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检索条件"主题词=Optimization Algorithms"

共 4006 条记录，以下是3191-3200 订阅

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Time-resolved reconstruction of scene reflectance hidden by a diffuser

Time-resolved reconstruction of scene reflectance hidden by ...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Naik, Nikhil Barsi, Christopher Velten, Andreas Raskar, Ramesh MIT Media Lab Cambridge MA 02139 USA

We use time-of-flight information in an iterative optimization algorithm to recover reflectance properties of a three-dimensional scene hidden behind a diffuser. We demonstrate reconstruction of wide-field images with... 详细信息

ISBN: (纸本)9781557529732

关键词： optimization algorithms Diffusers FLIGHT INFORMATION Reconstructive Surgical Procedures Time resolved Reflectance Reconstructions TIME OF FLIGHT

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New Graph Structured Sparsity Model for Multi-Label Image Annotations

New Graph Structured Sparsity Model for Multi-Label Image An...

引用

IEEE International Conference on Computer Vision (ICCV)

作者： Cai, Xiao Nie, Feiping Cai, Weidong Huang, Heng Univ Texas Arlington Arlington TX 76019 USA Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

ISBN: (纸本)9781479928392

In multi-label image annotations, because each image is associated to multiple categories, the semantic terms (label classes) are not mutually exclusive. Previous research showed that such label correlations can largely boost the annotation accuracy. However, all existing methods only directly apply the label correlation matrix to enhance the label inference and assignment without further learning the structural information among classes. In this paper, we model the label correlations using the relational graph, and propose a novel graph structured sparse learning model to incorporate the topological constraints of relation graph in multi-label classifications. As a result, our new method will capture and utilize the hidden class structures in relational graph to improve the annotation results. In proposed objective, a large number of structured sparsity-inducing norms are utilized, thus the optimization becomes difficult. To solve this problem, we derive an efficient optimization algorithm with proved convergence. We perform extensive experiments on six multi-label image annotation benchmark data sets. In all empirical results, our new method shows better annotation results than the state-of-the-art approaches.

关键词： Graph Structured Sparsity Multi-Label Annotation Structured Sparsity-Inducing Norm ANNOTATIONS Relational graph Imagery (Psychotherapy) images optimization algorithms Image Line graph

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Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel

Displacement prediction of landslide based on PSOGSA-ELM wit...

引用

6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Lian, Cheng Zeng, Zhigang Yao, Wei Tang, Huiming Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan Peoples R China South Cent Univ Nationalities Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Fac Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781467363433

The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter gamma, the polynomial kernel parameter q and the mixing weight coefficient eta. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.

关键词： Seeds Landslips Kernel Elm Ecosystem models KERNEL FUNCTIONS Particle swarm optimization Job Ladders DISPLACEMENT displacement casualties Displacement (Defense Mechanism) Dislocated Workers optimization algorithms

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Aeolus: An Optimizer for Distributed Intra-Node-Parallel Streaming Systems

Aeolus: An Optimizer for Distributed Intra-Node-Parallel Str...

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29th IEEE International Conference on Data Engineering (ICDE)

作者： Sax, Matthias J. Castellanos, Malu Chen, Qiming Hsu, Meichun Humboldt Univ Databases & Informat Syst Grp Berlin Germany Hewlett Packard Labs Palo Alto CA USA

ISBN: (纸本)9781467349093;9781467349086

Aeolus is a prototype implementation of a topology optimizer on top of the distributed streaming system Storm. Aeolus extends Storm with a batching layer which can increase the topology's throughput by more than one order of magnitude. Furthermore, Aeolus implements an optimization algorithm that computes the optimal batch size and degree of parallelism for each node in the topology automatically. Even if Aeolus is built on top of Storm, the developed concepts are not limited to Storm and can be applied to any distributed intra-node-parallel streaming system. We propose to demo Aeolus using an interactive Web UI. One part of the Web UI is a topology builder allowing the user to interact with the system. Topologies can be created from scratch and their structure and/or parameters can be modified. Furthermore, the user is able to observe the impact of the changes on the optimization decisions and runtime behavior. Additionally, the Web UI gives a deep insight in the optimization process by visualizing it. The user can interactively step through the optimization process while the UI shows the optimizer's state, computations, and decisions. The Web UI is also able to monitor the execution of a non-optimized and optimized topology simultaneously showing the advantage of using Aeolus.

关键词： optimization Procedure storms Streaming FIRST ORDER topology Web search engines webs World Wide Web congenital web optimization algorithms Distribute parallelism Consumers

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A Branch-and-Bound algorithm using multiple GPU-based LP solvers

A Branch-and-Bound algorithm using multiple GPU-based LP sol...

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20th International Conference on High Performance Computing (HiPC)

作者： Meyer, Xavier Chopard, Bastien Albuquerque, Paul Univ Geneva Dept Comp Sci Geneva Switzerland Univ Appl Sci Western Switzerland Inst Informat & Telecommun Geneva Switzerland

ISBN: (纸本)9781479907298

The Branch-and-Bound (B& B) method is a wellknown optimization algorithm for solving integer linear programming (ILP) models in the field of operations research. It is part of software often employed by businesses for finding solutions to problems such as airline scheduling problems. It operates according to a divide-and-conquer principle by building a treelike structure with nodes that represent linear programming (LP) problems. A LP solver commonly used to process the nodes is the simplex method. Nowadays its sequential implementation can be found in almost all commercial ILP solvers. In this paper, we present a hybrid CPU-GPU implementation of the B&B algorithm. The B&B tree is managed by the CPU, while the revised simplex method is mainly a GPU implementation, relying on the CUDA technology of NVIDIA. The CPU manages concurrently multiple instances of the LP solver. The principal difference with a sequential implementation of the B&B algorithm pertains to the LP solver, provided that the B&B tree is managed with the same strategy. We thus compared our GPU-based implementation of the revised simplex to a wellknown open-source sequential solver, named CLP, of the COINOR project. For given problem densities, we measured a size threshhold beyond which our GPU implementation outperformed its sequential counterpart.

关键词： Solvers Simplex method optimization algorithms central processing units Branch and bound algorithms inductive logic programming Sequential GRAPPER PICK UP Graphics Processing Unit Ganglia

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Spatial Frequency Reuse in a Novel Generation of PMR Networks

Spatial Frequency Reuse in a Novel Generation of PMR Network...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Lamiable, Alexis Tomasik, Joanna SUPELEC Syst Sci E3S Dept Comp Sci F-91192 Gif Sur Yvette France

ISBN: (纸本)9781467359399;9781467359382

Private Mobile Radio (PMR) networks are cellular infrastructures dedicated to be used by professionals, such as public safety, military, industry and transportation organizations. In those networks, resources are scarce, and there are strong Quality of Service (QoS) requirements. The emergence of new services which need more bandwidth has made the world PMR leader focus on the LTE-Advanced protocol. In order to ensure the QoS despite resource shortage, we propose an algorithm of Resource Blocks (RBs) allocation with spatial frequency reuse whose scheme takes into account users' (UEs') interference possibility and probability. We start by defining the underlying problem, which we call Weighted Fractional Coloring Problem (WFCP), in terms of graph theory. Next, we prove its NP-hardness. As obtaining an exact solution of such a problem in reasonable time is unrealistic, we propose a heuristic algorithm. In order to evaluate the performance of our algorithm we use a rigorous validation procedure. We compare its performance with that of a random one which we propose as a reference and the exact one which can be run on very small networks. Thanks to the results obtained we believe that the proposed algorithm can establish a solid starting point to conceive its distributed versions for novel PMR protocols.

关键词： frequency reuse resource allocation optimization algorithms interference control

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Downloading and Analysing Images from the Internet in Order to Detect Special Objects

Downloading and Analysing Images from the Internet in Order ...

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6th International Conference on Multimedia Communications, Services and Security (MCSS)

作者： Leszczuk, Mikolaj Piwowarczyk, Tomasz Grega, Michal AGH Univ Sci & Technol PL-30059 Krakow Poland

ISBN: (纸本)9783642385599

Along with all the positives brought by the Internet, the global network is also used for criminal purposes. The goal of the presented work was to create and optimize applications working in parallel with the search and illegal content analysis system (SWAT) created previously at the authors' university. The role of the SWAT system is to penetrate the Internet to search for images and provide links to the presented application. The presented application is able to detect various special objects, such as faces, symbols or child pornography. The use of bees algorithm-based optimization made it possible to increase the effectiveness of image analysis. With appropriate assumptions, the efficiency was increased by a factor of three times compared to the application without optimization. The efficiency of the optimization algorithm depends largely on the nature of the input data, the URL addresses.

关键词： OpenCV optimization algorithms C# Python Internet safety Web Services

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Energy-Optimal Collaborative GPS Localization with Short Range Communication

Energy-Optimal Collaborative GPS Localization with Short Ran...

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11th International Symposium and Workshops on Modeling and optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt)

作者： Kwak, Jeongho Kim, Jihwan Chong, Song Korea Adv Inst Sci & Technol Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781612848242

The key issue of the localization study is that how we can minimize the energy consumption of devices with guaranteeing high degree of accuracy. In this paper, we show that the collaboration among proxy devices with short range communication is helpful to energy-efficiently localize their locations in time-average sense by analyzing the device proximity including real GPS trace of students in KAIST and NCSU campuses. Next, we deliberate what is the best method for selfish mobile users to collaborate for the energy-efficient localization, and formulate an optimization problem which considers the energy efficiency and/or user fairness. However, optimizing this problem is tricky since it requires a global knowledge of sets of proxy devices and also solving a NP-hard problem to select devices which directly measure locations. This paper makes a contribution towards presenting a practical and fully distributed location sharing protocol based on competition for turning off GPS, and an optimal algorithm which controls mean waiting time used for the competition. Through the extensive simulations under several sample topologies and real mobility trace in KAIST campus, we obtain the following interesting observations: (i) (in sample topologies) our scheme achieves a near-optimal performance of proposed problem in terms of energy efficiency and fairness (up to 27.2% power saving with 35.8% higher fairness than existing heuristic algorithms), (ii) (in real mobility trace) our scheme well adapts at even unpredictably changing mobility environment (65.5% power saving than no collaboration, 27.4% or more power saving with 25% higher fairness than the existing algorithms).

关键词： short-range communication Energy efficiency Global Positioning Systems optimization algorithms Nondeterministic polynomial-time hard energy consumption Waiting time

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A barrier function based continuous-time algorithm for linear model predictive control

A barrier function based continuous-time algorithm for linea...

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European Control Conference (ECC)

作者： Feller, Christian Ebenbauer, Christian Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany

ISBN: (纸本)9783033039629

In this paper, we present a novel linear model predictive control (MPC) scheme that relies on a continuous-time, barrier function based algorithm which asymptotically tracks the solution of a time-varying open-loop optimal control problem. In particular, the control input is obtained as the sampled output of a continuous-time dynamical system and no iterative optimization algorithm is needed in the on-line implementation. In addition, we present a new approach towards stabilizing MPC based on gradient recentered logarithmic barrier functions that allows enlargement of the employed terminal set.

关键词： barrier function nonlinear model predictive control Predictive control optimization algorithms linear models control input Maximum Allowable Concentration Hypertrophy disorientation to time Optimal control based asymptotic

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On Mitigating Wind Energy Variability with Storage

On Mitigating Wind Energy Variability with Storage

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5th International Conference on Communication Systems and Networks (COMSNETS)

作者： Arya, Vijay Dutta, Partha Kalyanaraman, Shivkumar IBM-Research India

ISBN: (纸本)9781467353304;9781467353298

Reducing fluctuations and mitigating the variability of wind energy is essential for its integration in grids, electricity markets, microgrids, and distributed generation settings. In this work, we present a storage-based wind power smoothing system that uses novel optimization algorithms to reduce the variability of wind energy. The system considers forecasted and actual energy generated, battery size, and energy prices and determines export rates that have low variability and maximize either the energy exported or revenue earned. Our optimization algorithms are novel as they model an equivalent relaxed buffer system that uses only linear constraints and allows the computation of optimal smoothing solutions in an efficient manner. This enables the system to be used in an online manner in real time as well as in planning and operations. The smoothing system and the mathematical models and programs used in optimization are presented along with preliminary simulation results that demonstrate the need and effectiveness of the system with the help of real wind energy data. Finally, we compare wind smoothing to video smoothing and point out the important similarities and differences.

关键词： Wind energy optimization algorithms smoothing buffer system REVENUE

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