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2015 International Conference on Computer Information Systems and Industrial Applications(CISIA2015)

作者： X.T.Ding C.X.Yang X.W.Ren P J.Ren Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781510805750

Most of routing algorithms for On-chip communication are neither application-aware nor routing packets using multiple *** addition,they hardly consider link bandwidth variation resulting from widely applied global asynchronous local synchronous(GALS) *** this paper,we propose a bandwidth-based application-aware multipath routing(BAMR)algorithm to assign multiple routing paths by leveraging the knowledge of application and network bandwidth *** the increase of number of flows resulting from the split of flows,we present a new method named dynamic-amount fixed-number(DAFN) flow control mechanism to avoid *** compare our algorithm with XY,YX,and OITURN with synthetic traffic patterns and traffic trace of parallel implementation of *** demonstrate that BAMR achieves higher throughput with decrease in ***,the proposed algorithm achieves better workload balance by distributing traffic over multiple paths.

关键词： on-chip network GALS multiple routing

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Renumber coevolutionary multiswarm particle swarm optimization for multi-objective workflow scheduling on cloud computing environment 15

Renumber coevolutionary multiswarm particle swarm optimizati...

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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Li, Hai-Hao Chen, Zong-Gan Zhan, Zhi-Hui Du, Ke-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education P.R. China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China Engineering Research Center of Supercomputing Engineering Software Ministry of Education P.R. China

ISBN: (纸本)9781450334884

Resources scheduling is a significant research topic in cloud computing, which is often modeled as a cost-minimization and deadline-constrained workflow scheduling model. This is a constrained single objective problem that to minimize the overall workflow execution cost while meeting deadline constraints. In this paper, we offer a new horizon to convert this single-objective problem to a multi-objective problem and present coevolutionary multiswarm particle swarm optimization (CMPSO) to find the non-dominated solutions with different execute cost and time. Meanwhile, the renumber strategy is adopted in CMPSO to make the learning efficient. CMPSO is compared with a renumber PSO (RNPSO) by setting the execute time in the CMPSO's nondominated solutions as the deadline constraint of RNPSO. Results show that CMPSO not only offers many non-dominated solutions with different prices and execute time, but also obtains better solution than RNPSO under a same deadline. Copyright is held by the owner/author(s).

关键词： Cloud computing

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IDFT:An Intermediate Node Based Deterministic Fault Tolerant Routing in 2D Mesh

IDFT:An Intermediate Node Based Deterministic Fault Tolerant...

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2015 International Conference on Computer Information Systems and Industrial Applications(CISIA2015)

作者： C.X.Yang X.W.Ren X.T.Ding P.J.Ren Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University State Key Laboratory of Mathematical Engineering and Advanced Computing Institute of Artificial Intelligence and Robotics\Xi'an Jiaotong University

ISBN: (纸本)9781510805750

In order to alleviate the problem raised in Gomez's method[1],where messages with different source-destination pairs have great probability choosing the overlapped paths therefore,new transmission bottleneck might be generated nearby,we present an intermediate node based deterministic fault-tolerant routing,named *** method selects the qualified intermediate node among all the candidates by its residual bandwidth without over-stressing any particular *** addition,deterministic routing is applied along both subpaths to guarantee deadlock-free and in-order message *** thereby achieves traffic workload balance across the network and graceful performance degradation even in the presence of faults.

关键词： fault tolerance bandwidth utilization deterministic routing

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The 5th International Conference on Biomedical engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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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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A pso approach for software project planning 14

A pso approach for software project planning

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Jia, Ya-Hui Chen, Wei-Neng Hu, Xiao-Min Department of Computer Science Sun Yat-sen University China School of Advanced Computing Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software Ministry of Education China School of Public Health Sun Yat-sen University China

ISBN: (纸本)9781450328814

Search-based software project management is a hot research point in software engineering. Based on the event-based scheduler (EBS) we have proposed in previous work [1], this paper intends to further propose a two-phase particle swarm optimization approach which uses a set-based representation for task scheduling and an integer representation for workload assignment scheduling to improve planning performance. Experimental results on 83 instances demonstrate the effectiveness of the proposed approach.

关键词： Scheduling

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Deadline Constrained Cloud computing Resources Scheduling through an Ant Colony System Approach

Deadline Constrained Cloud Computing Resources Scheduling th...

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International Conference on Cloud computing Research and Innovation (ICCCRI)

作者： Zong-Gan Chen Zhi-Hui Zhan Hai-Hao Li Ke-Jing Du Jing-Hui Zhong Yong Wee Foo Yun Li Jun Zhang Department of Computer Science Sun Yat-sen University Guangzhou P. R. China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing P.R. China Ministry of Education Engineering Research Center of Supercomputing Engineering Software P.R. China School of Computer Science and Engineering South China University of Technology Guangzhou P. R. China School of Computer Engineering Nanyang Technological University Singapore School of Engineering University of Glasgow Glasgow U.K. Nanyang Polytechnic School of Engineering Singapore

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions.

关键词： Cloud computing Processor scheduling Encoding Scheduling Optimization Heuristic algorithms Schedules

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Normalization group brain storm optimization for power electronic circuit optimization 14

Normalization group brain storm optimization for power elect...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Zhang, Guang-Wei Zhan, Zhi-Hui Du, Ke-Jing Chen, Wei-Neng Department of Computer Science Sun Yat-sen University Guangzhou 510275 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software Ministry of Education China Department of Management Science City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781450328814

This paper proposes a novel normalization group strategy (NGS) to extend brain storm optimization (BSO) for power electronic circuit (PEC) design and optimization. As different variables in different dimensions of the PEC represent different circuit components such as resistor, capacitor, or inductor, they have different physical significances and various search space that are even not in comparable range. Therefore, the traditional group method used in BSO, which is based on the solution position information, is not suitable when solving PEC. In order to overcome this issue, the NGS proposed in this paper normalizes different dimensions of the solution to the same comparable range. This way, the grouping operator of BSO can work when using BSO to solve PEC. The NGS based BSO (NGBSO) approach has been implemented to optimize the design of a buck regulator in PEC. The results are compared with those obtained by using genetic algorithm (GA) and particle swarm optimization (PSO). Results show that the NGBSO algorithm outperforms GA and PSO in our PEC design and optimization study. Moreover, the NGS can be regarded as an efficient method to extend BSO to real-world application problems whose dimensions are with different physical significances and search ranges.

关键词： Brain

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Research on fractional order controller based on best rational approximation approach

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Journal of Convergence Information Technology 2012年第4期7卷 50-58页

作者： Zhao, Huimin Nie, Bing Li, Wen Deng, Wu Software Institute Dalian Jiaotong University Dalian 116028 China Key Laboratory of Numerical Simulation in the Sichuan Provincial College Neijiang Normal University Neijiang 641112 China Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Zigong 643000 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Electrical Engineering Chongqing University Chongqing 400044 China Key Laboratory of advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China

Introduce the basic concept of best rational approximation and constructing procedure of the best rational approximation function in Laplace domain. By an illustrative example the design method of FOC based on best rational approximation is discussed. The performance comparison of transfer functions between the FOC obtained by best rational approximation method and conventional PID shows that it is effective to apply the method in design of FOC, which can guarantee maximum absolute error of amplitude frequency characteristic of approximation function in a given error tolerance.

关键词： Rational functions

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A new Gaussian Radial Basis Function Neural Network controller for induction motor control drives

A new Gaussian Radial Basis Function Neural Network controll...

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Mediterranean Conference on Control and Automation (MED)

作者： L. Barazane M. Laribi M.M Krishan R. Ouiguini Faculty of Electronic and Computing University of the Sciences and Technology Houari Boumediene (USTHB) Algiers Algeria Department of Electronic Engineering Polytechnic National School of Algiers Algiers Algeria Engineering of information and measurement systems Al-Balqa Applied University Jordan Laboratory of Robotic and artificial Intelligence Centre for Development of Advanced Technologies Algiers Algeria

The principal objective of this work consists in reducing the chattering phenomenon and ensuring more improvement of performances the proposed control scheme especially ensuring the decoupling between the control of the two principle variables of the motor “speed and flux” and also the system's robustness towards parameter's variations and external perturbations. In fact and as a first step a control scheme based on the combination of feedback linearization control is first proposed. Then, and in order to ensure more robustness of the process towards parameter's variations and external perturbation...ect, sliding mode controllers are used. These controllers are based on a piecewise function for smoothing in order to reduce the effect of the chattering phenomenon which is harmful of the actuator. However, this smooth function is relied closely to the upper bound of uncertainties, which include parameter variations and external disturbances. So, fuzzy sliding mode controllers are investigated to solve this difficulty based on the method introduced by Ben-Galia et al. By replacing the gain and the signum of the attractivity function of sliding mode controllers by fuzzy maps. However, it seems that this technique couldn't reduced the chattering considerably which let the authors to propose another type of controllers based on Gaussian Radial Basis Function Neural Network (GRBFNN) which revealed some very interesting features and prove that such controllers are efficients to attempt the required objectives.

关键词： Sliding mode control Induction motors Robustness Switches Neurons Process control

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