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检索条件"机构=Intelligent Systems Laboratory Department of Computer Science and Software Engineering"

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Gene subset selection using an iterative approach based on genetic algorithms

Gene subset selection using an iterative approach based on g...

引用

作者： Mohamad, M.S. Omatu, S. Deris, S. Yoshioka, M. Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai Osaka 599-8531 Japan Department of Software Engineering Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia 81310 Skudai Johore Malaysia

ISBN: (纸本)9784990288037

Microarray data are expected to be useful for cancer classification. The main problem that needs to be addressed is the selection of a smaller subset of genes from the thousands of genes in the data that contributes to a disease. This selection process is difficult due to many irrelevant genes, noisy genes, and the availability of a small number of samples compared to a huge number of genes (higher-dimensional data). Hence, this paper aims to select a near-optimal (smaller) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other related previous works as well as four methods experimented in this work. In addition a list of informative genes in the best gene subsets is also presented for biological usage. ©ISAROB 2009.

关键词： Genes

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Dependable workflow scheduling in global grids

Dependable workflow scheduling in global grids

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2009 10th IEEE/ACM International Conference on Grid Computing, Grid 2009

作者： Rahman, Mustafizur Ranjan, Rajiv Buyya, Rajkumar Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9781424451494

In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid overlays. The proposed approach builds upon structured peer-to-peer indexing and overlay networking techniques to create a scalable wide-area networking of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers (Grid Resource Brokers). The proposed algorithm can dynamically adapt to changing resource conditions and offer significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. We evaluate and demonstrate the feasibility of our approach through an extensive trace driven simulation. The results show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system. © 2009 IEEE.

关键词： Scheduling algorithms

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A distributed heuristic for decentralized workflow scheduling in global grids

A distributed heuristic for decentralized workflow schedulin...

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2009 10th IEEE/ACM International Conference on Grid Computing, Grid 2009

ISBN: (纸本)9781424451494

Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid environment. However, in this paper, we present a distributed list heuristic for decentralized scheduling of workflow applications in global Grids. The simulation results show that the proposed scheduling approach is scalable with respect to increased workload on the system. © 2009 IEEE.

关键词： Scheduling

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Scheduling parallel applications on Utility Grids: Time and cost trade-off management

Scheduling parallel applications on Utility Grids: Time and ...

引用

32nd Australasian computer science Conference, ACSC 2009

作者： Garg, Saurabh Kumar Buyya, Rajkumar Siegel, H.J. Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne VIC 3010 Australia Electrical and Computer Engineering Department Colorado State University Fort Collins United States Computer Science Department Colorado State University Fort Collins United States

ISBN: (纸本)9781920682729

With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments,users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, choosing of the best Grid resources becomes a challenge in such a competitive market. This paper presents two novel heuristics for scheduling parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real parallel workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against other common algorithms used by current meta-schedulers. The results shows that our algorithms outperform other algorithms by minimizing the time and cost of application execution on Utility Grids. Copyright © 2009, Australian computer Society, Inc.

关键词： Scheduling algorithms

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Emotion classification based on gamma-band EEG

Emotion classification based on gamma-band EEG

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31st Annual International Conference of the IEEE engineering in Medicine and Biology Society: engineering the Future of Biomedicine, EMBC 2009

作者： Li, Mu Lu, Bao-Liang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424432967

In this paper, we use EEG signals to classify two emotions-happiness and sadness. These emotions are evoked by showing subjects pictures of smile and cry facial expressions. We propose a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered. We use common spatial patterns (CSP) and linear-SVM to classify these two emotions. To investigate the time resolution of classification, we explore two kinds of trials with lengths of 3s and 1s. Classification accuracies of 93:5% ± 6:7% and 93:0%±6:2% are achieved on 10 subjects for 3s-trials and 1s-trials, respectively. Our experimental results indicate that the gamma band (roughly 30-100 Hz) is suitable for EEG-based emotion classification. ©2009 IEEE.

关键词： Signal processing

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PSF-constraints based iterative blind deconvolution method for image deblurring

PSF-constraints based iterative blind deconvolution method f...

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16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010

作者： Mo, Xuan Jiao, Jun Shen, Chen Department of Automation State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China Dept. of Computer Science and Technology Nanjing University Nanjing China School of Software and Electronics Peking University Beijing China

ISBN: (纸本)3642113001

In recent years, Image Deblurring techniques have played an essential role in the field of Image Processing. In image deblurring, there are several kinds of blurred image such as motion blur, defocused blur and gaussian blur. Many methods to address this problem have been proposed by researchers in previous research, among which the iterative blind deconvolution (IBD) method is the most popular method to solve this problem. However, the convergence of this method is not ensured, and there is no effective method to choose a proper initial estimate image and PSF(point spread function). In this paper, we improve the iterative blind deconvolution method by adding several constraints, which could be the type or parameters range of the PSF, on the PSF in each iteration. Experiment results validate that, with the help of these newly added constraints, our method are more likely to converge and has better deblurring performance than the IBD. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Optical transfer function

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An introduction to the echo state network and its applications in power system

An introduction to the echo state network and its applicatio...

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2009 15th International Conference on intelligent System Applications to Power systems, ISAP '09

作者： Dai, Jing Venayagamoorthy, Ganesh K. Harley, Ronald G. Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA 30332 United States Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9781424450985

Echo State Network (ESN) is a new type of Recurrent Neural Network (RNN) proposed in recent years. The training process of ESN is easier and requires less computational effort than regular RNN which has the same size. Due to its high modeling capability of complex dynamic system, ESN has been used in various power system applications such as power system nonlinear load modeling and true harmonic current detection, wide area monitoring, intelligent control of an Active Power Filter (APF), overhead conductor thermal dynamics identification, wind speed or water inflow forecasting, etc. This paper introduces the basic concept and the offline and online training algorithms of the ESN in detail and reviews the state of the art of ESN applications in power systems. © 2009 IEEE.

关键词： Recurrent neural networks

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Load forecasting of a desert: A computational intelligence approach

Load forecasting of a desert: A computational intelligence a...

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2009 15th International Conference on intelligent System Applications to Power systems, ISAP '09

作者： Saber, Ahmed Yousuf Al-Shareef, Abdulaziz Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States Electrical and Computer Engineering Department King Abdulaziz University Jeddah KSA Saudi Arabia

ISBN: (纸本)9781424450985

This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other places. Thus this model is different from a typical forecasting model considering inputs and outputs. In this research, two models are implemented - firstly load forecasting model for prediction;however, it is not sufficient for desired level of accurate forecasting, and secondly, optimization to improve the results up to at least better than existing results. This paper includes ANN and PSO models for 24-hours ahead load forecasting. ANN is a mathematical tool for mapping complex relations;it is well proved for the successful use of prediction, function approximation with dynamics, categorization, classification, and so forth. In this research, 24- step ahead calculations are performed in the ANN model and results are moderate. On the other hand, PSO is the most promising optimization tool. It is a swarmed based optimization method;it has better information sharing and conveying mechanism;it has better balance of local and global searching abilities;it can handle huge multi-dimensional optimization problems efficiently with hundreds of thousands of constraints. Thus PSO is chosen as the optimization tool that is applied on the weight matrix of ANN to improve results. In this research, PSO reliably and accurately tracks the continuously changing weights of ANN for uncertain load demand. By analyzing the model of ANN for the load-forecasting problem of SEC-WOA with hundreds of thousands of data and changing-uncertain load demand, the PSO is applied for the ANN weight adjustment and to optimize the uncertain load demand, as the ANN is not an optimization method. Results

关键词： Neural networks

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Offer-based scheduling of deadline-constrained Bag-of-Tasks applications for utility computing systems

Offer-based scheduling of deadline-constrained Bag-of-Tasks ...

引用

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Marco A. S. Netto Rajkumar Buyya Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

Metaschedulers can distribute parts of a bag-of-tasks (BoT) application among various resource providers in order to speed up its execution. When providers cannot disclose private information such as their load and computing power, which are usually heterogeneous, the metascheduler needs to make blind scheduling decisions. We propose three policies for composing resource offers to schedule deadline-constrained BoT applications. Offers act as a mechanism in which resource providers expose their interest in executing an entire BoT or only part of it without revealing their load and total computing power. We also evaluate the amount of information resource providers need to expose to the metascheduler and its impact on the scheduling. Our main findings are: (i) offer-based scheduling produces less delay for jobs that cannot meet deadlines in comparison to scheduling based on load availability (i.e. free time slots); thus it is possible to keep providers' load private when scheduling multi-site BoTs; and (ii) if providers publish their total computing power they can have more local jobs meeting deadlines.

关键词： Processor scheduling computer applications Job shop scheduling Grid computing Cloud computing Application software Information resources Delay Biological system modeling Message passing

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Illumination Invariant Object Tracking with Incremental Subspace Learning

Illumination Invariant Object Tracking with Incremental Subs...

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International Conference on Image and Graphics (ICIG)

作者： Gang Yu Hongtao Lu MOE-Microsoft Laboratory of Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai China

ISBN: (纸本)9781424452378

In this paper, we present an efficient and robust subspace learning based object tracking algorithm with special illumination handling. Illumination variances pose a great challenge to most of object tracking algorithms. In this paper, an edge orientation based feature has been proposed and proven to approximately invariant to illumination changes. Besides, we utilize the incremental subspace learning based particle filter framework which is effective to handle various appearance changes. To reduce the amount of computation when the particle number is large, a new layer of preprocessing step has been added to the particle filter framework with the help of edge orientation features. From the experimental m results, it is obvious that our proposed algorithm achieves promising performance especially in the scenarios with large illumination changes.

关键词： Lighting Target tracking Stochastic processes Particle filters Iterative algorithms Optimization methods Stochastic resonance Noise robustness computer graphics Laboratories

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