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检索条件"主题词=Optimization Algorithms"

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MAXIMUM CORRENTROPY CRITERION FOR DISCRIMINATIVE DICTIONARY LEARNING

MAXIMUM CORRENTROPY CRITERION FOR DISCRIMINATIVE DICTIONARY ...

引用

IEEE International Conference on Image Processing

作者： Pengyi HAO Sei-ichiro KAMATA Waseda University Japan Graduate School of Information Production and Systems

ISBN: (纸本)9781479923427

In this paper, a novel discriminative dictionary learning with pairwise constraints by maximum correntropy criterion is proposed for pair matching problem. Comparing with the conventional dictionary learning approaches, the proposed method has several advantages: (i) It can deal with the outliers and noises problem more efficiently during the reconstruction step. (ii) It can be effectively solved by half-quadratic optimization algorithm, and in each iteration step, the complex optimization problem can be reduced to a general problem that can be efficiently solved by feature-sign search optimization. (iii) The proposed method is capable of analyzing non-Gaussian noise to reduce the influence of large outliers substantially, resulting in a robust and discriminative dictionary. We test the performance of the proposed method on two applications: face verification on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark and face-track identification on a dataset with more than 7,000 face-tracks. Compared with the recent state-of-the-art approaches, the outstanding performance of the proposed method validates its robustness and discriminability.

关键词： Dictionary learning Maximum correntropy criterion Face verification face verification Dictionaries as Topic outliers Learning optimization algorithms iterative methods

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A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Youssef Oualil Mathew Magimai.-Doss Friedrich Faubel Dietrich Klakow Spoken Language Systems Saarland University Idiap Research Institute

ISBN: (纸本)9781479903573

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the time difference of arrival (TDOA) and subsequently approximated as a Gaussian mixture. The distribution parameters are estimated with a weighted expectation maximization algorithm. Then, the joint distribution of the TDOA Gaussian mixtures is mapped to a multimodal distribution in the location space, where each mode represents a potential source location. The approach taken here performs the localization by 1) reducing the search space to some regions that are likely to contain a source and then 2) extracting the actual speaker locations with a numerical optimization algorithm. The effectiveness of the proposed approach is shown using the AV16.3 corpus.

关键词： Microphone arrays Localization Multiple speakers Gaussian mixture Steered response power microphone arrays Loudspeakers Time difference of arrival expectation-maximisation algorithm Probability distribution distributed parameter Position measurement TimeDif optimization algorithms

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FUSION OF IMAGE SEGMENTATION algorithms USING CONSENSUS CLUSTERING

FUSION OF IMAGE SEGMENTATION ALGORITHMS USING CONSENSUS CLUS...

引用

IEEE International Conference on Image Processing

作者： Mete Ozay Fatos T. Yarman Vural Sanjeev R. Kulkarni H. Vincent Poor Department of Electrical Engineering Princeton University NJ 08544 USA Department of Computer Engineering Middle East Technical University Ankara Turkey

ISBN: (纸本)9781479923427

A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image. The candidate segmentation sets are processed to achieve a consensus segmentation using a stochastic optimization algorithm based on the Filtered Stochastic BOEM (Best One Element Move) method. For this purpose, Filtered Stochastic BOEM is reformulated as a segmentation fusion problem by designing a new distance learning approach. The proposed algorithm also embeds the computation of the optimum number of clusters into the segmentation fusion problem.

关键词： Segmentation clustering fusion consensus stochastic optimization segmentation Segmentation Fusion reactions FUSION fusion (melting) optimum number Fusion, biological Cluster Analysis optimization algorithms images Distance learning

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Trajectory-based proofs for sampled-data extremum seeking control

Trajectory-based proofs for sampled-data extremum seeking co...

引用

American Control Conference

作者： Sei Zhen Khong Dragan Nesic Ying Tan Chris Manzie Department of Electrical and Electronic Engineering The University of Melbourne VIC 3010 Australia Department of Mechanical Engineering The University of Melbourne VIC 3010 Australia

ISBN: (纸本)9781479901777

Extremum seeking of nonlinear systems based on a sampled-data control law is revisited. It is established that under some generic assumptions, semi-global practical asymptotically stable convergence to an extremum can be achieved. To this end, trajectory-based arguments are employed, by contrast with Lyapunov-function-type approaches in the existing literature. The proof is simpler and more straightforward;it is based on assumptions that are in general easier to verify. The proposed extremum seeking framework may encompass more general optimisation algorithms, such as those which do not admit a state-update realisation and/or Lyapunov functions. Multi-unit extremum seeking is also investigated within the context of accelerating the speed of convergence.

关键词： Data sampling optimization algorithms acceleration control law Converge

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Thompson Sampling for 1-Dimensional Exponential Family Bandits 13

Thompson Sampling for 1-Dimensional Exponential Family Bandi...

引用

Annual Conference on Neural Information Processing Systems

作者： Nathaniel Korda Emilie Kaufmann Remi Munos INRIA Lille - Nord Europe Team SequeL Institut Mines-Telecom Telecom ParisTech

ISBN: (纸本)9781632660244

Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by proving asymptotic optimality of the algorithm using the Jeffreys prior for 1-dimensional exponential family bandits. Our proof builds on previous work, but also makes extensive use of closed forms for Kullback-Leibler divergence and Fisher information (through the Jeffreys prior) available in an exponential family. This allow us to give a finite time exponential concentration inequality for posterior distributions on exponential families that may be of interest in its own right. Moreover our analysis covers some distributions for which no optimistic algorithm has yet been proposed, including heavy-tailed exponential families.

关键词： Exponential family closed forms Fisher information optimization algorithms Proof Sampling Posterior distribution

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Low-rank Modifications of Riccati Factorizations with Applications to Model Predictive Control

Low-rank Modifications of Riccati Factorizations with Applic...

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IEEE Annual Conference on Decision and Control

作者： Isak Nielsen Daniel Ankelhed Daniel Axehill Division of Automatic Control Linkoping University SE-58183 Linkoping Sweden

ISBN: (纸本)9781467357159

In optimization algorithms used for on-line Model Predictive Control (MPC), the main computational effort is spent while solving linear systems of equations to obtain search directions. Hence, it is of greatest interest to solve them efficiently, which commonly is performed using Riccati recursions or generic sparsity exploiting algorithms. The focus in this work is efficient search direction computation for activeset methods. In these methods, the system of equations to be solved in each iteration is only changed by a low-rank modification of the previous one. This highly structured change of the system of equations from one iteration to the next one is an important ingredient in the performance of active-set solvers. It seems very appealing to try to make a structured update of the Riccati factorization, which has not been presented in the literature so far. The main objective of this paper is to present such an algorithm for how to update the Riccati factorization in a structured way in an active-set solver. The result of the work is that the computational complexity of the step direction computation can be significantly reduced for problems with bound constraints on the control signal. This in turn has important implications for the computational performance of active-set solvers used for linear, nonlinear as well as hybrid MPC.

关键词： System of equations Predictive control Solvers on-line model iterative methods Model predictive control computational performance optimization algorithms factorization Linear system of equations Linear system nonlinear model predictive control complexity classes

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Optimal Design of a Special Permanent Magnet Synchronous Machine for Magnetocaloric Refrigeration

Optimal Design of a Special Permanent Magnet Synchronous Mac...

引用

IEEE Energy Conversion Congress and Exposition

作者： Christophe Espanet Amine Mira Stefan Giurgea Frederic Gustin Christophe Kieffer University of Franche-Comte Femto-ST Intitute ENERGY department Belfort France

ISBN: (纸本)9781479903375

As the refrigeration technology consumes more than 15% of worldwide consumption of electricity, the development of technologies with improved efficiency remains an important challenge. In this context, magnetic refrigeration is an interesting solution and this paper proposes an original magnetic refrigerator. The aim - and also the originality - of this new device is to integrate the magnetocaloric regenerator in the air gap of a Permanent Magnet Synchronous Machine (PMSM). The conventional design of PMSM stator and rotor is presented and the results lead to the construction of a prototype that enables the validation of the proposed concept. Finally, thanks to the coupling of an optimization algorithm and a 2D FEM simulation, the rotor is optimized and the air-gap flux density is greatly improved (from 0.9 T to 1.35 T).

关键词： PMSM coupling Permanent magnet synchronous machines coupling Optimal design Couplings air-gap flux density electricity consumption optimization algorithms AIR GAP refrigeration Creativity

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Sizing of hybrid energy storage system in independent microgrid based on BP neural network

Sizing of hybrid energy storage system in independent microg...

引用

2nd IET Renewable Power Generation Conference (RPG 2013)

作者： Chengchen Sun Yue Yuan College of Energy and Electrical Engineering Hohai University No.8 Focheng Road (West) Nanjing Jiangsu 211100 China

ISBN: (纸本)9781629936017

As a result of the uncertainties and significant fluctuations of both power generation and consumption in a microgrid, the battery energy storage system (BESS) endures large oscillations in absorbing and releasing active power. This paper proposes a hybrid energy storage system (HESS) composed of supercapacitors and batteries. An optimal design method of HESS capacity is introduced so that HESS can meet the technical requirements, such as smoothing out the wind power fluctuations and the economic requirement in a microgrid. Based on the level of smoothing (LOS) of power curves, a neural network model was established to reflect the interrelationship between characteristic parameters of HESS and LOS of output power transmitted to the independent microgrid. Besides, taking the economic demand into consideration, a long-term mathematical model was built. Optimal algorithm was used to determine optimal size of HESS by optimizing the objective function, which was derived from the built model. Finally, an example indicates the effectiveness of the proposed method.

关键词： Independent microgrid Hybrid energy storage system Neural network battery energy storage system Neural network THERMAL ENERGY STORAGE SYSTEMS flywheel energy storage system SIZING MICRO-GRID energy storage optimization algorithms HYBRID Neural Networks (Computer) Objective function Supercapacitors

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CT SLICE RETRIEVAL BY SHAPE ELLIPSES DESCRIPTORS FOR SKULL REPAIRING

CT SLICE RETRIEVAL BY SHAPE ELLIPSES DESCRIPTORS FOR SKULL R...

引用

IEEE International Conference on Image Processing

作者： Marcelo Rudek Osiris Canciglieri Andreas Jahnen Gerson Linck Bichinho Industrial and Systems Engineering Graduate Program - Pontifical Catholic University of Parana PUCPR Brazil. Ressource Centre for HealthCare Technologies (SANTEC) Public Research Centre Henri Tudor Luxembourg. Technology in Healthcare Postgraduate Program - Pontifical Catholic University of Parana PUCPR Brazil.

ISBN: (纸本)9781479923427

This paper addresses the problem of automated prosthesis modelling and manufacturing, whose machining parameters are based on images extracted from different medical databases. The specific case of 3D surface restoration of a defective skull was used as study case. A method based on adjusted ellipses on skull bone curvature performs the symbolic representation of searching parameters. The superellipse concept permits to define geometric parameters to fit an ellipse on each Computed Tomography slice. Those ellipse descriptors can be used as a template for the retrieval of similar images from databases whose parameters match the sampled image. The similarity is measured according to the best fitness values through an optimization algorithm. The slices found by similarity are retrieved from all databases in order to build the 3D model. Experiments show that the proposed method is a promising technique for content based image retrieval.

关键词： Prosthesis Modelling Image Retrieval Ellipse 3D Medical Imaging Ellipse image retrieval Medical imaging content-based image retrieval Skull retrieval Database X-Ray Computed Tomography Three Dimensions Descriptors images geometric parameter optimization algorithms

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Novel Spiral Antennas Design Using Swarm Intelligence for Passive UHF RFID Tags

Novel Spiral Antennas Design Using Swarm Intelligence for Pa...

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European Conference on Antennas and Propagation

作者： Sotirios K. Goudos Katherine Siakavara John N. Sahalos Radiocommunications Laboratory: Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Radio & Telecommunications Lab: Department of Electrical and Computer Engineering University of Nicosia Cyprus

ISBN: (纸本)9781467321877

In this paper, new planar spiral antennas for passive RFID tag application at UHF band are designed and optimized using the Artificial Bee Colony (ABC) algorithm. The optimization goals are antenna size minimization, gain maximization and conjugate matching. The antenna dimensions were optimized and evaluated using ABC in conjunction with commercial EM software. Furthermore, a theoretical analysis of the spiral antennas was performed. The input impedance of the printed spiral, was calculated approximately via the Transmission Line Model and the electromotive force induced along the spirals. The theoretical results seem to be in good agreement with the EM solver results. The optimization results produced show that ABC is a powerful optimization algorithm that can be efficiently applied to tag antenna design problems. RFID tags with dimensions less than 4cm, gain that reaches the value of 2 dBi and read distance about 11.7m were among those obtained by the algorithm.

关键词： RFID tags Helical antennas Particle swarm optimization Passive RFID tags TRANSMISSION LINE MODELLING impedance optimization algorithms UHF band Spiral

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