filters is applied to the control of a gantry crane. Simulation results show the advantages of the proposed method. (C) 2003 Elsevier Inc. All rights reserved.

关键词： model predictive control branch-and-bound optimization MIMO systems control control and optimization fuzzy predictive filters

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Fuzzy model predictive control for active power filter

Fuzzy model predictive control for active power filter

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2nd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Fan, SS Wang, YN Hunan Univ Dept Elect Engn Changsha Peoples R China

ISBN: (纸本)0780382374

A fuzzy model predictive control strategy for active power filter is presented in this paper. In the strategy, T-S fuzzy model is employed to predict future harmonic compensating current. The fuzzy model is derived from input-output data by means of product-space fuzzy clustering. In order to make the fuzzy model compact and accurate, similarity driven rule base simplification is applied to detect and merge compatible fuzzy sets in the model and a new validity measure is proposed to determine appropriate number of the clusters. Based on the model output, branch-and-bound optimization method is adopted to produce proper value of control vector, this value is adequately modulated by means of a space vector PWM modulator which generate proper gating patterns of the inverter switches to maintain tracking of reference current. The fuzzy model predictive algorithm is used in internal model control scheme to compensate for process disturbances, measurement noise and modeling errors. The proposed control is applied to compensate the harmonic produced by the variable non-linear load. Simulation results show the fuzzy model based predictive controller is effective and feasible.

关键词： fuzzy model predictive control fuzzy modeling branch-and-bound optimization active power filter

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Fuzzy Predictive Filtering in Nonlinear Economic Model Predictive Control for Demand Response

Fuzzy Predictive Filtering in Nonlinear Economic Model Predi...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Santos, Rui Mirra Zong, Yi Sousa, Joao M. C. Mendonca, Luis You, Shi Mihet-Popa, Lucian Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Tech Univ Denmark Dept Elect Engn Roskilde Denmark Escola Super Naut Infante D Henrique Dept Marine Engn Lisbon Portugal Oestfold Univ Coll Fac Engn Krateroy Norway

ISBN: (纸本)9781509019199

The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy filtering, is performed. The results show that the controller achieves a good performance while keeping the temperature inside the predefined comfort limits. Fuzzy predictive filtering has shown to be an effective tool which is capable of reducing the computational burden and increasing the performance level of the control algorithm.

关键词： branch-and-bound optimization demand response fuzzy predictive filtering nonlinear economic model predictive control smart buildings

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