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共 588 条记录，以下是151-160 订阅

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Enhancing problem frames with trust and reputation for analyzing smart grid security requirements 1

引用

2nd international workshop on Smart Grid Security, SmartGridSec 2014

作者： Moyano, Francisco Fernández-Gago, Carmen Beckers, Kristian Heisel, Maritta Network Information and Computer Security Lab University of Malaga Malaga29071 Spain Paluno - The Ruhr Institute for Software Technology University of Duisburg-Essen Essen Germany

ISBN: (数字)9783319103297

ISBN: (纸本)9783319103280

Smart grids are expected to scale over millions of users and provide numerous services over geographically distributed entities. Moreover, smart grids are expected to contain controllable local systems (CLS) such as fridges or heaters that can be controlled using the network communication technology of the grid. Security solutions that prevent harm to the grid and to its stakeholders from CLS are essential. Moreover, traditional security approaches such as static access control systems cause a lot of administrative workload and are difficult to maintain in fast growing and changing systems. In contrast, trust management is a soft security mechanism that can reduce this workload significantly. Even though there is not any accepted definition of trust, it is agreed that it can improve decision-making processes under risk and uncertainty, improving in turn systems’ security. We use the problem frames notation to discuss requirements for a trust-based security solution concerning CLS. © Springer international Publishing Switzerland 2014.

关键词： Smart power grids

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Alternating direction method of multipliers for hierarchical basis approximators 1

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2nd workshop on Sparse Grids and Applications, SGA 2012

作者： Khakhutskyy, Valeriy Pflüger, Dirk Institute for Advanced Study Technische Universität München Lichtenbergstrasse 2a Garching85748 Germany Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstr. 3 Stuttgart70569 Germany

ISBN: (数字)9783319045375

ISBN: (纸本)9783319045368

Sparse grids have been successfully used for the mining of vast datasets with a moderate number of dimensions. Compared to established machine learning techniques like artificial neural networks or support vector machines, sparse grids provide an analytic approximant that is easier to analyze and to interpret. More important, they are based on a high-dimensional discretization of the feature space, are thus less data-dependent than conventional approaches, scale only linearly in the number of data points and are well-suited to deal with huge amounts of data. But with an increasing size of the datasets used for learning, computing times clearly can become prohibitively large for normal use, despite the linear scaling. Thus, efficient parallelization strategies have to be found to exploit the power of modern hardware. We investigate the parallelization opportunities for solving high-dimensional machine learning problems with adaptive sparse grids using the alternating direction method of multipliers (ADMM). ADMM allows us to split the initially large problem into smaller ones. They can then be solved in parallel while their reduced problem sizes can even be small enough for an explicitly assembly of the system matrices. We show the first results of the new approach using a set of problems and discuss the challenges that arise when applying ADMM to a hierarchical basis. © Springer international Publishing Switzerland 2014.

关键词： Neural networks

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Classification with probability density estimation on sparse grids 1

引用

2nd workshop on Sparse Grids and Applications, SGA 2012

作者： Peherstorfer, Benjamin Franzelin, Fabian Pflüger, Dirk Bungartz, Hans-Joachim Department of Informatics Technische Universität München Boltzmannstr. 3 Garching85748 Germany Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 Stuttgart70569 Germany

ISBN: (数字)9783319045375

ISBN: (纸本)9783319045368

We present a novel method to tackle the multi-class classification problem with sparse grids and show how the computational procedure can be split into an Offline phase (pre-processing) and a very rapid Online phase. For each class of the training data the underlying probability density function is estimated on a sparse grid. The class of a new data point is determined by the values of the density functions at this point. Our classification method can deal with more than two classes in a natural way and it provides a stochastically motivated confidence value which indicates how to rate the respond to a new point. Furthermore, the underlying density estimation method allows us to pre-compute the system matrix and store it in an appropriate format. This so-called Offline/Online splitting of the computational procedure allows an Online phase where only a few matrix vector products are necessary to learn a new, previously unseen training data set. In particular, we do not have to solve a system of linear equations anymore. We show that speed ups by a factor of several hundred are possible. A typical application for such an Offline/Online splitting is cross validation. We present the algorithm and the computational procedure for our classification method, report on the employed density estimation method on sparse grids and show by means of artificial and real world data sets that we obtain competitive results compared to the classical sparse grid classification method based on regression. © Springer international Publishing Switzerland 2014.

关键词： Probability density function

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Efficient pseudorecursive evaluation schemes for non-adaptive sparse grids 1

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2nd workshop on Sparse Grids and Applications, SGA 2012

作者： Buse, Gerrit Pflüger, Dirk Jacob, Riko TU München München Germany Institute for Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany ETH Zurich Zurich Switzerland

ISBN: (数字)9783319045375

ISBN: (纸本)9783319045368

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the international Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation. © Springer international Publishing Switzerland 2014.

关键词： parallel programming

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An Efficient Algorithm for Solving Eigenproblem

An Efficient Algorithm for Solving Eigenproblem

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The 13th international Symposium on distributed Computing and Applications to Business,engineering and Science(DCABES 2014)(第十三届分布式计算及其应用国际学术研讨会)

作者： Huirong Zhang Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese

ISBN: (纸本)9781479941681

In this paper,we consider second order elliptic ODE eigenproblems on general *** construct an efficient algorithm for computing the eigenvalue by using weighted mean combination of the linear finite element method and corresponding 2nd-order finite difference *** first take the arithmetic mean of the two *** we compute the quasi-optimal combined parameters for different eigenvalues to improve our efficient *** algorithm we construct convergence faster and have higher accuracy than the linear finite element method and corresponding 2nd-order finite difference *** numerical examples tested on both uniform meshes and nonuniform meshes are given to illustrate the computational cost of different numerical methods for solving eigenvalue *** efficiency,all the matrices use sparse storage in our algorithm.

关键词： Keywords-Efficient algorithm combinatorial quasioptimal eigenproblem

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An Efficient Algorithm for Solving Eigenproblem

An Efficient Algorithm for Solving Eigenproblem

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13th international Symposium on distributed Computing and Applications to Business,engineering & Science（DCABES 2014）

作者： Huirong Zhang Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

In this paper, we consider second order elliptic ODE eigenproblems on general grids. We construct an efficient algorithm for computing the eigenvalue by using weighted mean combination of the linear finite element method and corresponding 2nd-order finite difference *** first take the arithmetic mean of the two methods. Then we compute the quasi-optimal combined parameters for different eigenvalues to improve our efficient algorithm. The algorithm we construct convergence faster and have higher accuracy than the linear finite element method and corresponding 2nd-order finite difference method. Some numerical examples tested on both uniform meshes and nonuniform meshes are given to illustrate the computational cost of different numerical methods for solving eigenvalue *** efficiency, all the matrices use sparse storage in our algorithm.

关键词： Efficient algorithm combinatorial quasioptimal eigenproblem.

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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2nd international workshop on Model-Driven engineering for High Performance and CLoud Computing, MDHPCL 2013 - Co-located with 16th international Conference on Model Driven engineering Languages and systems, MODELS 2013

The proceedings contain 8 papers. The topics discussed include: 2nd MDHPCL: model-driven engineering for high performance and cloud computing;towards a solution avoiding vendor lock-in to enable migration between cloud platforms;modeling cloud architectures as interactive systems;vehicleFORGE: a cloud-based infrastructure for collaborative model-based design;a model-driven approach for price/performance tradeoffs in cloud-based MapReduce application deployment;towards domain-specific testing languages for software-as-a-service;architecture framework for mapping parallel algorithms to parallel computing platforms;and model-driven transformations for mapping parallel algorithms on parallel computing platforms.

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Residential Electrical Demand Forecasting in Very Small Scale: An Evaluation of Forecasting Methods

Residential Electrical Demand Forecasting in Very Small Scal...

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2nd international workshop on software engineering Challenges for the Smart Grid (SE4SG)

作者： Marinescu, Andrei Harris, Colin Dusparic, Ivana Clarke, Siobhan Cahill, Vinny Univ Dublin Trinity Coll Sch Comp Sci & Stat Distributed Syst Grp Dublin 2 Ireland

ISBN: (纸本)9781467362801

Applications such as generator scheduling, household smart device scheduling, transmission line overload management and microgrid islanding autonomy all play key roles in the smart grid ecosystem. Management of these applications could benefit from short-term load prediction, which has been successfully achieved on large-scale systems such as national grids. However, the scale of the data for analysis is much smaller, similar to the load of a single transformer, making prediction difficult. This paper examines several prediction approaches for day and week ahead electrical load of a community of houses that are supplied by a common residential transformer, in particular: artificial neural networks;fuzzy logic;auto-regression;auto-regressive moving average;auto-regressive integrated moving average;and wavelet neural networks. In our evaluation, the methods use pre-recorded electrical load data with added weather information. Data is recorded from a smart-meter trial that took place during 2009-2010 in Ireland, which registered individual household consumption for 17 months. Two different scenarios are investigated, one with 90 houses, and another with 230 houses. Results for the two scenarios are compared and the performances of the evaluated prediction methods are discussed.

关键词： autoregressive moving average processes load forecasting neural nets power engineering computing smart meters

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Design of software for high-speed inkjet-printing system based on distributed

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2012 2nd international Conference on Machinery Electronics and Control engineering, ICMECE 2012

作者： Qi, Liwei Guo, Peiyuan NO.33 Fucheng Road Haidian District Beijing China

ISBN: (纸本)9783037856840

With the rapid development of the printing industry, the speed of the inkjet printer has soon become the focus. In view of the high requirements of printing speed, the in-depth analysis of the bottleneck, which restricts the press printing speed, is made. As the computer hard disk access data speed is limited at this stage, the high-speed inkjet printing system software based on distributed processing is developed. This software system based on object-oriented programming theory, by taking VS2008 as the development platform and using C/S system architecture, realizes multiple servers in parallel processing, data transfer and printing capabilities. The practical application proved that it deed improve the printing speed effectiveness and feasibility. © (2013) Trans Tech Publications, Switzerland.

关键词： software design

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Computing mode study of large-scale power grid online analysis software based on cloud computing technology

Computing mode study of large-scale power grid online analys...

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2nd international Conference on Mechanical engineering, Industrial Electronics and Informatization, MEIEI 2013

作者： Li, Jing Luo, Ya Di Xu, Jiea Jia, Yu Pei China Electric Power Research Institute Beijing 100192 China

ISBN: (纸本)9783037858905

This paper construct a large-scale power grid online analysis architecture based on "Dispatch Cloud' architecture" and combined with the power system characteristics and large grid dispatch system online analysis software features, it propose power grid online analysis software wide-area multi-level distributed parallel computing mode based on Dispatch Cloud Platform which is service oriented architecture(SOA). In this mode the coordination manage needed is achieved in the way of service encapsulation based SOA, including, data and model management services, computing resource management services, the job management and parallel computing services, In this mode the coordination manage needed is achieved in the way of service encapsulation based on SOA, including, data and model management services, computing resource management services, the job management and parallel computing services. which will provide theoretical and technical support to the large-scale power grid Online analysis software design and development Using Cloud technology. © (2013) Trans Tech Publications, Switzerland.

关键词： Resource allocation

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