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检索条件"主题词=Algorithm design and analysis"

共 82948 条记录，以下是521-530 订阅

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PrivateGraph: A Cloud-Centric System for Spectral analysis of Large Encrypted Graphs 37

PrivateGraph: A Cloud-Centric System for Spectral Analysis o...

引用

37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Sharma, Sagar Chen, Keke Wright State Univ Dept Comp Sci & Engn Data Intens Anal & Comp DIAC Lab Knoesis Ctr Dayton OH 45435 USA

ISBN: (纸本)9781538617915

Graph datasets have invaluable use in business applications and scientific research. Because of the growing size and dynamically changing nature of graphs, graph data owners may want to use public cloud infrastructures to store, process, and perform graph analytics. However, when outsourcing data and computation, data owners are at burden to develop methods to preserve data privacy and data ownership from curious cloud providers. This demonstration exhibits a prototype system for privacy-preserving spectral analysis framework for large graphs in public clouds (PrivateGraph) that allows data owners to collect graph data from data contributors, and store and conduct secure graph spectral analysis in the cloud with preserved privacy and ownership. This demo system lets its audience interactively learn the major cloud-client interaction protocols: the privacy preserving data submission, the secure Lanczos and Nystriim approximate eigen-decomposition algorithms that work over encrypted data, and the outcome of an important application of spectral analysis - spectral clustering. In the process of demonstration the audience will understand the intrinsic relationship amongst costs, result quality, privacy, and scalability of the framework.

关键词： Cloud computing Data privacy Cryptography Clustering algorithms Spectral analysis Distributed databases algorithm design and analysis

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Salient Object Detection Using Array Images 9

Salient Object Detection Using Array Images

引用

9th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Li, Tingtian Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538615423

Most existing saliency detection methods utilize low-level features to detect salient objects. In this paper, we first verify that the foreground objects in the scene can be an effective cue for saliency detection. We then propose a novel saliency detection algorithm which combines low level features with high level object detection results to enhance the performance. For extracting the foreground objects in a scene, we first make use of a camera array to obtain a set of images of the scene from different viewing angles. Based on the array images, we identify the feature points of the objects so as to generate the foreground and background feature point cues. Together with a new K-Nearest Neighbor model, a cost function is developed to allow a reliable and automatic segmentation of the foreground objects. The outliers in the segmentation are further removed by a low-rank decomposition method. Finally, the detected objects are fused with the low-level object features to generate the saliency map. Experimental results show that the proposed algorithm consistently gives a better performance compared to the traditional methods.

关键词： Feature extraction Saliency detection Cost function Cameras Image segmentation algorithm design and analysis Shape

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A Novel Online Incentive Mechanism under Budget Constraint for Crowdsourcing Systems 9

A Novel Online Incentive Mechanism under Budget Constraint f...

引用

9th International Conference on Wireless Communication and Signal Processing (WCSP)

作者： Zhang, Jiawei Wang, Xiong Gan, Xiaoying Tian, Xiaohua Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781538620625

Crowdsourcing is a new paradigm which leverages human computation to finish specific tasks. Efficient incentive mechanisms are proposed to motivate crowd workers' participation, in which however, researchers frequently concentrate on offline forms. Considering worker's successive arrival and dynamic departure, we are motivated to design an online incentive mechanism. Particularly, the workers come one after another to the crowdsourcing platform and meanwhile the platform makes irrevocable decision to select the qualified workers to the winner set and give them monetary reward. We formulate the incentive mechanism as a knapsack secretary auction, where we take advantages of the principles of solving knapsack problem and secretary problem to tackle the budget constraint and online scenario in our case. Some prevalent-used economic properties, such as individual rationality, truthfulness and fairness are theoretically guaranteed. Extensive evaluations are conducted to demonstrate the effectiveness and correctness of our mechanism with respect to the aforementioned properties.

关键词： Crowdsourcing Sensors algorithm design and analysis Smart phones Economics Mobile communication

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The improved Item-based clustering collaborative filtering algorithm based on Hadoop 2

The improved Item-based clustering collaborative filtering a...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Wei Zhao Hongtao Zhang Software and Applied Science and Technology Institute of Zhengzhou University Distance learning School of Zhengzhou University

Firstly, according to the Hadoop platform the novel data-analysis architecture is designed, then the paper builds the Item-based clustering collaborative filtering algorithm based on Hadoop. And it ta

ISBN: (纸本)9781467389808

关键词： Collaboration Filtering algorithms Clustering algorithms algorithm design and analysis Application software Matrix converters Distributed databases

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Control of Linear Time-Invariant Plants with Compensation of Measurement Noises and Disturbances 56

Control of Linear Time-Invariant Plants with Compensation of...

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56th Annual IEEE Conference on Decision and Control (CDC)

作者： Furtat, Igor B. Russian Acad Sci Inst Problems Mech Engn 61 Bolshoy Ave VO St Petersburg 199178 Russia ITMO Univ 49 Kronverkskiy Ave St Petersburg 197101 Russia

ISBN: (纸本)9781509028733

In the paper the algorithm with compensation of parametric uncertainties, external disturbances and measurement noises for linear time-invariant plants is proposed. It is assumed, that the dimension of the noise can be equal to the state vector dimension and the disturbance can be presented in any equation of the plant model. Analytical condition for algorithm feasibility is proposed. The simulations show the efficiency of the proposed algorithm.

关键词： Mathematical model algorithm design and analysis Noise measurement Transmission line measurements Symmetric matrices Measurement uncertainty Electrical resistance measurement

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VLSI Implementation of LS-SVM Training and Classification using Entropy based Subset-Selection 50

VLSI Implementation of LS-SVM Training and Classification us...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Bytyn, Andreas Springer, Jannik Leupers, Rainer Ascheid, Gerd Rhein Westfal TH Aachen Inst Commun Technol & Embedded Syst Aachen Germany

ISBN: (纸本)9781467368537

Machine Learning techniques such as Support Vector Machines (SVM) have found applications in many fields, e.g. in Wireless Sensor Networks (WSN) and sensor data processing in general. Especially in the case of WSN energy is very limited as agents solely operate based on battery power after they have been deployed, therefore energy efficiency is of great importance. Furthermore, agents are supposed to adapt to their environment by being capable of re-training themselves based on feedback they get from their surroundings, which increases the computational demands on the digital hardware involved. To meet these demands, dedicated hardware in form of a very-large-scale integrated (VLSI) circuit is a reasonable approach and is investigated here. In this paper a specific variant of the SVM - the Least-Squares SVM - is implemented as VLSI circuit. Additionally during the training phase a subset-selection technique based on the quadratic Renyi entropy is implemented in order to reduce the computational and hardware demands. The resulting design consumes 21.35mW and requires an area of 81.2 kGE without memories.

关键词： Kernel Training Hardware Random access memory Entropy Computer architecture algorithm design and analysis

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An Optimal Method for Load Restoration in AC-DC System

An Optimal Method for Load Restoration in AC-DC System

引用

China International Electrical and Energy Conference (CIEEC)

作者： Qiu, Chenguang Cheng, Jinmin Chen, Qiwei Jiang, Ping Jiangsu Elect Power Co Ltd Dispatching & Control Ctr Nanjing 210024 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509062768

A load restoration optimization method in AC-DC system with the consideration of VSC-HVDC is proposed in this paper. Firstly, steady state power flow model of VSC is established. Then, this paper set the maximum capacity- and importance of the restoration load as the objective functions, steady state power flow with frequency characteristic as constraint condition. Based on that, the 0-1 programming model with single objective and multi-constraints is established. Furthermore, the model is simplified as one-dimensional knapsack problem with consideration of frequency stability only by adopting the linear programming relaxation method. The results are used as the initial values of the genetic algorithm and the optimal load restoration schemes are finally obtained. At the end of this paper, New England 10-Machine 39-Bus System shows that the proposed method is suitable for complex AC-DC system, which makes up the disadvantage of falling into local optimal solution and slow convergence speed.

关键词： algorithm design and analysis optimization power system restoration power grids VSC-HVDC

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Robust identification for singularly perturbed nonlinear systems using multi-time-scale dynamic neural network 56

Robust identification for singularly perturbed nonlinear sys...

引用

56th Annual IEEE Conference on Decision and Control (CDC)

作者： Zheng, Dong-Dong Xie, Wen-Fang Luo, Chaomin Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada Univ Detroit Mercy Dept Elect & Comp Engn Detroit MI 48221 USA

ISBN: (纸本)9781509028733

In this paper, a novel identification scheme is proposed for a class of singularly perturbed nonlinear systems. In order to identify the unknown singularly perturbed nonlinear system, a set of filtered variables are firstly defined and incorporated into the multi-time-scale dynamic neural network (DNN). Subsequently, the new weight's updating laws are proposed to train the neural network, such that the neural network weights will converge to their nominal values. By incorporating the filtered variables into the dynamic neural network, the derivatives of the identification errors are no longer needed in the weight's updating laws. As a result, the identification scheme proposed here is more robust to the measurement noises. The stability analysis of the identification algorithm using Lyapunov method is presented. Numerical simulations are performed to demonstrate the validity of the proposed identification algorithm.

关键词： Artificial neural networks algorithm design and analysis Nonlinear systems Training Heuristic algorithms Robustness

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A Research on Zero-cross Point Forecast Method of Short Circuit Current 2

A Research on Zero-cross Point Forecast Method of Short Circ...

引用

2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Jingjin Cao Yue Zhao Na Zhang Wei Wang Dawei Wang Equipment status evaluation center State Grid Shanxi EPRI State Grid Taigu Power Company State Grid Jinzhong Power

The paper studies the rapid identification technology of short circuit fault and the predictive technology of the time at which the fault current is zero,introduces the current signal can be used for

ISBN: (纸本)9781467389808

The paper studies the rapid identification technology of short circuit fault and the predictive technology of the time at which the fault current is zero,introduces the current signal can be used for

关键词： Short-circuit currents Circuit faults Prediction algorithms Harmonic analysis algorithm design and analysis Power systems Integrated circuit modeling

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Closed Walk Sampler: An Efficient Method for Estimating the Spectral Radius of Large Graphs 5

Closed Walk Sampler: An Efficient Method for Estimating the ...

引用

IEEE International Conference on Big Data (IEEE Big Data)

作者： Han, Guyue Sethu, Harish Drexel Univ Dept ECE Philadelphia PA 19104 USA

ISBN: (纸本)9781538627150

The spectral radius of a graph, i.e., the largest eigen-value of its adjacency matrix, is of high interest in graph analytics for Big Data due to its relevance to many important properties of the graph including network resilience, community detection and the speed of viral propagation. Accurate computation of the largest eigenvalue of extremely large graphs is infeasible due to the prohibitive computational and storage costs, and also because full access to many social network graphs is often restricted to most researchers. In this paper, we present a new and efficient algorithm called Closed Walk Sampler (cWalker) which solves both of the above-mentioned problems. Unlike previous methods which try to extract a subgraph with the most influential nodes, the cWalker samples only a small portion of the large graph via a simple random walk, and arrives at an estimate of the largest eigenvalue by estimating the number of closed walks of a certain length. Our experimental results using real graphs show that the cWalker is substantially faster while also achieving significantly better accuracy on most graphs than the current state-of-the-art algorithms.

关键词： Eigenvalues and eigenfunctions algorithm design and analysis Social network services Iterative methods Symmetric matrices Big Data Graph theory

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