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检索条件"任意字段=Conference on High Performance Algorithms and Software for Nonlinear Optimization"

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nonlinear weighted multiple centrality corrections interior point method for optimal power flow

Nonlinear weighted multiple centrality corrections interior ...

引用

International conference on Sustainable Power Generation and Supply, SUPERGEN

作者： Zhiguang Huang Quanyuan Jiang College of Electrical Engineering University of Zhejiang Hangzhou Zhejiang China

ISBN: (纸本)9781424449347

Optimal power flow (OPF) is a large scale nonlinear non-convex optimization problem. In the last decades many algorithms are developed to solve these problems and the interior point method (IPM) is a popular one. The primal-dual IPMs and their later developments have attracted much research interest. Impressed by the improvements in convergent performance of the further developed multiple centrality corrections IPM in linear programming (LP), this paper extended it to nonlinear programming and made certain modifications to make it adaptable to the nonlinear OPF. Test results on several cases ranging from 9-bus to 2746-bus system indicate the efficiency and high convergence of the proposed algorithm, which outperforms the original multiple centrality corrections IPM. Comparisons between the proposed method and other forms of interior point methods are also given to show its advantage in convergent performance.

关键词： Load flow Large-scale systems Linear programming System testing Power generation Reactive power control Voltage control Control systems Power generation economics Economic forecasting

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Real-time algorithm research and realization of Optimal Unsymmetrical PWM method

Real-time algorithm research and realization of Optimal Unsy...

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International conference on Power Electronics and Motion Control (IPEMC)

作者： Qin Liang Kaipei Liu Hui Zhang Xiaoming Zha School of Electrical Engineering WuHan University WuHan China School of Electrical Engineering WuHan University 430072 China

As the work frequency is very low, the Optimal Unsymmetrical PWM Method is suitable to be applied in high-power situation requiring control of fundamental and harmonics simultaneously, which will reduce the switch loss and the EMI greatly. Because of the low work frequency, the optimal unsymmetrical PWM method requires optimizing each independent switch angle in order to achieve good control effect, and it needs to solve the nonlinear equations on-line inevitably to obtain the switch angle. A new algorithm by combining the predominance of the Newton and homotopy algorithms is proposed, which can effectively realize switch angle on-line computation with relative high convergence rate. At last, this paper validated the real-time performance and the effectiveness of the switch angle algorithms on the experimental platform with the control core of TMS320F2812.

关键词： Pulse width modulation Switches Frequency Optimal control Power harmonic filters optimization methods Convergence Electromagnetic interference nonlinear equations Pulsed power supplies

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Support Vector Selection and Adaptation and its application in remote sensing

Support Vector Selection and Adaptation and its application ...

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International conference on Recent Advances in Space Technologies (RAST)

作者： Gulsen Taskin Kaya Okan K. Ersoy Mustafa E. Kamasak Computational Science and Engineering Istanbul Technical University Istanbul Turkey School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Computer Engineering Istanbul Technical University Istanbul Turkey

Classification of nonlinearly separable data by nonlinear support vector machines is often a difficult task, especially due to the necessity of a choosing a convenient kernel type. Moreover, in order to get high classification accuracy with the nonlinear SVM, kernel parameters should be determined by using a cross validation algorithm before classification. However, this process is time consuming. In this study, we propose a new classification method that we name Support Vector Selection and Adaptation (SVSA). SVSA does not require any kernel selection and it is applicable to both linearly and nonlinearly separable data. The results show that the SVSA has promising performance that is competitive with the traditional linear and nonlinear SVM methods.

关键词： Remote sensing Support vector machines Support vector machine classification Kernel Data engineering Training data Testing Machine learning algorithms Application software Classification algorithms

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A multi-phase genetic algorithm for the efficient management of multi-chiller systems

A multi-phase genetic algorithm for the efficient management...

引用

Asian Control conference

作者： Alessandro Beghi Marco Bertinato Luca Cecchinato Mirco Rampazzo Dipartimento di Ingegneria dell'Informazione Universita di Padova Padova Italy Dipartimento di Fisica Tecnica Universita di Padova Padova Italy

ISBN: (纸本)9781424454402

In HVAC plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other to provide standby capacity, operational flexibility, and less disruption maintenance. However, the problem of efficiently managing multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore significant energy savings can be achieved by optimizing the chiller operation of HVAC systems. In this paper an unified method for multi-chiller management optimization is presented, that deals simultaneously with the optimal chiller loading and optimal chiller sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an efficient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment developed in Matlab/Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of energy performance.

关键词： Genetic algorithms Fault detection nonlinear systems Parameter estimation Sliding mode control Fault diagnosis Noise robustness nonlinear control systems Control system synthesis Convergence

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Storage-efficient quasi-Newton algorithms for image super-resolution

Storage-efficient quasi-Newton algorithms for image super-re...

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International conference on Digital Signal Processing (DSP)

作者： Diego A. Sorrentino Andreas Antoniou Department of Electrical and Computer Engineering University of Victoria BC Canada

Multiframe image super-resolution algorithms can be used to obtain a higher-resolution higher-quality image from a set of low-resolution, blurred, and noisy images. Very often, these algorithms rely on an optimization-based inversion of the image acquisition model. Recently, two algorithms for grayscale and hybrid demosaicing and color super-resolution have been proposed by Farsiu et al. These algorithms are of practical interest because they are fast and also they can overcome mismatches in the assumed acquisition model. However, they rely on the use of steepest-descent minimization which is inefficient in highly nonlinear and ill-conditioned problems like super-resolution. In this paper, we use two storage-efficient quasi-Newton algorithms, the memoryless and the limited-memory BFGS algorithms, to improve the performance of the super-resolution approaches proposed by Farsiu et al.

关键词： Image storage Image resolution Strontium Gray-scale Layout high-resolution imaging Minimization methods Robustness Image reconstruction Image quality

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Automated Design of Logic Circuits with a Increasable Evolution Approach

Automated Design of Logic Circuits with a Increasable Evolut...

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IEEE International conference on high performance Computing and Communications (HPCC)

作者： Guoliang He Naixue Xiong Athanasios V. Vasilakos Yuanxiang Li Zhongzhi Shi State Key Laboratory of Software Engineering Wuhan University of China Wuhan China Key Laboratory of Intelligent Information Process Institute of Computing Technology Chinese Academy and Sciences Beijing China Department of Computer Science Georgia State University USA Department of Computer Science Saint Francis Xavier University Canada

Since the scalability of logic circuits is becoming larger and more complex, the auto-design is becoming more and more difficult. In order to improve automatic design and performance evaluation of logic circuits in efficiency and capability of optimization, multiobjective simulated annealing (MSA) based increasable evolution approach is designed to evolve logic circuits automatically with an extended matrix encoding method, which can be able to reflect the potential performance of a circuit and reduce the risk of deleting a circuit with a good developing potential during evolution is devised. In the process of evolution, each individual is renewedly associated to a corresponding objective in terms of a novel adaptive evaluation method at each generation. In experiments, complicated arithmetic circuits are designed to assess the performance of MSA against other algorithms. Results indicate that the proposed method could design logic circuits efficiently.

关键词： Logic circuits Encoding Hardware Scalability Circuit simulation Simulated annealing Genetic algorithms Laboratories Computer science Arithmetic

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Distortion Prediction for Video Quality optimization over Packet Switched Networks

Distortion Prediction for Video Quality Optimization over Pa...

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GLOBECOM 2009 - 2009 IEEE Global Telecommunications conference

作者： Andrea Vesco Enrico Masala Carlo Novara Control and Computer Engineering Department Politecnico di Turino Torino Italy

Scheduling techniques are often deployed at the network edge to maximize the quality of the video communication while satisfying a given constraint on the maximum high priority usable bandwidth. For the case of video communications, the importance of each packet, in terms of the distortion that would be caused by its loss, can be used to decide which packets should be prioritized in order to maximize the expected video quality. However, the resulting performance strictly depends on the length of the video stream segment considered, at each time instant, by the distortion-aware scheduling algorithms. This work focuses on improving the performance of those algorithms by predicting the characteristics of the near-future part of video streams, exploiting the short and long term dependencies in packet distortion values. This approach could be particularly valuable in live video scenarios where accessing the future part of the video would imply the insertion of an additional delay. Several distortion prediction models are derived and their performance evaluated with actual scheduling algorithms. Results show that the best predictions are provided by neural network models, yielding significant improvements of the video communication quality.

关键词： Packet switching Streaming media nonlinear distortion Predictive models Neural networks Video compression Rate-distortion Scheduling algorithm Telecommunication traffic Traffic control

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Immune optimization Based Genetic Algorithm for Incremental Association Rules Mining

Immune Optimization Based Genetic Algorithm for Incremental ...

引用

International conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Genxiang Zhang Haishan Chen Software school Xiamen University Xiamen Fujian China Software School Xiamen University Xiamen Fujian China

Business activity and engineering practice always produce large data sets carrying important information, but because of the data sets' largeness and frequent updating, if we apply the Apriori based algorithms to them for incremental rules mining, it is not only inefficient, but also either redundant rules would be produced under low threshold of minimal support, which makes users hardly distinguish which rules are really meaningful, or significant rules with low support in additional data set would possibly lost when the threshold is defined high. Motivated by these, therefore, following genetic principles, and combining with natural immune evolution theory and relevant bionic mechanism, this paper proposes an IOGA (immune optimization based genetic algorithm) approach for incremental association rules mining to large and frequent updating data sets. Experiment demonstrates the method's efficiency and presents its good performance in pruning redundant rules and discovering meaningful rules, perceiving low support rules in additional data set.

关键词： Genetic algorithms Association rules Data mining Immune system Evolution (biology) Artificial intelligence Computational intelligence Educational institutions Data engineering Genetic engineering

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A Multiple Constraint Quality of Service Routing Algorithm Base on Dominating Tree

A Multiple Constraint Quality of Service Routing Algorithm B...

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International conference on Computational Intelligence and software Engineering (CiSE)

作者： Xueshun Wang Shao-hua Yu JinYou Dai Ting Luo Wuhan FiberHome Networks Company. Ltd Wuhan Hubei P.R. China Huazhong University of Science and Technology Wuhan Hubei CN

Providing quality of service (QoS) guarantees in networks gives rise to several challenging issues. One of them is how to determine a feasible path that satisfies a set of constraints while maintaining high utilization of network resources. Multi-constrained QoS routing algorithm finds a feasible route in network that satisfies multiple independent constraints. In general, multi-constrained path selection is a NP-complete problem that cannot be exactly solved in polynomial time. The NP-complete problem is converted into a multi-objective optimization problem with constraints, and optimizes multi-constraint QoS parameter respectively in this paper. A multiple constrained QoS routing algorithm is then proposed to generate a set of non-dominated optimal route based on the optimization principle of multi-objective evolutionary algorithms (DTRA), which can attain a satisfied multi-constraint QoS parameter nondominates set based on dominating tree. The dominating tree preserves the necessary relationships among QoS routings, and reduces the comparisons among QoS routing distinctly. So the convergence performance is quick. The theoretic analysis and experiment results show that our proposed algorithm (DTRA) is effective for multi-constrained QoS routing, and can improve the performance of networks.

关键词： Quality of service Routing Constraint optimization Evolutionary computation NP-complete problem Protocols Convergence Delay Jitter Polynomials

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Design Flow for the Generation of Optimized FIR Filters

Design Flow for the Generation of Optimized FIR Filters

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IEEE International conference on Electronics, Circuits and Systems

作者： Vagner S. Rosa Fabio F. Daitx Eduardo Costa Sergio Bampi Informatics Institute UFRGS Univ. UCPel Univ.

ISBN: (纸本)9781424450909

This paper presents a design flow for the generation of optimized FIR filters. It includes a graphical interface to integrate the complete synthesis flow, from the filter specification to a synthesizable VHDL. This allows the user to quickly develop high performance filters that meet the design constraints. The designed software supports coefficient and data bit-width configuration at design time. The generated VHDL code is generic and can be synthesized using any logic synthesis tool. The generation tool employs a combination of the reduction of the coefficients to N-Power-of-Two (NPT) terms, where the maximum number of non-zeros in each coefficient is taken as a constraint, and two Common Subexpression Elimination (CSE) algorithms to minimize the number of addition/subtraction operations used in the multiplier-less implementation of the filter. Synthesis results for a range of different filter specifications for both FPGA using Virtex 5 Pro device and TSMC 0,18μm CMOS standard cell, using Cadence synthesis tool are presented.

关键词： Design optimization Finite impulse response filter Informatics software algorithms software design Field programmable gate arrays CMOS technology Logic Heuristic algorithms Hardware

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