Representation of odometry errors on occupancy grids

SOUZA, Anderson A. S. ; SANTANA, André M. ; BRITTO, Ricardo S. ; GONÇALVES, Luiz Marcos G. ; MEDEIROS, Adelardo A. D. Representation of Odometry Errors on Occupancy Grids. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

Prevalence of refractive errors in students in Northeastern Brazil

To determine the prevalence of refractive errors in the public and private school system in the city of Natal, Northeastern Brazil. Methods: Refractometry was performed on both eyes of 1,024 randomly selected students, enrolled in the 2001 school year and the data were evaluated by the SPSS Data Editor 10.0. Ametropia was divided into: 1- from 0.1 to 0.99 diopter (D); 2- 1.0 to 2.99D; 3- 3.00 to 5.99D and 4- 6D or greater. Astigmatism was regrouped in: I- with-the-rule (axis from 0 to 30 and 150 to 180 degrees), II- against-the-rule (axis between 60 and 120 degrees) and III- oblique (axis between > 30 and < 60 and >120 and <150 degrees). The age groups were categorized as follows, in: 1- 5 to 10 years, 2- 11 to 15 years, 3- 16 to 20 years, 4- over 21 years. Results: Among refractive errors, hyperopia was the most common with 71%, followed by astigmatism (34%) and myopia (13.3%). Of the students with myopia and hyperopia, 48.5% and 34.1% had astigmatism, respectively. With respect to diopters, 58.1% of myopic students were in group 1, and 39% distributed between groups 2 and 3. Hyperopia were mostly found in group 1 (61.7%) as well as astigmatism (70.6%). The association of the astigmatism axes of both eyes showed 92.5% with axis with-the-rule in both eyes, while the percentage for those with axis againstthe- rule was 82.1% and even lower for the oblique axis (50%). Conclusion: The results found differed from those of most international studies, mainly from the Orient, which pointed to myopia as the most common refractive error, and corroborates the national ones, with the majority being hyperopia

Method of numerical correction of errors occasioned by delay of records during the monitoring of environmental variables of interest for animal production

As condições de ambiente térmico e aéreo, no interior de instalações para animais, alteram-se durante o dia, devido à influência do ambiente externo. Para que análises estatísticas e geoestatísticas sejam representativas, uma grande quantidade de pontos distribuídos espacialmente na área da instalação deve ser monitorada. Este trabalho propõe que a variação no tempo das variáveis ambientais de interesse para a produção animal, monitoradas no interior de instalações para animais, pode ser modelada com precisão a partir de registros discretos no tempo. O objetivo deste trabalho foi desenvolver um método numérico para corrigir as variações temporais dessas variáveis ambientais, transformando os dados para que tais observações independam do tempo gasto durante a aferição. O método proposto aproximou os valores registrados com retardos de tempo aos esperados no exato momento de interesse, caso os dados fossem medidos simultaneamente neste momento em todos os pontos distribuídos espacialmente. O modelo de correção numérica para variáveis ambientais foi validado para o parâmetro ambiental temperatura do ar, sendo que os valores corrigidos pelo método não diferiram pelo teste Tukey, a 5% de probabilidade dos valores reais registrados por meio de dataloggers.

Egg production curve fitting using nonlinear models for selected and nonselected lines of White Leghorn hens

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Contribuições à análise de robustez de sistemas de controle usando redes neurais

This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented

Desacoplamento de um gerador síncrono através de um controle adaptativo por modelo de referência baseado em funções de Base radial

An alternative nonlinear technique for decoupling and control is presented. This technique is based on a RBF (Radial Basis Functions) neural network and it is applied to the synchronous generator model. The synchronous generator is a coupled system, in other words, a change at one input variable of the system, changes more than one output. The RBF network will perform the decoupling, separating the control of the following outputs variables: the load angle and flux linkage in the field winding. This technique does not require knowledge of the system parameters and, due the nature of radial basis functions, it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied in control. The RBF decoupler is designed in this work for decouple a nonlinear MIMO system with two inputs and two outputs. The weights between hidden and output layer are modified online, using an adaptive law in real time. The adaptive law is developed by Lyapunov s Method. A decoupling adaptive controller uses the errors between system outputs and model outputs, and filtered outputs of the system to produce control signals. The RBF network forces each outputs of generator to behave like reference model. When the RBF approaches adequately control signals, the system decoupling is achieved. A mathematical proof and analysis are showed. Simulations are presented to show the performance and robustness of the RBF network

Investigation of the two-photon absorption cross-section in perylene tetracarboxylic derivatives: Nonlinear spectra and molecular structure

We investigated the 2PA absorption spectrum of a family of perylene tetracarboxylic derivatives ( PTCDs): bis( benzimidazo) perylene ( AzoPTCD), bis( benzimidazo) thioperylene ( Monothio BZP), n-pentylimidobenzimidazoperylene ( PazoPTCD), and bis( n-butylimido) perylene ( BuPTCD). These compounds present extremely high two-photon absorption, which makes them attractive for applications in photonics devices. The two-photon absorption cross-section spectra of perylene derivatives obtained via Z-scan technique were fitted by means of a sum-over-states ( SOS) model, which described with accuracy the different regions of the 2PA cross-section spectra. Frontier molecular orbital calculations show that all molecules present similar features, indicating that nonlinear optical properties in PTCDs are mainly determined by the central portion of the molecule, with minimal effect from the lateral side groups. In general, our results pointed out that the differences in the 2PA cross-sections among the compounds are mainly due to the nonlinearity resonance enhancement.

About the Use of the HdHr Algorithm Group in Integrating the Movement Equation with Nonlinear Terms

This work summarizes the HdHr group of Hermitian integration algorithms for dynamic structural analysis applications. It proposes a procedure for their use when nonlinear terms are present in the equilibrium equation. The simple pendulum problem is solved as a first example and the numerical results are discussed. Directions to be pursued in future research are also mentioned. Copyright (C) 2009 H.M. Bottura and A. C. Rigitano.

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.

Implementation of two-stage Hopfield model and its application in nonlinear systems

This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)