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Cauchy particle swarm optimization with dynamic adaptation applied to inverse heat transfer problem

Cauchy particle swarm optimization with dynamic adaptation a...

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2010 IEEE International Conference on systems, Man and Cybernetics, SMC 2010

作者： Mariani, Viviana Cocco Neckel, Vagner Jorge Grebogi, Rafael Bartnik Dos Santos Coelho, Leandro Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424465880

The particle swarm optimization (PSO) algorithm is a member of the wide category of swarm intelligence methods for solving global optimization problems. Its basic idea is the simulation of simplified animal social behaviors such as fish schooling and bird flocking. PSO algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. In this paper, a modified version of the original PSO based on Cauchy distribution and dynamic adaptation of inertia factor, named modified PSO (MPSO), is proposed. to estimate the unknown variables of an inverse heat transfer problem. To validate the optimization performance of the proposed MPSO, an inverse heat transfer problem is illustrated and the algorithm has to estimate its unknown variables. The results testify that the MPSO can perform well in an inverse heat transfer problem. ©2010 IEEE.

关键词： Heat transfer

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Geometric modeling from the medical images in the CAD system to support prosthesis design

Geometric modeling from the medical images in the CAD system...

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International Conference on Computers & Industrial engineering (CIE)

作者： Osiris Canciglieri M. Rudek Tiago Francesconi Teófilo Miguel de Souza Pontificia Universidade Catolica do Parana Curitiba PR BR Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Department of Manufacturing Engineering of Pontifical Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Department of Electrical Engineering São Paulo State University (UNESP) Guaratinguetá SP Brazil

This paper presents an individual designing prosthesis for surgical use and proposes a methodology for such design through mathematical extrapolation of data from digital images obtained via tomography of individual patient's bones. Individually tailored prosthesis designed to fit particular patient requirements as accurately as possible should result in more successful reconstruction, enable better planning before surgery and consequently fewer complications during surgery. Fast and accurate design and manufacture of personalized prosthesis for surgical use in bone replacement or reconstruction is potentially feasible through the application and integration of several different existing technologies, which are each at different stages of maturity. Initial case study experiments have been undertaken to validate the research concepts by making dimensional comparisons between a bone and a virtual model produced using the proposed methodology and a future research directions are discussed.

关键词： Solid modeling Design automation Bones Tomography Prosthetics Surgery Image reconstruction

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A case study of concurrent engineering application on the development of parts for the White Goods industry in Brazil

A case study of concurrent engineering application on the de...

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International Conference on Computers & Industrial engineering (CIE)

作者： Osiris Canciglieri R. J. B. de Sampaio J. P. B. Kovalchuk Teófilo Miguel de Souza Department of Manufacturing Engineering of Pontifical Catholic Industrial and Systems Engineering Graduate Program (PPGEPS) University of Paraná (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering São Paulo State University Sao Paulo Brazil

This classical way to manage product development processes for massive production seems to be changing: high pressure for cost reduction, higher quality standards, markets reaching for innovation lead to the necessity of new tools for development control. Into this, and learning from the automotive and aerospace industries factories from other segments are starting to understand and apply manufacturing and assembly oriented projects to ease the task of generate goods and from this obtain at least a part of the expected results. This paper is intended to demonstrate the applicability of the concepts of Concurrent engineering and DFM/DFA (Design for Manufacturing and Assembly) in the development of products and parts for the White Goods industry in Brazil (major appliances as refrigerators, cookers and washing machines), showing one case concerning the development and releasing of a component. Finally is demonstrated in a short term how was reached a solution that could provide cost savings and reduction on the time to delivery using those techniques.

关键词： Doped fiber amplifiers Assembly production Plastics Industries

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Forecasting electricity prices using a RBF neural network With GARCH errors

Forecasting electricity prices using a RBF neural network Wi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Andre Alves Portela Santos Leandro dos Santos Coelho Carlos Eduardo Klein Department of Statistics Universidad Carlos III de Madrid Spain Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil CNPq National Council for Scientific and Technological Development Brazil

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose a novel estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide hour-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

关键词： Load modeling Artificial neural networks Predictive models

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Multivariable nonlinear boiler power plant identification through neural networks and Particle Swarm Optimization approaches

Multivariable nonlinear boiler power plant identification th...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Fábio A. Guerra Helon V. H. Ayala André E. Lazzaretti Marcio R. Sans Leandro S. Coelho Cesar A. Tacla Eletroelectronics Department (DPEE) Electrical System Division (DVSE) Centro Politécnico da UFPR LACTEC-Institute of Technology for Development Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program (LAS/PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Graduate Program in Eletrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology Parana Curitiba Parana Brazil

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

关键词： Artificial neural networks Boilers Neurons Modeling Power generation Optimization Nonlinear systems

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Methodology proposal for acquisition of biotelemetric data in a real time

Methodology proposal for acquisition of biotelemetric data i...

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International Conference on Computers & Industrial engineering (CIE)

作者： Osiris Canciglieri M. Rudek Lucas A. Bodaczny Teófilo Miguel de Souza Pontificia Universidade Catolica do Parana Curitiba PR BR Department of Manufacturing Engineering Pontifical Catholic University of Paran Curitiba Parana Brazil Department of Manufacturing Engineering of Pontifical Catholic Industrial and Systems Engineering Graduate Program (PPGEPS) Curitiba PR Brazil Pontifical Catholic University of Paran Curitiba Parana Brazil Department of Electrical Engineering Sao Paulo State University (UNESP) Guaratingueta SP Brazil

This work presents a methodological proposal for acquisition of biometric data through telemetry basing its development on a research-action and a case study. Nowadays, the qualified professionals of physical evaluation have to use specific devices to obtain biometric signals and data. These devices in the most of the time are high cost and difficult to use and handling. Therefore, the methodological proposal was elaborate in order to develop, conceptually, a bio telemetric device which could acquire the desirable biometric signals: oxymetry, biometrics, corporal temperature and pedometry which are essential for the area of physical evaluation. It was researched the existent biometrics sensors, the possible ways for the remote transmission of signals and the computer systems available so that the acquisition of data could be possible. This methodological proposal of remote acquisition of biometrical signals is structured in four modules: Acquisitor of biometrics data; Converser and transmitter of biometric signals; Receiver and Processor of biometrics signals and Generator of Interpretative Graphs. The modules aim the obtention of interpretative graphics of human biometric signals. In order to validate this proposal a functional prototype was developed and it is presented in the development of this work.

关键词： Temperature sensors Proposals Bioinformatics Real time systems Product development Temperature measurement

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Power Demand Forecast Using Least-Squares Support Vector Machines

Power Demand Forecast Using Least-Squares Support Vector Mac...

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International Conference on Intelligent systems Applications to Power systems (ISAP)

作者： Leandro dos Santos Coelho Carlos Eduardo Klein Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

This paper aims to share the results on forecasting power demand using least-squares support vector machines. The development is based on model estimation taking in consideration the past measurements for power demand and ambient temperature. All approximated models were evaluated using the multiple correlation coefficient (R 2 ) or mean absolute percentage error (MAPE) and maximum error combined as quality parameters.

关键词： Power demand Demand forecasting Support vector machines Power system modeling Temperature Power generation Power engineering and energy systems engineering and theory Power measurement System identification

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A Harmony search algorithm combined with differential operator applied to reliability-redundancy optimization

A Harmony search algorithm combined with differential operat...

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2009 IEEE International Conference on systems, Man and Cybernetics, SMC 2009

作者： Dos Santos Coelho, Leandro De Andrade Bernert, Diego Luis Mariani, Viviana Cocco Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424427949

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. This paper introduces a modified HS approach combined with an operator of differential evolution - a paradigm of evolutionary computation - to solve optimization problems in reliability engineering. In this context, an example of mixed integer programming in reliability-redundancy design of an over-speed protection system for a gas turbine is evaluated. In this application domain, HS was found to outperform the previously best-known solutions available. ©2009 IEEE.

关键词： Integer programming

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A normative self-organizing migrating algorithm for power economic dispatch of thermal generators with valve-point effects and multiple fuels

A normative self-organizing migrating algorithm for power ec...

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IEEE International Conference on systems, Man and Cybernetics

作者： Viviana Cocco Mariani Rodrigo Clemente Thom de Souza Leandro dos Santos Coelho Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Curitiba Parana Brazil Industrial and Systems Engineering PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Parana Brazil

A new class of meta-heuristics called SOMA (Self-Organizing Migrating Algorithm) was proposed in recent literature. SOMA works on a population of potential solutions called specimen and it is based on the self-organizing behavior of groups of individuals in a ¿social environment¿. This paper proposes a modified SOMA approach to solving the economic load dispatch problem of thermal generators with the valve-point effect. To show the performance of the proposed modified SOMA algorithm based on fundamentals of normative knowledge in cultural algorithms, which was applied to test the power economic problem comprised 10 generating units with valve-point effects and multiple fuels for the load demands of 2400 MW. Simulation results show that the classical and modified SOMA algorithms are efficient and have good convergence property when compared with results of other optimization methods reported in the literature.

关键词： Power generation economics Fuel economy Power generation Environmental economics Power system economics Industrial economics systems engineering and theory Stochastic processes Evolutionary computation Modeling

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An LMI approach for output feedback Robust Predictive Control using Orthonormal Basis Functions

An LMI approach for output feedback Robust Predictive Contro...

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IEEE Conference on Decision and Control

作者： Humberto X. Araújo Gustavo H. C. Oliveira Department of Electrical Engineering Federal University of Bahia Salvador Bahia Brazil Industrial and Systems Engineering Graduate Program (PPGEPS Pontifícia Universidade Catóica do Paraná Curitiba Parana Brazil

ISBN: (纸本)9781424438716

This paper is focused on the problem of uncertain process control by using RMPC (robust model predictive control). A relevant class of RMPC algorithms is the one characterized by the use of the LMI framework. This field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been proposed. Most of them assume a polytopic representation of the process uncertainties and require full-state feedback. On the other hand, several works describing the theory and applicability of OBF (orthonormal basis functions) in identification and control fields can be found in the literature. So, the present paper proposes the use of OBF for uncertain process modelling and for RMPC algorithm synthesis. It is shown that, due to the proposed approach, one obtains an output feedback control law, with set-point tracking. This represents an advantage in actual applications. Moreover, well established state feedback RMPC strategies can be reviewed under the OBF modelling perspective. Simulation results illustrate the proposed methodology.

关键词： Output feedback Robust control Predictive control Predictive models Uncertainty State feedback Prediction algorithms Finite impulse response filter Parameter estimation Lyapunov method

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