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Comparison between two FPGA implementations of the particle swarm optimization algorithm for high-performance embedded applications

Comparison between two FPGA implementations of the particle ...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGPES Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Particle Swarm Optimization (PSO) algorithms have been proposed to solve engineering problems that require to find an optimal point of operation. There are several embedded applications which requires to solve online optimization problems with a high performance. However, the PSO suffers on large execution times, and this fact becomes evident when using Reduced Instruction Set Computer (RISC) microprocessors in which the operational frequencies are low in comparison with the high operational frequencies of traditional personal computers (PCs). This paper compares two hardware implementations of the parallel PSO algorithm using an efficient floating-point arithmetic which perform computations with large dynamic range and high precision. The full-parallel and the partially-parallel PSO architectures allow the parallel capabilities of the PSO to be exploited in order to decrease the running time. Three well-known benchmark test functions have been used to validate the hardware architectures and a comparison in terms of cost in logic area, quality of the solution and performance is reported. In addition, a comparison of the execution time between the hardware and two C-code software implementations, based on a Intel Core Duo at 1.6GHz and a embedded Microblaze microprocessor at 50MHz, are presented. © 2010 IEEE.

关键词： Global optimization

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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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Biogeography-Based Optimization Combined with Predator-Prey Approach Applied to Economic Load Dispatch

Biogeography-Based Optimization Combined with Predator-Prey ...

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Brazilian Symposium on Neural Networks

作者： Marsil de Athayde Costa e Silva Leandro dos Santos Coelho Undergraduate Course in Mechatronics Engineering Pontifical Catholic University of Paraná (PUCPR) Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná (PUCPR) Curitiba Paraná Brazil

Economic Load Dispatch (ELD) problems are nonlinear constrained problems which occupy an important role in the economic operation of power system. Recently, as an alternative to the conventional mathematical approaches, evolutionary algorithms have been given much attention by researchers due to their ability to find good solutions in ELD problems. In this paper, a biogeography based-optimization approach is validated. Biogeography is the study of the geographical distribution of biological organisms. Furthermore, a new approach of the biogeography based-optimization methodology combined with predator-prey approach were proposed and validated for two test systems consisting of 15 and 20 generation units (thermal generators), respectively. Simulation results are compared with those of other studies reported in the literature.

关键词： Optimization Biogeography Mathematical model Generators Economics Convergence Equations

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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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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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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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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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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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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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2009 15th International Conference on Intelligent System Applications to Power systems, ISAP '09

作者： Dos Santos Coelho, Leandro Klein, Carlos Eduardo PUC-PR Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Imaculada Conceição 1155 Zip code 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424450985

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 (R2) or mean absolute percentage error (MAPE) and maximum error combined as quality parameters. © 2009 IEEE.

关键词： Support vector machines

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