The Experts below are selected from a list of 9144 Experts worldwide ranked by ideXlab platform
J.s. Chai - One of the best experts on this subject based on the ideXlab platform.
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A highly Parallel Method for transient stability analysis
IEEE Transactions on Power Systems, 1990Co-Authors: M. La Scala, A. Bose, D.j. Tylavsky, J.s. ChaiAbstract:A Method for transient stability simulation is presented that aims to exploit the maximum degree of Parallelism that the problem presents. The transient stability problem is viewed as a coupled set of nonlinear algebraic and differential equations; by applying a discretization Method such as the trapezoidal rule, the overall algebraic-differential set of equations is thus transformed into an unique algebraic problem at each time step. A solution that considers every time step, not in a sequential way but concurrently, is suggested. The solution of this set of equations with a relaxation-type indirect Method gives rise to a highly Parallel algorithm. The Method can handle all the typical dynamic models of realistic power system components. Test results are presented and shown to favorably compare with those obtained with the sequential dishonest Newton algorithm for realistic power systems.
M. La Scala - One of the best experts on this subject based on the ideXlab platform.
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A highly Parallel Method for transient stability analysis
IEEE Transactions on Power Systems, 1990Co-Authors: M. La Scala, A. Bose, D.j. Tylavsky, J.s. ChaiAbstract:A Method for transient stability simulation is presented that aims to exploit the maximum degree of Parallelism that the problem presents. The transient stability problem is viewed as a coupled set of nonlinear algebraic and differential equations; by applying a discretization Method such as the trapezoidal rule, the overall algebraic-differential set of equations is thus transformed into an unique algebraic problem at each time step. A solution that considers every time step, not in a sequential way but concurrently, is suggested. The solution of this set of equations with a relaxation-type indirect Method gives rise to a highly Parallel algorithm. The Method can handle all the typical dynamic models of realistic power system components. Test results are presented and shown to favorably compare with those obtained with the sequential dishonest Newton algorithm for realistic power systems.
Zhaohui Wu - One of the best experts on this subject based on the ideXlab platform.
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top k automatic service composition a Parallel Method for large scale service sets
IEEE Transactions on Automation Science and Engineering, 2014Co-Authors: Shuiguang Deng, Longtao Huang, Zhaohui WuAbstract:Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing Methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded Methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a Parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.
Masaru Fukushi - One of the best experts on this subject based on the ideXlab platform.
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CANDAR - A Memory Efficient Parallel Method for Voxel-Based Multiview Stereo
2014 Second International Symposium on Computing and Networking, 2014Co-Authors: Yudai Tateishi, Masaru FukushiAbstract:Voxel-based multi-view stereo is a Method for reconstructing a 3D scene from a set of images taken at different viewpoints. This Method reconstructs the 3D scene by defining a voxel space and determining the voxels corresponding with object surfaces. Due to the excessive processing time, some Parallel Methods have been proposed based on the voxel space division approach. However, a naive implementation of the existing Parallel Methods requires a large amount of memory and limits the use of a large scale voxel space, thus resulting in a low-quality reconstruction. To overcome this problem, this paper presents a memory-efficient Parallel Method for the voxel-based multi-view stereo. To reduce the memory usage in each processing element (PE), our Method introduces a correspondence table which records the assigned PE and the location for each voxel. Experimental results show that although the processing time is slightly increased, the proposed Method enables the reconstruction in large scale voxel space (up to 700×700×700) which is unattainable by the existing Methods.
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A Memory Efficient Parallel Method for Voxel-Based Multiview Stereo
2014 Second International Symposium on Computing and Networking, 2014Co-Authors: Yudai Tateishi, Masaru FukushiAbstract:Voxel-based multi-view stereo is a Method for reconstructing a 3D scene from a set of images taken at different viewpoints. This Method reconstructs the 3D scene by defining a voxel space and determining the voxels corresponding with object surfaces. Due to the excessive processing time, some Parallel Methods have been proposed based on the voxel space division approach. However, a naive implementation of the existing Parallel Methods requires a large amount of memory and limits the use of a large scale voxel space, thus resulting in a low-quality reconstruction. To overcome this problem, this paper presents a memory-efficient Parallel Method for the voxel-based multi-view stereo. To reduce the memory usage in each processing element (PE), our Method introduces a correspondence table which records the assigned PE and the location for each voxel. Experimental results show that although the processing time is slightly increased, the proposed Method enables the reconstruction in large scale voxel space (up to 700×700×700) which is unattainable by the existing Methods.
Umpei Nagashima - One of the best experts on this subject based on the ideXlab platform.
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a Parallel Method for large sparse generalized eigenvalue problems using a gridrpc system
Future Generation Computer Systems, 2008Co-Authors: Tetsuya Sakurai, Yoshihisa Kodaki, Hiroto Tadano, Daisuke Takahashi, Mitsuhisa Sato, Umpei NagashimaAbstract:In this paper we present a master-worker type Parallel Method for finding several eigenvalues and eigenvectors of a generalized eigenvalue problem [email protected], where A and B are large sparse matrices. A moment-based Method that finds all of the eigenvalues that lie inside a given domain is used. In this Method, a small matrix pencil that has only the desired eigenvalues is derived by solving large sparse systems of linear equations constructed from A and B. Since these equations can be solved independently, we solve them on remote servers in Parallel. This approach is suitable for master-worker programming models. We have implemented and tested the proposed Method in a grid environment using a grid RPC (remote procedure call) system called OmniRPC. The performance of the Method on PC clusters that were used over a wide-area network was evaluated.
