Controle robusto com realimentação derivativa de sistemas não lineares via LMI

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Alocação de pólos com realimentação da derivada dos estados usando LMIs

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bilhares dependentes do tempo: um mecanismo para suprimir aceleração de Fermi

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliação de nove espécies de Eucalyptus spp. em tratamento preservativo industrial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento de um sensor virtual para processos não-lineares e variantes no tempo, com aplicação em planta de neutralização de PH

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Controle robusto chaveado de sistemas lineares variantes no tempo com aplicação em falhas estruturais

Pós-graduação em Engenharia Elétrica - FEIS

Controle robusto de sistemas não lineares e chaveado de sistemas lineares usando realimentação derivativa

Pós-graduação em Engenharia Elétrica - FEIS

Projeto de controlador gain scheduling usando realimentação derivativa via LMI

Pós-graduação em Engenharia Elétrica - FEIS

Scalar field description of decaying-Lambda cosmologies

The conditions under which cosmologies driven by time-varying cosmological terms can be described by a scalar field coupled to a perfect fluid are discussed. An algorithm to reconstruct potentials dynamically and thermodynamically analogous to given phenomenological λ models is presented. As a working example, the deflationary cosmology which evolves from a pure de Sitter vacuum state to a slightly modified Friedmann-Robertson-Walker cosmology is considered. It is found that this is an example of nonsingular warm inflation with an asymptotic exponential potential. Differences with respect to other scalar field descriptions of decaying vacuum cosmologies are addressed and possible extensions are indicated.

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

A Time-domain Harmonic Power-Flow Analysis in Electrical Energy Distribution Networks, Using Norton Models for Non-linear Loading

The purpose of this paper is to present the application of a three-phase harmonic propagation analysis time-domain tool, using the Norton model to approach the modeling of non-linear loads, making the harmonics currents flow more appropriate to the operation analysis and to the influence of mitigation elements analysis. This software makes it possible to obtain results closer to the real distribution network, considering voltages unbalances, currents imbalances and the application of mitigation elements for harmonic distortions. In this scenario, a real case study with network data and equipments connected to the network will be presented, as well as the modeling of non-linear loads based on real data obtained from some PCCs (Points of Common Coupling) of interests for a distribution company.

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.