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IEEE International Conference on e-Business engineering (ICEBE)

作者： Jing-Mian Tang Liang Luo Kai-Ming Wei Xun Guo Xiao-Yu Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Air Force Early Warning Academy Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

Cloud computing provides utility-oriented IT services for users worldwide, and it enables offering various kinds of applications to consumer in scientific or business field based on a pay-as-you-go model. Although cloud computing is still in its infancy, the scale of cloud infrastructure is expanding fast, which result in huge energy consumption and operating costs. Due to the complex architecture of cloud infrastructure, it is hard to evaluate and optimize energy consumption of cloud infrastructure in a non-intrusive manner under varying application, user configurations and requirements. In this paper, we present Bin-Balancing Algorithm (BBA), an innovative resource scheduling algorithm for private clouds that integrating the advantages of both bin packing solutions and polygons correlation calculations. BBA is designed to optimize energy consumption, while considering the task deadline, host PE (processing element), memory and bandwidth. Polygons correlation calculation integrated in BBA is used to meet the elastic characteristics of cloud computing services. BBA is validated and well compared with existing resource scheduling algorithms in Cloud Sim toolkit. The results demonstrate that BBA can save energy in cloud infrastructure while balancing the loss of performance and SLA of cloud users.

关键词： Cloud computing Scheduling Bandwidth Scheduling algorithms Correlation Computational modeling

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Optimisation of operational reliability of large-scale industrial wind turbines 1st

Optimisation of operational reliability of large-scale indus...

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1st International Conference on Renewable Energies Offshore, RENEW 2014

作者： Camacho Questa, J.E. Requena Montejano, V. Polo, F. Moreno, L. Vanhnonacker, T. Stalaart, B. Karyotakis, A. Panagoiliopoulos, O. Karakassidis, V. Lang, Z.-Q. Roldán De La Cuadra, C. Murillo Calleja, M. Oses, I.E. Auer, G. Zalacain, I. Errea Mugica, J. Garcia Marquez, F.P. Pedegral, D. Lee, D. Hillmansen, S. Tricoli, P. Hajiabady, S. Fernando, G. Papaelias, Mayorkinos Ingeteam Service S.A Albacete Spain D2S International N.V Leuven Belgium TERNA Energy S.A Athens Greece Department of Automatic Control and Systems Engineering The University of Sheffield United Kingdom Indra Sistemas S.A Madrid Spain ACCIONA ENERGIA S.A Pamplona Spain ETSII Universidad De Castilla-La Mancha Ciudad Real Spain School of Electronics Engineering Kyungpook National University Daegu Korea Republic of School of Electronic Electrical and Computer Engineering The University of Birmingham United Kingdom School of Metallurgy and Materials The University of Birmingham United Kingdom

ISBN: (纸本)9781138028715

Condition monitoring (CM) and fault detection of the drive-train and power electronics are two very important tasks which are necessary in order to maintain the reliability of industrial wind turbines. Unexpected failures, expensive repair and downtime costs can be avoided or limited through effective remote CM technology. The consortium of the FP7 OPTIMUS project is involved in the development and demonstration of an integrated CM system based on a modular design, which enables reliable diagnosis and prognosis of the power electronic faults and drive-trains. CM systems have been installed onACCIONAAW-1500 wind turbines to continuously evaluate the condition of power electronics and drive-trains. Some of the key results and analysis methodologies employed are presented in this paper for the power converters. © 2015 Taylor & Francis Group, London.

关键词： Wind turbines

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Optimizing matrix mapping with data dependent kernel for image classification

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Journal of Information Hiding and Multimedia Signal Processing 2014年第1期5卷 72-79页

作者： Zhang, De-Long Qiao, Jiaqing Li, Jun-Bao Qiao, Li-Yan Chu, Shu-Chuan Roddick, John F. Department of Automatic Test and Control Harbin Institute of Technology China School of Computer Science Engineering and Mathematics Flinders University GPO Box 2100 Adelaide SA 5001 Australia

Kernel based nonlinear feature extraction is feasible to extract the feature of image for *** current kernel-based method endures two problems: 1) kernel-based method is to use the data vector through transforming the image matrix into vector, which will cause the store and computing burden;2) the parameter of kernel function has the heavy influences on kernel based learning method. In order to solve the two problems, we present the method of optimizing matrix mapping with data dependent kernel for feature extraction of the image for classification. The method implements the algorithm without transforming the matrix to vector, and it adaptively optimizes the parameter of kernel for nonlinear mapping. The comprehensive experiments are implemented evaluate the performance of the algorithms. © 2014.

关键词： Matrix algebra

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Recursive Identification of Chain Dynamics in Hidden Markov Models Using Non-Negative Matrix Factorization

Recursive Identification of Chain Dynamics in Hidden Markov ...

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IEEE Annual Conference on Decision and control

作者： Robert Mattila Vikram Krishnamurthy Bo Wahlberg Department of Automatic Control and ACCESS School of Electrical Engineering KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Department of Electrical and Computer Engineering University of British Columbia BC V6T 1Z4 Vancouver Canada

ISBN: (纸本)9781479978878

Hidden Markov Models (HMMs) and associated Markov modulated time series are widely used for estimation and control in e.g. robotics, econometrics and bioinformatics. In this paper, we modify and extend a recently proposed approach in the machine learning literature that uses the method of moments and a Non-Negative Matrix Factorization (NNMF) to estimate the parameters of an HMM. In general, the method aims to solve a constrained non-convex optimization problem. In this paper, it is shown that if the observation probabilities of the HMM are known, then estimating the transition probabilities reduces to a convex optimization problem. Three recursive algorithms are proposed for estimating the transition probabilities of the underlying Markov chain, one of which employs a generalization of the Pythagorean trigonometric identity to recast the problem into a non-constrained optimization problem. Numerical examples are presented to illustrate how these algorithms can track slowly time-varying transition probabilities.

关键词： TRANSITION PROBABILITIES matrix decomposition Nonnegative METHOD OF MOMENTS Trigonometric identity Bioinformatics

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Predictive Gradient Iterative Learning control

Predictive Gradient Iterative Learning Control

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IEEE Annual Conference on Decision and control

作者： Bing Chu David H Owens Christopher T Freeman school of Electronic and Computer Science University of Southampton Southampton SO17 1BJ UK Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street Sheffield S1 3JD UK

ISBN: (纸本)9781479978878

Iterative learning control (ILC) is a control design method for high performance trajectory tracking. Most existing results achieve this by learning from information collected over past executions of the task (named trials). This paper proposes a novel gradient based ILC design which updates the control input by learning not only from past trials but also from the predicted future trials using plant model knowledge. It is shown that by including information from predicted future trials, the designed ILC controller is less short-sighted and therefore better performance can be achieved. Analysis of the algorithm's properties reveals potentially substantial benefit in terms of convergence speed;the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

关键词： control input design approach model uncertainty experimentation Trials Myopia Converge plant models

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Stochastic Packet Scheduling for Optimal Parameter Estimation

Stochastic Packet Scheduling for Optimal Parameter Estimatio...

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IEEE Annual Conference on Decision and control

作者： Duo Han Keyou You Lihua Xie Junfeng Wu Ling Shi School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Automation Tsinghua University Beijing 100084 China Department of Automatic Control The Royal Institute of Technology (KTH) Stockholm Sweden Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9781479978878

In this paper we consider optimal parameter estimation with a constrained packet transmission rate. Due to the limited battery power and the traffic congestion over a large sensor network, each sensor is required to discard some packets and save transmission times. We propose a packet-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler in [1], our stochastic packet scheduling is novelly designed to maintain the computational simplicity of the resulting maximum-likelihood estimator (MLE). This results in a nice feature that the MLE is still able to be recursively computed in a closed form, and the Cramer-Rao lower bound (CRLB) can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the optimal parameters of the scheduling policy under which the estimation performance is comparable to the standard MLE (with full measurements) even with a moderate transmission rate. Numerical simulations are included to show the effectiveness.

关键词： Packet optimal parameter Parameter estimation packet schedule Battery power supply scheduling policy traffic congestion Sensors Sensor networks closed forms Stochastic

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Analysis and optimization of centralized sequential channel sensing in cognitive radio networks 20

Analysis and optimization of centralized sequential channel ...

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20th European Wireless Conference, EW 2014

作者： Shokri-Ghadikolaei, Hossein Yaghoubi, Forough Fischione, Carlo Automatic Control Department KTH Royal Institute of Technology Stockholm10044 Sweden School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9783800736218

Effective spectrum sensing strategies enable cognitive radios to enhance the spectrum efficiency. In this paper, modeling, performance analysis, and optimization of spectrum handoff in a centralized cognitive radio network are studied. More specifically, for a given sensing order, the average throughput of secondary users and average interference level among the secondary and primary users are evaluated for a cognitive radio network with only one secondary user. By a Markov chain analysis, a network with multiple secondary users performing cooperative spectrum sensing is modeled, and the above performance metrics are derived. Then, a maximization of the secondary network performance in terms of throughput while keeping under control the average interference is formulated. Finally, numerical results validate the analytical derivations and show that optimally tuning sensing time significantly enhances the performance of the spectrum handoff. Also, we observe that exploiting OR rule for cooperative spectrum sensing provides a higher average throughput compared to AND rule. © VDE VERLAG GMBH, Berlin, Offenbach, Germany.

关键词： Cognitive radio

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Collective control of multiple unicycle agents with non-identical constant speeds: Tracking control and performance limitation

Collective control of multiple unicycle agents with non-iden...

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Conference on control Applications

作者： Zhiyong Sun Georg S. Seyboth Brian D. O. Anderson Shandong Computer Science Center SCSC (National Supercomputer Center in Jinan) and Shandong Provincial Key Laboratory of Computer Networks Jinan China Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70550 Stuttgart Germany National ICT Australia and Research School of Engineering The Australian National University Canberra ACT 0200 Australia

ISBN: (纸本)9781479977970

This paper discusses collective control of multiple unicycle-type vehicles with nonholonomic constraints and nonidentical constant speeds, with focus on the design of a tracking controller for a desired target's position and velocity. The tracking control task is divided into several sub-tasks. The group centroid is controlled to track a desired reference velocity, while the reference velocity is constructed with information from the target's position and velocity so that the group centroid can successfully track both the position and velocity of a target vehicle. In order to keep all the agents close to the centroid within a reasonable spacing, an additive spacing controller is devised. We also discuss in detail the performance limitation and trade-offs of the tracking control due to the constraint of non-identical and constant speeds in such a heterogeneous agent group.

关键词： position velocity reasonable

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