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检索条件"主题词=Optimization algorithms"

共 4003 条记录，以下是3331-3340 订阅

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OPTIMUM RESTORATION OF ANGULAR RADIO BRIGHTNESS IN SCANNING RADIOMETER

OPTIMUM RESTORATION OF ANGULAR RADIO BRIGHTNESS IN SCANNING ...

引用

14th International Conference on Mathematical Methods in Electromagnetic Theory (MMET)

作者： Pavlikov, V. V. Natl Aerosp Univ Dept Design Radioelect Syst Aircraft UA-61070 Kharkov Ukraine

the problem of radio brightness image restoration as a function of angular coordinates in the radiometric systems with a scanning antenna boresight is solved. The optimal and quasi optimal algorithms of signal process... 详细信息

ISBN: (纸本)9781467344784

关键词： Spherical coordinates scanning radiometer brightness Radio signal processing Scanning antennas Optimal Angle optimization algorithms aiming line restoration

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Revisiting Minimum-Length Scheduling in Wireless Networks: An Algorithmic Framework

Revisiting Minimum-Length Scheduling in Wireless Networks: A...

引用

International Symposium on Information Theory and its Applications (ISITA)

作者： He, Qing Angelakis, Vangelis Ephremides, Anthony Yuan, Di Linkoping Univ Dept Sci & Technol S-58183 Linkoping Sweden

ISBN: (纸本)9784885522673

We consider the problem of constructing the minimum length schedule required to empty a wireless network with queues of given size. In a recent work we have provided new fundamental insights towards its structure and complexity. Motivated by the problem computational complexity, we demonstrate here how a one-size-fits-all optimal algorithm cannot be expected and introduce a framework that decomposes the problem in two core sub-problems: Selecting which subset of wireless links to activate and for how long. This modular approach enables the construction of algorithms that can yield solutions ranging from simple and intuitive to exact optimal. We provide a comprehensive set of design strategies and results to elucidate how different combinations within the framework modules can be used to approach optimality.

关键词： Wireless networks Frameworks Queue Intuitive Set design optimality wireless optimization algorithms modular approach

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Efficient Versioning for Scientific Array Databases

Efficient Versioning for Scientific Array Databases

引用

28th IEEE International Conference on Data Engineering (ICDE)

作者： Seering, Adam Cudre-Mauroux, Philippe Madden, Samuel Stonebraker, Michael MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781467300421

In this paper, we describe a versioned database storage manager we are developing for the SciDB scientific database. The system is designed to efficiently store and retrieve array-oriented data, exposing a "no-overwrite" storage model in which each update creates a new "version" of an array. This makes it possible to perform comparisons of versions produced at different times or by different algorithms, and to create complex chains and trees of versions. We present algorithms to efficiently encode these versions, minimizing storage space or IO cost while still providing efficient access to the data. Additionally, we present an optimal algorithm that, given a long sequence of versions, determines which versions to encode in terms of each other (using delta compression) to minimize total storage space. We compare the performance of these algorithms on real world data sets from the National Oceanic and Atmospheric Administration (NOAA), OpenStreetMaps, and several other sources. We show that our algorithms provide better performance than existing version control systems not optimized for array data, both in terms of storage size and access time, and that our delta-compression algorithms are able to substantially reduce the total storage space when versions exist with a high degree of similarity.

关键词： storage spaces STORAGE(BY SIZE) algorithms Storage access times optimization algorithms Database arrays Scientific database

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FHSS SIGNAL RECONSTRUCTION BASED ON THE COMPRESSIVE SAMPLING PRINCIPLE

FHSS SIGNAL RECONSTRUCTION BASED ON THE COMPRESSIVE SAMPLING...

引用

20th Telecommunications Forum

作者： Draganic, Andjela Orovic, Irena Stankovic, Srdjan Amin, Moeness G. Univ Crne Gore Elektrotehn Fak Podgorica 81000 Montenegro

ISBN: (纸本)9781467329842;9781467329835

The method for the reconstruction of Frequency Hopping Spread Spectrum (FHSS) signals is described in the paper. FHSS is a modulation technique employed in spread spectrum communications. Reconstruction procedure is based on the compressive sampling principle, which allows signal reconstruction using a significantly smaller number of samples than required by the Nyquist-Shannon sampling theorem. The method is applied on signals having a sparse representation in transform domain, which often appears in real applications. Reconstruction is based on the optimization algorithms using l(1)-norm minimization in the domain in which the signal is sparse. In addition, the joint time-frequency signal analysis is combined with a compressive sampling method, in order to compare parameters of the original and the reconstructed signal.

关键词： Bluetooth FHSS kompresivno odabiranje l1-norma optimizacioni algoritmi vremensko-frekvencijska analiza Bluetooth Signal reconstruction frequency hopping spread spectrum signals optimization algorithms Reconstructive Surgical Procedures Signal analysis sparse representation Spread spectrum communication

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A New Intelligent Group Elevator Control Approach

A New Intelligent Group Elevator Control Approach

引用

15th International Conference on Mechatronics (Mechatronika)

作者： Baygin, Mehmet Karakose, Mehmet Ardahan Univ Dept Comp Engn Ardahan Turkey

ISBN: (纸本)9788001049877

Given the structure of urbanization, the use of elevator has become a necessity. The increase of population density in buildings in parallel to the structure of urbanization has led to the emergence of a group controlled elevator systems. In general, the group elevator system is intended to reduce the average waiting time of passengers and provide energy efficiency. For these purposes, in this study, optimization for group elevator systems and a group of estimation based control algorithm has been realized. In a recent study, the artificial immune system, genetic algorithm, and DNA has been used as optimization algorithms, again an estimation algorithm has been proposed for the group controlled elevator systems. The results obtained from system optimization and estimation algorithm are evaluated with fuzzy system and the most appropriate technique is determined.

关键词： component estimation fuzzy system group controlled elevator systems optimization algorithms

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SCHEDULING PROBLEMS WITH PRECEDENCE CONSTRAINTS: MODELS AND algorithms

SCHEDULING PROBLEMS WITH PRECEDENCE CONSTRAINTS: MODELS AND ...

引用

European Simulation and Modelling Conference (ESM 2012)

作者： Montemanni, Roberto Univ Appl Sci Southern Switzerland SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Manno Switzerland

ISBN: (纸本)9789077381731

The sequential ordering problem is a basic scheduling problem with precedence constraints. It can be used to model many real world applications arising in different fields in terms of combinatorial optimization. In this paper we review two approaches to the problem recently appeared in the literature. Both the methods are based on a mixed integer linear programming, which is in turn introduced and discussed. Experimental results are presented to analyze the performance of the algorithms.

关键词： Sequential ordering problem scheduling problems precedence constraints mixed integer linear programming optimization algorithms

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Orchestrating Constrained Programming and Local Search to Solve a Large Scale Energy Management Problem

Orchestrating Constrained Programming and Local Search to So...

引用

Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Buljubasic, Mirsad Gavranovic, Haris Univ Sarajevo Fac Nat Sci Sarajevo 71000 Bosnia & Herceg Int Univ Sarajevo & Strategeme Lab Bosnia & Herzegovina Sarajevo Bosnia & Herceg

ISBN: (纸本)9788360810484

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and produce competitive results. The local search for production assignment is a simple yet optimal solution for the relaxed initial problem. We also propose an efficient way to scale the method for huge instances. A large part of the presented work is done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and the Electricite de France. The numerical results obtained for the official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

关键词： Energy management constraint satisfaction problems optimization algorithms scaling method Scale formation Programming

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On Objective Feature Selection for Affective Sounds Discrimination

On Objective Feature Selection for Affective Sounds Discrimi...

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54th ELMAR International Symposium

作者： Chmulik, Michal Jarina, Roman Kuba, Michal Univ Zilina Audiolab Dept Telecommun & Multimedia Univ 2 Zilina 01026 Slovakia

ISBN: (纸本)9789537044138

We present an objective acoustic feature selection for automatic affective sounds detection based on stochastic evolutionary optimization algorithms. Particle Swarm optimization (PSO) as well as Genetic algorithms (GA) are exploit to select the most appropriate audio features from a large set of available features. We performed experiments on a dataset containing about two hours of affective sounds - cry, laughter and applause, and supplemented with several hours of recordings of other sounds (speech, music and various types of noise). Applying the feature selection methods, the classification performance is increased about 4-9 % with final accuracy 92-98 % while feature space dimension is reduced about 50-90 %.

关键词： Genetic algorithms optimization algorithms Particle Swarm optimization Affective Sound discrimination

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In-Network Local Distributed Estimation for Power-constrained Wireless Sensor Networks

In-Network Local Distributed Estimation for Power-constraine...

引用

IEEE 75th Vehicular Technology Conference (VTC Spring)

作者： Shah, Santosh Beferull-Lozano, Baltasar Univ Valencia Grp Informat & Commun Syst Inst Robot & Tecnol Informac & Comunicac Paterna 46980 Valencia Spain

ISBN: (纸本)9781467309905;9781467309882

In this paper, we consider the problem of power-efficient distributed estimation of a localized event in the large-scale Wireless Sensor Networks (WSNs). In order to increase the power efficiency in these networks, we develop a joint optimization problem that involves both selecting a subset of active sensors and the routing structure so that the quality of estimation at a given querying node is the best possible subject to a total imposed communication cost. We first formulate our problem as an optimization problem and show that it is NP-Hard. Then, we propose a local distributed optimization algorithm that is based on an Estimate-and-Forward (EF) strategy, which allows to perform sequentially this joint optimization in an efficient way. We also provide a lower bound for our optimization problem and show that our local distributed optimization algorithm provides a performance that is close to this bound. Although there is no guarantee that the gap between this lower bound and the optimal solution of the main problem is always small, our numerical experiments support that this gap is actually very small in many cases. An important result from our work is that because of the interplay between the communication cost over the links and the gains in estimation accuracy obtained by choosing certain sensors, the traditional Shortest Path Tree (SPT) routing structure, widely used in practice, is no longer optimal, that is, our routing structures provide a better trade-off between the overall power efficiency and the final estimation accuracy obtained at the querying node. Our experimental results show that our algorithms yield a significant energy saving.

关键词： dynamic efficiency optimization algorithms Distribute Network routing

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A Learning Adaptive Bollinger Band System

A Learning Adaptive Bollinger Band System

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Butler, Matthew Kazakov, Dimitar Univ York Dept Comp Sci Artificial Intelligence Grp York YO10 5DD N Yorkshire England

ISBN: (纸本)9781467318037

This paper introduces a novel forecasting algorithm that is a blend of micro and macro modelling perspectives when using Artificial Intelligence (AI) techniques. The micro component concerns the fine-tuning of technical indicators with population based optimization algorithms. This entails learning a set of parameters that optimize some economically desirable fitness function as to create a dynamic signal processor which adapts to changing market environments. The macro component concerns combining the heterogeneous set of signals produced from a population of optimized technical indicators. The combined signal is derived from a Learning Classifier System (LCS) framework that combines population based optimization and reinforcement learning (RL). This research is motivated by two factors, that of non-stationarity and cyclical profitability (as implied by the adaptive market hypothesis [10]). These two properties are not necessarily in contradiction but they do highlight the need for adaptation and creation of new models, while synchronously being able to consult others which were previously effective. The results demonstrate that the proposed system is effective at combining the signals into a coherent profitable trading system but that the performance of the system is bounded by the quality of the solutions in the population.

关键词： signal processor signals optimization algorithms micro Learning Artificial Intelligence Self tuning System performance given signal

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