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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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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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Clonal Selection Algorithm with Oppositional Approach Applied to Trajectory Planning of a Robotic Manipulator

Clonal Selection Algorithm with Oppositional Approach Applie...

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Brazilian Symposium on Neural Networks

作者： Daniel C. Jeronymo Yuri C. C. Borges Leandro dos S. Coelho Department of Electrical Engineering Federal University of Paraná (UFPR) Curitiba Brazil Telecommunications and Control Department Polytechnique School University of São Paulo Sao Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

A complex optimization problem in robotics is the optimum movement between certain physical configurations while minimizing certain criteria. The fundamental objective in movement planning for robot arms is to minimize movement time, total distance, joint torque, avoiding obstacles and the robot arm itself as an obstacle. This paper presents a study on clonal selection algorithm (CSA) and CSA with oppositional approach to optimize the 2D point to point movement of a 3-links robot arm. Simulations with MATLAB of the problem were implemented with both techniques to minimize an objective function depending on the mentioned criteria. Results of both methods are presented, showing that the CSA with oppositional approach presented better results than the classical CSA in terms of convergence and response quality.

关键词： Manipulators Joints Planning Torque Trajectory Optimization

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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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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasilia D.F Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F Brazil

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide suboptimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems.

关键词： Mathematics Field programmable gate arrays Table lookup

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Design-dimensioning model for grid-connected photovoltaic systems

Design-dimensioning model for grid-connected photovoltaic sy...

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2009 IEEE electrical Power and Energy Conference, EPEC 2009

作者： Hernandez, Johann Diaz, Nelson L. Gordillo, Gerardo Universiudad Nacional de Colombia Bogota Colombia Department of Electrical and Computer Engineering Graduate Program in Engineering - Industrial Automation Universidad Nacional de Colombia Bogota Colombia Universidad Nacional Bogota Colombia

ISBN: (纸本)9781424445080

The main goal in design of grid connected photo voltaic systems is to maximize the energy generation based on local energy requirements, weather conditions, economic and social impact. These factors ought to be taken into account to perform a model that allow design grid-connected photo voltaic systems. In this paper a design-dimensioning model for a small grid-connected photo voltaic systems is developed. This model is based on energy conditions instead of power conditions. There are shown the two main trends that are typically used in the design process and the calculation of the two main components of the system (generator and inverter). An example of the design for a system located in Bogota-Colombia is studied. ©2009 IEEE.

关键词： Design

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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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Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference

作者： Humberto X.Araújo Gustavo H.C.Oliveira Department of Electrical Engineering Federal University of Bahia (UFBA) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná(PUCPR)Rua Imaculada Concei?o

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 *** field started in the middle of nineties and since then several works applying LMIs in the context of RMPC have been *** of them assume a polytopic representation of the process uncertainties and require full-state *** 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 ***,the present paper proposes the use of OBF for uncertain process modelling and for RMPC algorithm *** is shown that,due to the proposed approach,one obtains an output feedback control law,with set-point *** represents an advantage in actual ***,well established state feedback RMPC strategies can be reviewed under the OBF modelling *** results illustrate the proposed methodology.

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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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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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