Parallel Vector Computing Technique for Discovering Communities on the Very Large Scale Web Graph

2003 ◽  
pp. 151-160 ◽  
Author(s):  
Kikuko Kawase ◽  
Minoru Kawahara ◽  
Takeshi Iwashita ◽  
Hiroyuki Kawano ◽  
Masanori Kawazawa
Keyword(s):  
Large Scale ◽  
Computing Technique ◽  
Web Graph ◽  
Vector Computing

A Hybrid Swarm Optimization Algorithm for Complex Assignment Problem

2010 ◽  
Vol 26-28 ◽  
pp. 1151-1154
Author(s):  
Zong Li Liu ◽  
Jie Cao ◽  
Zhan Ting Yuan
Keyword(s):  
Assignment Problem ◽  
Production Scheduling ◽  
Large Scale ◽  
Optimization Problems ◽  
Graph Model ◽  
Task Assignment ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Computing Technique ◽  
Task Assignment Problem

The optimization of complex systems, such as production scheduling systems and control systems, often encounters some difficulties, such as large-scale, hard to model, time consuming to evaluate, NP-hard, multi-modal, uncertain and multi-objective, etc. It is always a hot research topic in academic and engineering fields to propose advanced theory and effective algorithms. As a novel evolutionary computing technique, particle swarm optimization (PSO) is characterized by not being limited by the representation of the optimization problems, and by global optimization ability, which has gained wide attentation and research from both academic and industry fields. The task assignment problem in the enterprise with directed graph model is presented. Task assignment problem with buffer zone is solved via a hybrid PSO algorithm. Simulation result shows that the model and the algorithm are effective to the problem.

scholarly journals Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Chao Dong ◽  
Lianfang Tian
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Message Passing Interface ◽  
Hyperspectral Image ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Training Procedure ◽  
Data Set ◽  
Computing Technique

Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine. The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image. However, RVM is not widespread influenced by its slow training procedure. To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper. The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations. The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library. The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms. It shows that the parallel RVMs accelerate the training procedure obviously.

scholarly journals Parallel ACO with a Ring Neighborhood for Dynamic TSP

2012 ◽  
Vol 5 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Camelia M. Pintea ◽  
Gloria Cerasela Crisan ◽  
Mihai Manea
Keyword(s):  
Ant Colony Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Large Data ◽  
Ant Colony ◽  
Data Sets ◽  
Routing Problem ◽  
Computing Technique ◽  
Slight Movement ◽  
Scale Optimization

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject.

scholarly journals Large scale model predictions on the effect of GDL thermal conductivity and porosity on PEM fuel cell performance

2017 ◽  
Author(s):  
Obaid ur Rehman ◽  
Amber Fishan Zafar
Keyword(s):  
Thermal Conductivity ◽  
Fuel Cell ◽  
Large Scale ◽  
Mesh Size ◽  
Pem Fuel Cell ◽  
Gas Diffusion ◽  
Scale Model ◽  
Membrane Electrode ◽  
Computing Technique ◽  
Large Scale Model

<p class="PaperAbstract">The performance of proton exchange membrane (PEM) fuel cell majorly relies on properties of gas diffusion layer (GDL) which supports heat and mass transfer across the membrane electrode assembly. A novel approach is adopted in this work to analyze the activity of GDL during fuel cell operation on a large-scale model. The model with mesh size of 1.3 million computational cells for 50 cm<sup>2</sup> active area was simulated by parallel computing technique via computer cluster. Grid independence study showed less than 5% deviation in criterion parameter as mesh size was increased to 1.8 million cells. Good approximation was achieved as model was validated with the experimental data for Pt loading of 1 mg/cm<sup>2</sup>. The results showed that GDL with higher thermal conductivity prevented PEM from drying and led to improved protonic conduction. GDL with higher porosity enhanced the reaction but resulted in low output voltage which demonstrated the effect of contact resistance. In addition, the compressive force reduced the porosity under the rib regions which resulted in lower gas diffusion and heat and water accumulation.</p>

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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