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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Jakub Gasior Franciszek Seredynski Systems Research Institute Polish Academy of Sciences Warsaw Poland Dept. of Math. & Natural Sci. Cardinal Stefan Wyszynski Univ. Warsaw Poland

ISBN: (纸本)9781467376853

We present in this paper a general framework to study issues of effective load balancing and scheduling in highly parallel and distributed environments such as currently built Cloud computing systems. We propose a novel approach based on the concept of the Sandpile cellular automaton: a decentralized multi-agent system working in a critical state at the edge of chaos. Our goal is providing fairness between concurrent job submissions by minimizing slowdown of individual applications and dynamically rescheduling them to the best suited resources. The algorithm design is experimentally validated by a number of numerical experiments showing the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources and its ability to react to dynamic changes in real time.

关键词： Load management Dynamic scheduling Heuristic algorithms Load modeling Automata Processor scheduling

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Factory Planning Model of Aircraft Engine Transmission Parts based on the Genetic Annealing Algorithm

Factory Planning Model of Aircraft Engine Transmission Parts...

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international Conference on Computer Science and Network Technology

作者： Jie Jia Pei Qin China Aviation Planning and Design Institute(Group) Co. Ltd. Beijing 100120 China Aviation Planning and Design Institue (Group) Co. Ltd. Beijing 100120

ISBN: (纸本)9781467381741

According to the premature convergence and low searching efficiency of the standard simulated annealing algorithm in workshop planning applications, this paper proposes a factory planning model of aircraft engine transmission parts based on improved genetic algorithm optimized simulated annealing. First, the mechanism floatingpoint coding is referenced to arithmetic crossover operation for crossover operator of genetic algorithm, and then adaptive mutation operators are adopted to keep the diversity of population, and the global optimization of the algorithm is improved. Finally, transmission parts factory programming model is constructed according to the characteristic of aviation engine. The results showed that compared with simulated annealing algorithm, the genetic simulated annealing algorithm has good convergence in application to factory planning.

关键词： Aircraft engine transmission parts factory planning model simulated annealing algorithm improved genetic algorithm Aircraft engines simulated annealing algorithm Genetic Algorithm (GA) Industrial Plants optimization algorithms

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Texture image classification based on support vector machine and bat algorithm

Texture image classification based on support vector machine...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Zhiwei Ye Lie Ma Mingwei Wang Hongwei Chen Wei Zhao Beijing Key Laboratory of Urban Spatial Information Engineering Beijing China School of Computer Science Hubei University of Technology China

ISBN: (纸本)9781467383622

Texture is the vital feature for remote sensing image classification, however, it is hard to be described and recognized by computer vision. As a result, lots of approaches have been presented to identify texture image. Among these methods, support vector machine (SVM) is the most successfully used one, which takes advantages of avoiding local optimum, conquering dimension disaster with small samples. Nevertheless, the selection of the kernel function parameter and error penalty factor has impact on the precision of SVM notably. Some methods have been put forward to learn good parameters for SVM. However, the traditional tuning methods may be inefficient or not robust. Hence, a novel meta-heuristic-bat algorithm is suggested to acquire the optimal parameters for SVM in the paper. In final, experimental results on actual remote sensing texture images manifest that the proposed approach is robust, it is able to distinguish different texture images with high accuracy.

关键词： Support vector machines optimization Image classification Remote sensing Classification algorithms Kernel Sociology

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Synoptic video based human crowd behavior analysis for forensic video surveillance

Synoptic video based human crowd behavior analysis for foren...

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international Conference on Advances in Pattern Recognition (ICAPR)

作者： B. Yogameena K. Sindhu Priya Thiagarajar College of Engineering Madurai Tamil Nadu IN

Modeling human blobs in crowd for analyzing the behavior is an important issue for video surveillance and is a challenging task due to the unpredictability. Huge video dataset is captured by using various resources like surveillance cameras in many places including the public environment like railway station, airport etc. It is very time consuming to watch the whole video manually for forensic applications of analysis. Most of the computer vision algorithms are concentrating for real time solutions. But, still after the fact also, so many issues could not be analyzed well. For example, if one wants to analyze a specific activity in a video, watching the video for so many hours is hectic. In this paper, Video synopsis is used to represent a short video while preserving the essential activities for a long video. In the existing methodology, active objects are shifted along the time axis and hence the video is compressed much, it causes collisions among the moving objects. Therefore, compact video synopsis is proposed by using a spatiotemporal optimization, which will shift the active object along the space as well as time and thus avoid collision among them. This synthesized compact background is introduced by using multilevel patch relocation (MPR) method to provide a larger virtual motion space for shifted objects. The synoptic video is proposed here to detect if any anomalies of human crowd(s) is present in the scene in a quicker time. Experimental results obtained by using extensive dataset show that the proposed algorithm is effective in detecting anomalous events for uncontrolled environment of video surveillance.

关键词： Spatiotemporal phenomena optimization Streaming media Video surveillance Algorithm design and analysis Forensics

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A STOCHASTIC COORDINATE DESCENT PRIMAL-DUAL ALGORITHM AND applications 24

A STOCHASTIC COORDINATE DESCENT PRIMAL-DUAL ALGORITHM AND AP...

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IEEE international workshop on Machine Learning for Signal Processing (MLSP)

作者： Bianchi, Pascal Hachem, Walid Franck, Iutzeler Telecom Paris Tech Paris France CNRS LTCI Paris France Supelec Gif Sur Yvette France

ISBN: (纸本)9781479936946

First, we introduce a splitting algorithm to minimize a sum of three convex functions. The algorithm is of primal dual kind and is inspired by recent results of Vu and Condat. Second, we provide a randomized version of the algorithm based on the idea of coordinate descent. Finally, we address two applications of our method: (i) for stochastic minibatch optimization;and (ii) for distributed optimization.

关键词： Distributed optimization Large-scale Learning Coordinate Descent Consensus algorithms Primal-Dual Algorithm

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A parallel two-level domain decomposition based one-shot method for shape optimization problems

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international JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2014年第13期99卷 945-965页

作者： Chen, Rongliang Cai, Xiao-Chuan Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Colorado Dept Comp Sci Boulder CO 80309 USA

A two-level domain decomposition method is introduced for general shape optimization problems constrained by the incompressible Navier-Stokes equations. The optimization problem is first discretized with a finite element method on an unstructured moving mesh that is implicitly defined without assuming that the computational domain is known and then solved by some one-shot Lagrange-Newton-Krylov-Schwarz algorithms. In this approach, the shape of the domain, its corresponding finite element mesh, the flow fields and their corresponding Lagrange multipliers are all obtained computationally in a single solve of a nonlinear system of equations. Highly scalable parallel algorithms are absolutely necessary to solve such an expensive system. The one-level domain decomposition method works reasonably well when the number of processors is not large. Aiming for machines with a large number of processors and robust nonlinear convergence, we introduce a two-level inexact Newton method with a hybrid two-level overlapping Schwarz preconditioner. As applications, we consider the shape optimization of a cannula problem and an artery bypass problem in 2D. Numerical experiments show that our algorithm performs well on a supercomputer with over 1000 processors for problems with millions of unknowns. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： shape optimization multilevel methods parallel computing one-shot method finite element domain decomposition preconditioning inexact Newton

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Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

Online Stochastic Tensor Decomposition for Background Subtra...

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international Conference on Computer Vision workshops (ICCV workshops)

作者： Andrews Sobral Sajid Javed Soon Ki Jung Thierry Bouwmans El-hadi Zahzah University of La Rochelle France Kyungpook National University Korea School of Computer Science and Engineering

ISBN: (纸本)9781467397124

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mono or trichromatic cameras within the visible spectrum or near infrared part. Recent advances in multispectral imaging technologies give the possibility to record multispectral videos for video surveillance applications. Due to the specific nature of these data, many of the bands within multispectral images are often strongly correlated. In addition, processing multispectral images with hundreds of bands can be computationally burdensome. In order to address these major difficulties of multispectral imaging for video surveillance, this paper propose an online stochastic framework for tensor decomposition of multispectral video sequences (OSTD). First, the experimental evaluations on synthetic generated data show the robustness of the OSTD with other state of the art approaches then, we apply the same idea on seven multispectral video bands to show that only RGB features are not sufficient to tackle color saturation, illumination variations and shadows problem, but the addition of six visible spectral bands together with one near infrared spectra provides a better background/foreground separation.

关键词： Tensile stress Matrix decomposition Streaming media Sparse matrices optimization Video surveillance Robustness

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multilevel thresholding for image segmentation based on parallel distributed optimization 6

Multilevel thresholding for image segmentation based on para...

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6th international Conference on Soft Computing and Pattern Recognition (SoCPaR)

作者： Sandeli, Mohamed Batouche, Mohamed Univ Constantine 2 Fac NTIC Software & Informat Technol Dept Constantine Algeria Univ Constantine 2 Fac NTIC Comp Sci & Applicat Dept Constantine Algeria

ISBN: (纸本)9781479959341

Image segmentation is a platform for many high-level applications. It aims to identify the different homogeneous regions of an image. In our work, we cast the problem as a parallel distributed optimization task and we present a new method for image segmentation by multilevel thresholding based on generalized island model (GIM). This model is characterized by the cooperation of three metaheuristics namely Particle Swarm optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC). The cooperation is ensured by the use of a migration pattern the purpose of which is to avoid getting stuck in local optima. The effectiveness of the proposed method has been assessed and compared to each of the algorithms of the GIM model when used separately and other methods from the literature like Darwinian PSO (DPSO), Fractional-Order Darwinian PSO (FO-DPSO). The obtained results are very promising and show the positive impact of the use of a parallel distributed model. They also show that the proposed method competes and even outperforms the other methods.

关键词： Image segmentation Generalized Island Model multilevel thresholding Metaheuristics Parallel Distributed optimization

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optimization of a multilevel Checkpoint Model with Uncertain Execution Scales 14

Optimization of a Multilevel Checkpoint Model with Uncertain...

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international Conference on High Performance Computing, Networking, Storage and Analysis

作者： Di, Sheng Bautista-Gomez, Leonardo Cappello, Franck INRIA Rocquencourt France Argonne Natl Lab Argonne IL 60439 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781479955008

Future extreme-scale systems are expected to experience different types of failures affecting applications with different failure scales, from transient uncorrectable memory errors in processes to massive system outages. In this paper, we propose a multilevel checkpoint model by taking into account uncertain execution scales (different numbers of processes/cores). The contribution is threefold: (1) we provide an in-depth analysis on why it is difficult to derive the optimal checkpoint intervals for different checkpoint levels and optimize the number of cores simultaneously;(2) we devise a novel method that can quickly obtain an optimized solution-the first successful attempt in multilevel checkpoint models with uncertain scales;and (3) we perform both large-scale real experiments and extreme-scale numerical simulation to validate the effectiveness of our design. The experiments confirm that our optimized solution outperforms other state-of- the-art solutions by 4.3-88% on wall-clock length.

关键词： checkpointing multiprocessing systems numerical analysis optimisation checkpoint levels extreme-scale numerical simulation extreme-scale systems failure scales massive system outages multilevel checkpoint model optimal checkpoint intervals optimization processes/cores transient uncorrectable memory errors uncertain execution scales wall-clock length Analytical models Approximation algorithms Computational modeling Equations Heating Mathematical model optimization Multiprocessing systems checkpointing Approximation algorithms ANALYSIS MODELS Computational modeling Cell Cycle Checkpoints Checkpoints Mathematical Model Numerical Analysis Dermal denticles Heating Plant uncertain BALANCES multilayers

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multilevel scheme of dimensionality reduction for parallel global search algorithms 1

Multilevel scheme of dimensionality reduction for parallel g...

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1st international Conference on Engineering and Applied Sciences optimization, OPT-i 2014

作者： Barkalov, Konstantin A. Gergel, Victor P. State University of Nizhni Novgorod 23 Prospekt Gagarina Nizhni Novgorod603950 Russia

ISBN: (纸本)9789609999465

In this paper the problems of multidimensional multiextremal optimization and parallel methods of their solution are considered. Only a general assumption is made regarding the optimizable function: the function is preset algorithmically (in the form of an algorithm of computation of values by input parameters) and satisfies the Lipschitz condition with an a priori unknown constant (problems of this kind are often found in applications). Within the limits of the considered approach solution of multidimensional problems is reduced to solution of one-dimensional problems equivalent to multidimensional problems. For problem dimension reduction a multi-level scheme is proposed, which combines the ideas of Peanotype space filling curves and nested optimization. The offered parallel algorithm uses a multilevel scheme of dimension reduction for effective parallelizing;substantiation of the algorithm is provided. Results of numerical experiments confirm convergence and speedup of the parallel algorithm in comparison with its sequential prototype.

关键词： Global optimization

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