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Multi-agent interaction for optimal resource allocation in computational grid

Multi-agent interaction for optimal resource allocation in c...

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international conference on machine learning and Cybernetics (icmlc)

作者： Chun-Lin Li Zheng-Ding Lu La-Yuan Li Department of Computer Science Huazhong University of Science and Technology Wuhan China Department of Computer Science Wuhan University of Technology Wuhan China

ISBN: (纸本)0780384032

This work presents a multi economic agent interaction scheme for resource allocation in the computational grid. The proposed competitive market economy is described. It consists of three different types of agents: grid request agents, grid resource agents, grid task agents; and a grid market that regulates the prices based on the observed aggregate excess demand. In order to solve the problem of heterogeneous demand in the grid users' preferences are summarized by means of their utility functions. We consider budget and tune as constraints of optimization problem. The objective of the optimization problem is to determine the amount of resources under different constraints to maximize the users' utilities. This paper proves the existence of a solution of the optimization problem, and specifies an iterative algorithm that is used by the grid market to update the prices, and it leads to an allocation that is close to a solution of the optimization problem in a finite number of iterations.

关键词： Resource management Grid computing Processor scheduling Costs Microeconomics Application software Computer science Power generation economics Environmental economics Constraint optimization

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A hybrid genetic algorithm for tasks scheduling in heterogeneous computing systems

A hybrid genetic algorithm for tasks scheduling in heterogen...

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international conference on machine learning and Cybernetics (icmlc)

作者： Yi-Wen Zhong Jian-Gang Yang Heng-Nian Qi College of Computer and Information FuJian Agriculture and Forestry University Fuzhou China College of Computer Science and Technology University of Zhejiang Hangzhou China

ISBN: (纸本)0780384032

Efficient application scheduling is critical for achieving high performance in heterogeneous computing systems (HCS). Because an application can be partitioned into a group of tasks and represented as a directed acyclic graph (DAG), the problem can be stated as finding a schedule for a DAG to be executed in a HCS so that the schedule length can be minimized. The tasks scheduling problem is NP-hard in general, except in a few simplified situations. In order to obtain optimal or suboptimal solutions, a large number of scheduling heuristics have been presented in the literature. Genetic algorithm (GA), as a power tool to achieve global optimal, has been successfully used in this field. This work presents a new hybrid genetic algorithm to solve the scheduling problem in HCS. It uses a direct method to encode a solution into chromosome. Topological sort of DAG is used to repair the offspring in order to avoid yielding illegal or infeasible solutions, and it also guarantees that all feasible solutions can be reached with some probability. In order to remedy the GA's weakness in fine-tuning, this paper uses a greedy strategy to improve the fitness of the individuals in crossover operator, based on Lamarckian theory in the evolution. The simulation results comparing with a typical genetic algorithm and a typical list heuristic, both from the literature, show that this algorithm produces better results in terms of both quality of solution and convergence speed.

关键词： Genetic algorithms Processor scheduling Scheduling algorithm High performance computing Clustering algorithms Computer networks Educational institutions Application software Computer applications Heuristic algorithms

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Agent-based retail electricity market: modeling and analysis

Agent-based retail electricity market: modeling and analysis

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international conference on machine learning and Cybernetics (icmlc)

作者： Jing Yu Jian-Zhong Zhou Jun-Jie Yang Wei Wu Bo Fu Rong-Tao Liao Department of Hydropower and Information Engineering Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)0780384032

In the past decades, power systems in the whole world have been facing pressure to be deregulated or restructured. This research has designed an agent-based retail electricity market to simulate the trading procedure in modern power systems, and utilize colored Petri nets technology to present the communications and cooperation of agents in the market. We design four kinds of participant agents: power exchanger agent (PXA), power user agents (PUAs), retail company agents (RCAs) and independent power plant agents (IPPAs). PXA is supervisor of the marketplace, in charge of opening and closing the market, and dealing with the trading request from the other agents, avoiding conflicts with constraints of power transmission grids. The other three kinds of agents could enter the marketplace arbitrary if it opens and freely select their satisfied consumers/providers, which are the chief distinctions of our design. The results of analysis show that the proposed retail electricity market could increase efficiency, reduce operational costs and give consumers more alternatives. Though it has more latently conflicts and concurrencies, which cause a little complication, we successfully deal with them by colored Petri nets.

关键词： Electricity supply industry Power system modeling Power system simulation Power system analysis computing Petri nets Electricity supply industry deregulation Power generation Power transmission Costs Concurrent computing

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Efficient feature selection for high-dimensional data using two-level filter

Efficient feature selection for high-dimensional data using ...

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international conference on machine learning and Cybernetics (icmlc)

作者： Yun Li Zhong-Fu Wu Jia-Min Liu Yan-Yun Tang Department of Computer ChongQing University ChongQing China College of Optics and Electronics ChongQing university ChongQing China ChongQing Branch China Light Industrial Materials Company Limited ChongQing China

ISBN: (纸本)0780384032

Feature selection is a key problem to pattern recognition and machine learning, and it is difficult to get the optimal feature subset for its NP-hard. Currently, the dimensionality of feature set or instance set is very high in many applications, such as information retrieval, so the feature selection from high-dimensional data is also an urgent task for researchers. This paper presents a new approach, which is a two-level filter model system integrating the relief and a newly developed algorithm of feature cluster, to reduce the dimensionality of large-scale feature set via the feature correlation (relevance) including the feature-feature correlation and feature-class correlation. Our major contributions are: (1) to present a system to perform feature selection from high-dimensional data; (2) to analyze the change of system architecture according to the time cost of the parts in the system; (3) to summarize and comment on the calculations of feature correlation; (4) to perform experiments to show the effective of the proposed approach, which has shown that the system can efficiently get a better compromise between dimensionality reduction and accuracy rate of classification than just part of the system. In many cases, it can improve the accuracy rate and dimensionality reduction.

关键词： Optical filters Costs Pattern recognition machine learning Optical computing Educational institutions Optical materials Information retrieval Clustering algorithms Large scale integration

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A study of the difference between partial derivative and stochastic neural network sensitivity analysis for applications in supervised pattern classification problems

A study of the difference between partial derivative and sto...

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international conference on machine learning and Cybernetics (icmlc)

作者： W.W.Y. Ng D.S. Yeung Xi-Zhao Wang I. Cloete Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China Machine Learnin Center Faculty of Mathematics and Computer Science Hebei University Baoding China School of Information Technology International University Bremen Bruchsal Germany

This work provides a brief development roadmap of the neural network sensitivity analysis, from 1960's to now on. The two main streams of the sensitivity measures: partial derivative and stochastic sensitivity measures are compared. The partial derivative sensitivity (PD-SM) finds the rate of change of the network output with respect to parameter changes, while the stochastic sensitivity (ST-SM) finds the magnitudes of the output perturbations between the original training samples and the perturbed samples, in statistical sense. Their computational complexities are compared. Furthermore, how to evaluate multiple parameters of the neural network with or without correlation are explored too. In addition, the differences of them in the application of supervised pattern classification problems are also discussed. The evaluations are based on three major applications of sensitivity analysis in supervised pattern classification problems: feature selection, sample selection and neural network generalization assessment. ST-SM and PD-SM of the RBFNN are used for investigations.

关键词： Stochastic processes Neural networks Sensitivity analysis Intelligent networks Pattern classification Application software Artificial neural networks Computer networks Mathematics Computer science

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Face detection under variable lighting based on resample by face relighting

Face detection under variable lighting based on resample by ...

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international conference on machine learning and Cybernetics (icmlc)

作者： Yue-Min Li Jie Chen Lai-Yun Qing Bao-Cai Yin Wen Gao Multimedia and Intelligent Software Technology Laboratory Beijing University of Technology Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)0780384032

Different environment illuminations have a great impact on face detection. We present a solution based on face relighting technology. The basic idea is that there exists nine harmonic images that can be derived from a 3D model of a face, and by which we can estimate the illumination coefficient of any face sample. Using an illumination radio image, we can produce images under new lighting conditions. To detect the faces under certain lighting condition, we relight the original face samples to get more new faces under different kinds of possible lighting condition, and add them to the training set. Our experimental results on support vector machine (SVM) turns out that the relighting subspace is effective in face detection under various lighting conditions. Moreover, if we relight original face samples to the new samples under different illuminations, the collected example sets are multiplied. We use the expanded database to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods. The later experiment also verifies the generalization capability of the proposed method.

关键词： Face detection Lighting Support vector machines Testing Face recognition Frequency Multimedia computing Laboratories Computer science Image databases

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Fault detection of power transformers using genetic programming method

Fault detection of power transformers using genetic programm...

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international conference on machine learning and Cybernetics (icmlc)

作者： Zheng Zhang Wei-Hua Huang Deng-Ming Xiao Yi-Lu Liu School of Electric Engineering Shanghai Jiaotong University Shanghai China College of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Department of Electrical Engineering Virginia Technology USA

ISBN: (纸本)0780384032

This paper proposes a novel method for insulation fault detection of power transformer using the genetic programming (GP) method. Fault detection can be seen as a problem of multi-class classification. GP is a way of automatically constructing computer programs using a process analogous to biological evolution. GP methods of problem solving have a great advantage in their power to represent solutions to complex classification problems. The flexibility of representation gives GP the capacity to represent classification problems with means unavailable to other techniques such as neural networks. A binary tree (Bi-tree) structure is presented to transfer an N-class problem into N-1 two-class problems. The proposed method has been tested on the actual records and compared with the conventional methods, fuzzy system method and artificial neural network method. The result shows that GP has advantages over the existing diagnosis methods and provides a new way to solve the problem of fault detection.

关键词： Fault detection Power transformers Genetic programming Artificial neural networks Power transformer insulation Biology computing Evolution (biology) Problem-solving Binary trees System testing

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Adaptive workflow-oriented services composition and allocation in distributed environment

Adaptive workflow-oriented services composition and allocati...

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international conference on machine learning and Cybernetics (icmlc)

作者： Jing-Fan Tang Bo Zhou Zhi-Jun He U. Pompe College of Computer Science & Technology University of Zhejiang Hangzhou China State Street Corporation Boston MA USA

With the increasing scale of distributed resources, which couples a wide variety of geographically distributed computational resources, storage systems, data sources and computational kernels, service management and scheduling is a complex undertaking. Since most services on the Internet are mutually independent, users have trouble in accessing several services to achieve a single purpose. Furthermore, the resources are owned by different individuals or organizations with their own policies, have different access and cost models, and have dynamically varying loads and availability. To address the confronted issues, we propose an adaptive approach in this paper for distributed services composition and allocation, which is service-centric and workflow-oriented. We use XML to describe the services by encoding such factors as resource configuration, performance, and service-specific capabilities, which enables services to be easily developed, composed, deployed, relocated, monitored and managed in distributed environments. We use service composition technology to construct composite service by combining several independent services, which can be considered as the element services of the composite service, executed as workflow process. The allocation of such element services will be decided dynamically due to the economic objective function to address the issues of adaptation, availability, performance and economic cost.

关键词： Distributed computing Resource management Availability Environmental economics Kernel Processor scheduling Web and internet services Costs XML Encoding

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Investigation of distributive data mining calculating architecture

Investigation of distributive data mining calculating archit...

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international conference on machine learning and Cybernetics (icmlc)

作者： Ying-Wu Fang Yu-Mei Huang Guang-Peng Zhang Yi Wang Shao-Long Wang School of Mechanical and Precision Instrument Engineering Xi'an University of Technology Xi'an China The Telecommunication Engineering Institute Air Force Engineering University Xi'an China School of Mechanical and Precision Instrument Engineering Wan University of Technology Xi'an China

ISBN: (纸本)0780384032

A distributive calculating architecture is presented to realize data mining efficiently. The architecture is a hierarchical computational method from the conception, which stores the information in every sub-node with the ideas of database partition, a united center distribute unit can be responsible for the collecting and maintenance of the information in every sub-node, by the scan of database, information is distributed to different nodes, this architecture can maintain a global set enumerate tree, the local large item-sets can be constructed by using any effective algorithm, it mainly solves the problem of highly effective data distribution and data skew, the detailed explaining and theoretical proving of the calculating architecture is given, and how to solve the data skew problem highly and effectively is also discussed in this paper. The partial implementation of this algorithm shows the correctness and feasibility, the calculating architecture can be used for distribute database and most applicable for distribute calculation, which can be used in highly and effectively data mining in distributive and parallel environment.

关键词： Data mining Computer architecture Association rules Data engineering Distributed computing Distributed databases Information analysis Partitioning algorithms Instruments Electronic mail

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Research on mobile agent computing theory

Research on mobile agent computing theory

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international conference on machine learning and Cybernetics

作者： Hua, Z Zhang, YL Fan, H Li, JJ Yantai Univ Shandong 264005 Peoples R China

A new system structure of Agent is givened. It gives out every semantic definition in the new Agent system structure, and analyzes how the Agent computing based on the system structure. Finally we discusses and verifi... 详细信息

ISBN: (纸本)0780378652

关键词： agent intentional system belief intention capability commitment

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