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检索条件"主题词=Algorithm Design And Analysis"

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2PCP: Two-Phase CP Decomposition for Billion-Scale Dense Tensors

2PCP: Two-Phase CP Decomposition for Billion-Scale Dense Ten...

引用

IEEE International Conference on Data Engineering

作者： Xinsheng Li Shengyu Huang K. Selcuk Candan Maria Luisa Sapino Arizona State University Tempe 85287 USA

ISBN: (纸本)9781509020218

Tensors are multi-dimensional arrays - consequently, tensor decomposition operations (CP and Tucker) are the bases for many high-dimensional data analysis tasks, from clustering, trend detection, anomaly detection, to correlation analysis in various application domains, including science and engineering. One key problem with tensor decomposition is its computational complexity and space requirements. Especially, as the relevant data sets get denser, in-memory schemes for tensor decomposition become increasingly ineffective;therefore out-of-core (secondary-memory supported, potentially parallel) computing is necessitated. However, existing techniques do not consider the I/O and network data exchange costs that out-of-core execution of the tensor decomposition operation will incur. In this paper, we note that when this operation is implemented with the help of secondary-memory and/or multiple servers to tackle the memory limitations, we would need intelligent buffer-management and task-scheduling techniques which take into account the cost of bringing the relevant blocks into the buffer to minimize I/O in the system. In this paper, we introduce 2PCP, a two-phase, block-based CP decomposition system with intelligent buffer sensitive task scheduling and buffer management mechanisms. 2PCP aims to reduce I/O costs in the analysis of relatively dense tensors common in scientific and engineering applications. Experiment results compare with current state of art tensor decomposition algorithms and show that our algorithms can significantly reduce the amount of I/O and execution time while maintaining decomposition accuracy.

关键词： Tensile stress Matrix decomposition Arrays Data analysis Servers algorithm design and analysis Optimization

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An exact approach for the container retrieval problem to reduce crane's trajectory

An exact approach for the container retrieval problem to red...

引用

IEEE International Conference on Intelligent Transportation Systems

作者： Andresson da Silva Firmino Ricardo Martins de Abreu Silva Valeria Cesario Times Center for Informatics Federal University of Pernambuco 50740-560 Recife Brazil

ISBN: (纸本)9781509018901

The container retrieval problem (CRP) is an important issue to achieve high yard operational efficiency in a container terminal system. The CRP aims to find an optimal sequence of operations for the crane to retrieve all the containers from the bay according to a predefined order. An optimal sequence of operations is obtained by either reducing the number of container relocations or reducing the total travel distance performed by the crane, i.e., crane's trajectory. In contrast to previous studies discussed in the literature, in this study, the optimization goal is to minimize the trajectory undertaken by crane. In this regard, this paper proposes two exact optimization algorithms based on Dijkstra's algorithm and A~* search algorithm frameworks. The latter includes proposal of an intelligent strategy to explore the most promising nodes in the search tree. The experimental results show that the proposed algorithms achieve an optimal solution in a shorter computation time when applied to realistic scenarios.

关键词： Containers Cranes Optimization Heuristic algorithms Trajectory Marine vehicles algorithm design and analysis

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Assessing Big Data SQL Frameworks for Analyzing Event Logs

Assessing Big Data SQL Frameworks for Analyzing Event Logs

引用

Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Markku Hinkka Teemu Lehto Keijo Heljanko Department of Computer Science Aalto University School of Science Finland QPR Software Plc Finland Aalto-yliopisto Aalto FI

Performing Process Mining by analyzing event logs generated by various systems is a very computation and I/O intensive task. Distributed computing and Big Data processing frameworks make it possible to distribute all kinds of computation tasks to multiple computers instead of performing the whole task in a single computer. This paper assesses whether contemporary structured query language (SQL) supporting Big Data processing frameworks are mature enough to be efficiently used to distribute computation of two central Process Mining tasks to two dissimilar clusters of computers providing BPM as a service in the cloud. Tests are performed by using a novel automatic testing framework detailed in this paper and its supporting materials. As a result, an assessment is made on how well selected Big Data processing frameworks manage to process and to parallelize the analysis work required by Process Mining tasks.

关键词： Big data Data mining algorithm design and analysis Computers Performance evaluation Software

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Multi-scale community detection in complex networks

Multi-scale community detection in complex networks

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International Conference on Research Advances in Integrated Navigation Systems (RAINS)

作者： P. Ambika M.R. Binu Rajan SCT College of Engineering Trivandrum India Sree Chitra Thirunal College of Engineering Thiruvananthapuram Kerala India

ISBN: (纸本)9781509011124

Community detection is an important problem in studying the structure and dynamics of networks. However it is difficult to determine community as networks have a hierarchical/multi-scale structure with different organizational scales or may be inhomogeneous with different nested modules with many links within same module and few between different modules. This paper reviews different methods to identify major multi scale clusters and overlapping community structures while reducing computational complexity.

关键词： Clustering algorithms Partitioning algorithms Image edge detection Optimization algorithm design and analysis Detection algorithms Classification algorithms

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Adaptive ECG Wavelet analysis for R-peaks detection

Adaptive ECG Wavelet analysis for R-peaks detection

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International Conference on Electrical and Information Technologies (ICEIT)

作者： Mounaim Aqil Atman Jbari Abdennasser Bourouhou Electrical Engineering Research Laboratory High School of Technical Education (ENSET) Mohamed V University Rabat Morocco

ISBN: (纸本)9781467384704

The Electrocardiogram has been established as a powerful diagnostic tool in medicine which provides important information about the patient's heart condition. The feature extraction of ECG such as R-peak detection is highly useful in cardiology. Continuous Wavelet transform based methods present best performance as irregularity measures and makes them suitable for ECG data analysis. In this work we propose a new method for R-peak detection based on continuous wavelet coefficients. The core of our method is to select automatically the analysis scale corresponding to maximum energy of ECG signal instead to consider all scales. In order to validate this method we apply it to several from apnea's database records. This new algorithm can be used to extract other features of ECG signals.

关键词： Electrocardiography Continuous wavelet transforms Wavelet analysis algorithm design and analysis Time-frequency analysis

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The Efficient Use of Storage Resources in SAN for Storage Tiering and Caching

The Efficient Use of Storage Resources in SAN for Storage Ti...

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International Conference on Computational Intelligence and Networks (CINE)

作者： U. Abhijith Mohanta Taranisen Ashish Kumar Leena Muddi HP Enterprise Bangalore India Hewlett Packard Enterprise Bengaluru India

With the rapid growth of the Hyper Convergence (HC) the use of the Solid State Device (SSDs) as the intelligent Storage Tiering and caching has increased immensely. It plays an important role to provide high throughput and performance with maximum utilization of the SSD storage space considering the life cycle of SSDs and workload. Efficient Utilization of such resources is one of the main criterion of the virtualization technology. By considering the rise of the VDI workload the proposed implementation of a sequential write detection algorithm, we try to maximize the utilization of higher tier storage such as SSDs (Tier 0) by placing the data which fall under the category of sequential writes into the lower tier disks (Tier 1, 2 - n) such as SAS (serial attached SCSI) / SATA (serial advanced technology attachment).

关键词： algorithm design and analysis Radiation detectors Virtualization Detection algorithms Solids Arrays Clocks

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Minimised moving distance algorithm for geometric deployment of robot swarms

Minimised moving distance algorithm for geometric deployment...

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SICE Annual Conference

作者： Cornelis Francois van Eeden Geunho Lee Shingo Katsuno Hiroki Yonekura Faculty of Engineering Miyazaki-shi University of Miyazaki Miyazaki

ISBN: (纸本)9781509009374

This paper addresses the deployment problem for a swarm of autonomous mobile robots initially randomly distributed in 2 dimensional space. A new local interaction rule which decreases the total distance moved by all robots in the swarm during deployment is proposed. This work allows a swarm of robots to configure themselves into a two-dimensional triangular tessellation. Each robot agent has a limited viewing range. No explicit communication or leader robot is used. Each robot interacts selectively with two neighbouring robots so that three robots converge onto each vertex of an equilateral triangle with fixed and pre-determined side lengths. The robot model, local interaction and neighbour selection algorithms are described. Through extensive simulations, comparisons are drawn between the performance of the algorithm from previous works and the proposed algorithm. Both the number of required activation steps and the total distance moved are investigated, it is shown that the proposed algorithm minimises the total distance moved by the swarm during deployment with a required number of activation steps similar to that of previous works.

关键词： Robot kinematics Robot sensing systems algorithm design and analysis Computational modeling Mobile robots

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GPU-Accelerated Bulk Computation of the Eigenvalue Problem for Many Small Real Non-symmetric Matrices

GPU-Accelerated Bulk Computation of the Eigenvalue Problem f...

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International Symposium on Computing and Networking (CANDAR)

作者： Hiroki Tokura Takumi Honda Yasuaki Ito Koji Nakano Mitsuya Nishino Yushiro Hirota Masami Saeki Department of Information Engineering Hiroshima University Higashi-Hiroshima JAPAN Department of Mechanical System Engineering Hiroshima University Higashi-Hiroshima JAPAN

ISBN: (纸本)9781509026562

The main contribution of this paper is to present a very efficient GPU implementation of bulk computation of eigenvalues for a large number of small non-symmetric real matrices. This work is motivated by the necessity of such bulk computation in design of control systems, which requires to compute the eigenvalues of hundreds of thousands non-symmetric real matrices of size up to 30 × 30. In our GPU implementation, we considered programming issues of the GPU architecture including warp divergence, coalesced access of the global memory, bank conflict of the shared memory, etc. In particular, we present three types of assignments of GPU threads to matrices and introduce three memory arrangements in the global memory. The experimental results on NVIDIA GeForce GTX TITAN X show that our GPU implementation for 500000 matrices of size 5 × 5 to 30 × 30 attains a speed-up factor of approximately 15 over the CPU implementation on Intel Core i7-4790.

关键词： Graphics processing units Eigenvalues and eigenfunctions Instruction sets Matrix decomposition algorithm design and analysis Symmetric matrices Rendering (computer graphics)

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A Scalable Object Detection Framework Based on Embedded Manycore Cluster

A Scalable Object Detection Framework Based on Embedded Many...

引用

International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Fang Gao Zhangqin Huang Shulong Wang Xinrong Ji Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China Beijing Advanced Innovation Center for Future Internet Technology

ISBN: (纸本)9781509051557

Performance of high resolution image process is one of the kernel problems that must be addressed to promote the development of embedded system. In this study, a scalable bi-level parallel object detection framework based on heterogeneous manycore cluster was established to improve object detection performance for embedded device. First, the fundamental principle of local binary pattern and cascade classifier combined object detection method was introduced as the basis of the research. Second, a set of key algorithm design to parallel access and process image for object detection based on Parallella manycore platform was proposed to improve the detection speed and the computational resource efficiency on single node. Third, a Message Passing Interface based distributed framework was established for cluster environment to further improve the performance. Finally, an experiment of face detection application was conducted to evaluate the accuracy and performance of this framework. The experimental results show that on one node, the proposed object detection system provides 7.8 times speedup than a serial algorithm on dual-core ARM which was integrated in Parallella with similar accuracy, and in cluster environment, the performance will be doubled. The results demonstrate the promising application of the proposed framework in the field of object detection performance improvement.

关键词： Object detection Classification algorithms Loading algorithm design and analysis Embedded systems Clustering algorithms Computer architecture

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Predictive data analysis driven multi-agent system approach for electrical micro grids management

Predictive data analysis driven multi-agent system approach ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jonas Queiroz Paulo Leitão Artur Dias Artificial Intelligence and Computer Science Laboratory Porto Portugal Polytechnic Institute of Braganca Braganca Portugal

ISBN: (纸本)9781509008742

Micro grid represents an emergent paradigm to address the challenges of recent smart electrical grid visions, where several small-scale and distributed electrical units cooperate to achieve higher levels of energy self-sustainability, by reducing the main grid dependence. Nevertheless, the realization of this paradigm requires advanced intelligent approaches that are able to effectively manage the micro grid infrastructure and its elements. Multi-agent systems provide a suitable framework to support the development of such systems, where autonomous agents endowed with predictive data analysis capabilities take advantage of the large amount of data produced to predict the renewable energy production and consumption. In this context, this paper presents a predictive data analysis driven multi-agent system for the management of micro grids renewable energy production. The proposed approach was applied to an experimental case study, considering different predictive algorithms and data sources for the short and midterm forecasting of the production of wind and photovoltaic energy-based units.

关键词： Production Data analysis Predictive models Analytical models Renewable energy sources Data models algorithm design and analysis

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