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IEEE international workshop on Electromagnetics, applications and Student Innovation (iWEM)

作者： Yoshie Mukainoge Hiroyuki Deguchi Mikio Tsuji Department of Electronics Doshisha University Japan

This paper proposes composed right/left handed transmission lines (CRLH-TL) consisting of arbitrarily-shaped conductor elements, which are designed by a genetic algorithm (GA). However, when the right/left handed transmission lines are constructed by these elements, the higher-order mode coupling between the adjacent elements often causes the unexpected transmission characteristics error and then the performance is deteriorated. So, we choose not only the phase constants in the case of infinite periodic cells but also transmission characteristics in periodic 3 cells as a fitness function. A design example is demonstrated, and its effectiveness is proved from comparison of transmission characteristics between the calculated and the measured results.

关键词： Genetic algorithms Design methodology Power transmission lines Conductors Couplings Transmission line measurements optimization

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optimization of a class of non-convex objectives on the Gaussian MIMO multiple access channel: Algorithm development and convergence analysis

Optimization of a class of non-convex objectives on the Gaus...

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IEEE workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Daniel Calabuig Ramy H. Gohary Halim Yanikomeroglu Institute of Telecommunications and Multimedia Applications Universidad Politécnica de Valencia Valencia Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada

In this paper we develop an algorithm for computing the optimal transmission parameters, which include the transmission covariance, the time-shares and the user-orderings that minimize a particular class of objectives defined over the capacity region of Gaussian multiple antenna multiple access channels. This class includes objectives that are twice-differentiable, non-increasing and convex in the users' rates, but not necessarily convex in the aforementioned transmission parameters. As such, this class includes design objectives that are non-convex and that, without the proposed algorithm, are difficult to solve in general. The proposed algorithm is iterative with polynomial complexity per iteration and with convergence to the global optimal guaranteed. The utility of this algorithm is illustrated via a numerical example for maximizing proportional fairness.

关键词： Signal processing algorithms Algorithm design and analysis Vectors Covariance matrices Convergence optimization Receivers

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Identify important spectrum bands for classification using importances of wrapper selection applied to hyperspectral data

Identify important spectrum bands for classification using i...

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international workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

作者： Arnaud Le Bris Nesrine Chehata Xavier Briottet Nicolas Paparoditis IGN/SR MATIS Saint Mandé France EA 4592 G&E University of Bordeaux Pessac Cedex France IRD/UMR LISAH EI Menzah 4 Tunis Tunisia institution French Aerospace Lab France

Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification accuracy was obtained thanks to SFFS, while a band importance measure was derived from intermediate sets of bands tested by GA. Such results are a first step toward the identification of the most suitable spectral bands to design superspectral camera systems dedicated to specific applications (e.g. classification of urban land cover and material maps).

关键词： Support vector machines Accuracy Genetic algorithms Hyperspectral imaging optimization Radio frequency Kernel

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Iterative Bipartite Graph Edit Distance Approximation

Iterative Bipartite Graph Edit Distance Approximation

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IAPR international workshop on Document Analysis Systems, DAS

作者： Kaspar Riesen Rolf Dornberger Horst Bunke Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Olten Switzerland Institute of Computer Science and Applied Mathematics University of Bern Bern Switzerland

One of the major tasks in many applications in the field of document analysis is the computation of dissimilarities between two or more objects from a given problem domain. Hence, employing graphs as representation formalism evokes the need for powerful, fast and flexible graph based dissimilarity models. Graph edit distance is powerful and applicable to any kind of graphs but suffers from its high computational complexity. Recently, however, a novel framework for graph edit distance approximation has been introduced. While the run time of this novel procedure is very convincing, the precision of the approximated graph distances is dissatisfying in some cases. The present paper introduces a generalized version of the existing approximation framework using an iterative bipartite procedure. With empirical investigations on three real world data sets we show that our extension substantially improves the accuracy of the approximations while the run time is increased only linearly with the number of additional iterations.

关键词： Approximation methods Text analysis Fingerprint recognition Bipartite graph Approximation algorithms optimization

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Parallelization of algorithms for Linear Discrete optimization using ParaPhrase

Parallelization of Algorithms for Linear Discrete Optimizati...

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24th international workshop on Database and Expert Systems applications (DEXA)

作者： Rossbory, Michael Reisner, Werner Software Competence Ctr Hagenberg GmbH Linz Austria

ISBN: (纸本)9780769550701

In industry optimization of processes, production planing, or resource usage is important to reduce costs and increase profit. Mathematical models for optimization can contribute to achieve this, but they also pose some challenges. Not only expertise in mathematics is needed to apply these optimization models, but furthermore expertise in programming is needed for implementation and integration into the software landscape of the company. Additionally most optimization algorithms are computationally very expensive and finding a solution takes a long time. Parallelization reduces the time and can lead to better results, but makes implementation even more challenging. How the high-level pattern-based approach of ParaPhrase [5] and its provided tools reduces this challenges will be described in this paper using a real-world example from industry.

关键词： cost reduction cutting integer programming linear programming mathematics computing paper mills parallel algorithms parallel programming production engineering computing profitability sheet materials

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Meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction

Meta-heuristic algorithms applied to the optimization of typ...

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

作者： Long, Nguyen Cong Meesad, Phayung Faculty of Information Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9781467357265

This paper describes an approach using Firefly Algorithm, Particle Swarm optimization and Genetic Algorithm to optimize the parameters of Takagi-Sugeno-Kang (TSK) fuzzy logic system (both type-1 and type-2) in order to find the optimal fuzzy logic system for sea water level prediction. The obtained results of the simulations performed are compared among these optimization algorithms in order to find which one is the best optimization algorithm for sea water level prediction. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization

A (μ, λ) evolutionary and particle swarm hybrid algorithm,...

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

作者： Matsumura, Yoshiyuki Kobayashi, Ayumu Sugiyama, Kiyotaka Pataky, Todd Yasuda, Toshiyuki Ohkura, Kazuhiro Sellers, Bill Shinshu University 3-15-1 Tokida Ueda Nagano 386-8567 Japan Hiroshima Universisty Kagamiyama Higashihiroshima Hiroshima 739-8527 Japan University of Manchester D1239 Michael Smith Building Oxford Road Manchester M13 9PT United Kingdom

ISBN: (纸本)9781467357265

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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Structural optimization of neural network for data prediction using dimensional compression and tabu search

Structural optimization of neural network for data predictio...

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

作者： Hayashida, Tomohiro Nishizaki, Ichiro Matsumoto, Tsubasa Hiroshima University 1-4-1 Kagamiyama Higashihiroshima Hiroshima Japan

ISBN: (纸本)9781467357265

In the traditional procedures, data classification with a high degree of accuracy by neural networks requires heuristic structural optimization by using expert knowledge. However, the optimization procedure takes an immense amount of time and effort. Additionally, high-dimensional data is difficult to classify for many analysts, thus, it would appears that accuracy of data classification grows higher by proper selection and dimensional compression of input data. This study suggests new procedure for data classification by using neural networks. For dimensional compression of input data, the suggested procedure uses sandglass type neural networks, and tabu search algorithms are applied for input data selection and structural optimization of union between a sandglass type and a feedforward neural networks. © 2013 IEEE.

关键词： Structural optimization

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Data Mining Techniques for Wireless Sensor Networks: A Survey

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international JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2013年第7期9卷 1-24页

作者： Mahmood, Azhar Shi, Ke Khatoon, Shaheen Xiao, Mi Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs. This work provides an overview of how traditional data mining algorithms are revised and improved to achieve good performance in a wireless sensor network environment. A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented. The taxonomy together with the comparative tables can be used as a guideline to select a technique suitable for the application at hand. Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.

关键词： WIRELESS sensor networks DATABASES DATA mining COMPUTER network resources REAL-time computing CONSTRAINED optimization

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013 - Proceedings

2013 IEEE 6th International Workshop on Computational Intell...

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2013 IEEE 6th international workshop on Computational Intelligence and applications, IWCIA 2013

ISBN: (纸本)9781467357265

The proceedings contain 34 papers. The topics discussed include: harmonic filters planning of system for specially connected transformers using PSO-NTVE method;emotion orientated recommendation system for Hiroshima tourist by fuzzy Petri net;an estimation of favorite value in emotion generating calculation by fuzzy Petri net;development of automatic positioning system for bicycle saddle based on lower limb's EMG signals during pedaling motion;dependent input neuron selection in contradiction resolution;location-based burst detection algorithm for geo-referenced document streams based on user's moving direction;meta-heuristic algorithms applied to the optimization of type-1 and type 2 TSK fuzzy logic systems for sea water level prediction;interval-valued differential evolution for evolving neural networks with interval weights and biases;and structural optimization of neural network for data prediction using dimensional compression and tabu search.

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