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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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An Improved Cache-Based PTSVM Learning Algorithm

An Improved Cache-Based PTSVM Learning Algorithm

引用

International conference on Computational Intelligence and software Engineering (CiSE)

作者： Yong Piao Peng Wu Xiu-kun Wang Qiang Sun EI / Software School Dalian University of Technology Dalian China Software School Dalian University of Technology Dalian China EI School Dalian University of Technology Dalian China ChengGao Technologies Dalian China

Support vector machine is gaining popularity due to many attractive features and promising empirical performance in the fields of nonlinear and high dimensional pattern recognition. TSVM (transductive support vector machine) takes into account a particular test set and tries to minimize misclassifications of just those particular examples. PTSVM (progressive transductive support vector machine) can automatically adapt to different data distributions and realize a transductive learning of support vectors in a more general sense. However, the process of pairwise labeling of PTSVM in the margin band is unnatural and products errors more easily. Although dynamical adjusting offers some sort of error recovery function, its ability is limited. In allusion to the shortcomings of PTSVM learning algorithm, ICPTSVM (an improved cache-based PTSVM) learning algorithm is presented. The algorithm uses pairwise labeling in the range and error-correcting on cache to replace pairwise labeling in the margin band and dynamical adjusting. Then it greatly reduces the number of mis-labeling and improves the speed and accuracy. Experiments data show the validity of this algorithm.

关键词： Labeling Support vector machines Machine learning Pattern recognition Statistical learning software performance Testing software algorithms Sun Inference algorithms

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A Testbed Development Framework for Cognitive Radio Networks

A Testbed Development Framework for Cognitive Radio Networks

引用

IEEE International conference on Communications (ICC)

作者： J. Jia Q. Zhang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Cognitive radio networking is a promising approach to fulfill the future's need for intelligent, high-performance communication and improve the efficiency of overall spectrum utilization. Testbed evaluation of protocols and algorithms is a must for the development of cognitive radio networks. In this paper, we present an integrated testbed framework for cognitive radio networks. The framework includes necessary components for cognitive radio operations: flexible RF front end, software define signal processing, adaptive MAC layer and network layer as well as a cross layer management interface. Such a design eases the cross layer configuration and performance optimization of cognitive protocol stack, while retaining the advantage of modularity. We design a testbed for ad-hoc cognitive radio network.

关键词： Testing Cognitive radio Protocols Signal processing algorithms Cross layer design Intelligent networks Radio frequency Adaptive signal processing Radio spectrum management optimization

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Multi-objective optimization of Graph Partitioning Using Genetic algorithms

Multi-objective Optimization of Graph Partitioning Using Gen...

引用

International conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP)

作者： Mehdi Farshbaf Mohammad-Reza Feizi-Derakhshi Department of Computer University of Tabriz Tabriz Iran

Graph partitioning is a NP-hard problem with multiple conflicting objectives. The graph partitioning should minimize the inter-partition relationship while maximizing the intra-partition relationship. Furthermore, the partition load should be evenly distributed over the respective partitions. Therefore this is a multi-objective optimization problem. There are two approaches to multi-objective optimization using genetic algorithms: weighted cost functions and finding the Pareto front. We have used the Pareto front method to find the suitable curve of non-dominated solutions, composed of a high number of solutions. The proposed methods of this paper used to improve the performance are injecting best solutions of previous runs into the first generation of next runs and also storing the non-dominated set of previous generations to combine with later generation's non-dominated set. These improvements prevent the GA from getting stuck in the local optima and make the search more efficient and increase the probability of finding more optimal solutions. Finally, a simulation research is carried out to investigate the effectiveness of the proposed algorithm. The simulation results confirm the effectiveness of the proposed multi-objective GA method.

关键词： Genetic algorithms Pareto optimization Cost function Computational modeling optimization methods Genetic engineering Computer applications Application software NP-hard problem Partitioning algorithms

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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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Accurate Measurements and Precise Modeling of Power Dissipation of CUDA Kernels toward Power Optimized high performance CPU-GPU Computing

Accurate Measurements and Precise Modeling of Power Dissipat...

引用

IEEE International conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Reiji Suda Da Qi Ren Graduate School of Information Science and Technology CREST University of Tokyo Japan Science and Technology SORST Tokyo Japan

Power dissipation is one of the most imminent limitation factors influencing the development of high performance Computing (HPC). Toward power-efficient HPC on CPU-GPU hybrid platform, we are investigating software methodologies to achieve optimized power utilization by algorithm design and programming technique. In this paper we discuss power measurements of GPU, propose a method of automatic extraction of power data of CUDA kernels from long measurement sequence, and execute an exactitude and effective power analysis on CUDA kernels. By using the proposed method above, we measured a sample kernel that performs single precision floating point additions on GeForce 8800 GTS. Our results suggest that the power consumption by a non-working thread in underoccupied half-warp is 71% of the power consumed by a working thread.

关键词： Power measurement Power dissipation Kernel high performance computing Yarn Design optimization software algorithms Algorithm design and analysis Data mining performance evaluation

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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 ...

引用

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...

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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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