Determinação dos parâmetros do controlador preditivo generalizado com função de pesos variante no tempo através de algoritmos genéticos

The predictive control technique has gotten, on the last years, greater number of adepts in reason of the easiness of adjustment of its parameters, of the exceeding of its concepts for multi-input/multi-output (MIMO) systems, of nonlinear models of processes could be linearised around a operating point, so can clearly be used in the controller, and mainly, as being the only methodology that can take into consideration, during the project of the controller, the limitations of the control signals and output of the process. The time varying weighting generalized predictive control (TGPC), studied in this work, is one more an alternative to the several existing predictive controls, characterizing itself as an modification of the generalized predictive control (GPC), where it is used a reference model, calculated in accordance with parameters of project previously established by the designer, and the application of a new function criterion, that when minimized offers the best parameters to the controller. It is used technique of the genetic algorithms to minimize of the function criterion proposed and searches to demonstrate the robustness of the TGPC through the application of performance, stability and robustness criterions. To compare achieves results of the TGPC controller, the GCP and proportional, integral and derivative (PID) controllers are used, where whole the techniques applied to stable, unstable and of non-minimum phase plants. The simulated examples become fulfilled with the use of MATLAB tool. It is verified that, the alterations implemented in TGPC, allow the evidence of the efficiency of this algorithm

Criação da matéria em cosmologias com velocidade da luz variável e eletrônico não-linear

In this work we obtain the cosmological solutions and investigate the thermodynamics of matter creation in two diferent contexts. In the first we propose a cosmological model with a time varying speed of light c. We consider two diferent time dependence of c for a at Friedmann-Robertson- Walker (FRW) universe. We write the energy conservation law arising from Einstein equations and study how particles are created as c decreases with cosmic epoch. The variation of c is coupled to a cosmological Λ term and both singular and non-singular solutions are possible. We calculate the "adiabatic" particle creation rate and the total number of particles as a function of time and find the constrains imposed by the second law of thermodynamics upon the models. In the second scenario, we study the nonlinearity of the electrodynamics as a source of matter creation in the cosmological models with at FRW geometry. We write the energy conservation law arising from Einstein field equations with cosmological term Λ, solve the field equations and study how particles are created as the magnetic field B changes with cosmic epoch. We obtain solutions for the adiabatic particle creation rate, the total number of particles and the scale factor as a function of time in three cases: Λ = 0, Λ = constant and Λ α H2 (cosmological term proportional to the Hubble parameter). In all cases, the second law of thermodynamics demands that the universe is not contracting (H ≥ 0). The first two solutions are non-singular and exhibit in ationary periods. The third case studied allows an always in ationary universe for a suficiently large cosmological term

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Avaliação espectrofotométrica do manchamento de resinas odontológicas contendo nanopartículas

Composite resins have been subjected to structural modifications aiming at improved optical and mechanical properties. The present study consisted in an in vitro evaluation of the staining behavior of two nanohybrid resins (NH1 and NH2), a nanoparticulated resin (NP) and a microhybrid resin (MH). Samples of these materials were prepared and immersed in commonly ingested drinks, i.e., coffee, red wine and acai berry for periods of time varying from 1 to 60 days. Cylindrical samples of each resin were shaped using a metallic die and polymerized during 30 s both on the bottom and top of its disk. All samples were polished and immersed in the staining solutions. After 24 hours, three samples of each resin immersed in each solution were removed and placed in a spectrofotome ter for analysis. To that end, the samples were previously diluted in HCl at 50%. Tukey tests were carried out in the statistical analysis of the results. The results revealed that there was a clear difference in the staining behavior of each material. The nanoparticulated resin did not show better color stability compared to the microhybrid resin. Moreover, all resins stained with time. The degree of staining decreased in the sequence nanoparticulated, microhybrid, nanohybrid MH2 and MH1. Wine was the most aggressive drink followed by coffee and acai berry. SEM and image analysis revealed significant porosity on the surface of MH resin and relatively large pores on a NP sample. The NH2 resin was characterized by homogeneous dispersion of particles and limited porosity. Finally, the NH1 resin depicted the lowest porosity level. The results revealed that staining is likely related to the concentration of inorganic pa rticles and surface porosity

Controlador preditivo generalizado multi-modelo aplicado ao controle de pressão arterial

Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to reduce elevated blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient are time varying. the objective of the study is to develop a procedure for blood pressure control i the presence of uncertainty of parameters and considerable time delays. So, a methodology was developed multi-model, and for each such model a Preditive Controller can be a priori designed. An adaptive mechanism is then needed for deciding which controller should be dominant for a given plant

Estudo da topologia de redes de conexão funcional no córtex sensorial primário e hipocampo durante o sono de ondas lentas

Complex network analysis is a powerful tool into research of complex systems like brain networks. This work aims to describe the topological changes in neural functional connectivity networks of neocortex and hippocampus during slow-wave sleep (SWS) in animals submited to a novel experience exposure. Slow-wave sleep is an important sleep stage where occurs reverberations of electrical activities patterns of wakeness, playing a fundamental role in memory consolidation. Although its importance there s a lack of studies that characterize the topological dynamical of functional connectivity networks during that sleep stage. There s no studies that describe the topological modifications that novel exposure leads to this networks. We have observed that several topological properties have been modified after novel exposure and this modification remains for a long time. Major part of this changes in topological properties by novel exposure are related to fault tolerance

Avaliação de desempenho da codificação wavelet em canais seletivos em frequência

Wavelet coding has emerged as an alternative coding technique to minimize the fading effects of wireless channels. This work evaluates the performance of wavelet coding, in terms of bit error probability, over time-varying, frequency-selective multipath Rayleigh fading channels. The adopted propagation model follows the COST207 norm, main international standards reference for GSM, UMTS, and EDGE applications. The results show the wavelet coding s efficiency against the inter symbolic interference which characterizes these communication scenarios. This robustness of the presented technique enables its usage in different environments, bringing it one step closer to be applied in practical wireless communication systems

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

Evolução top-hat hidrodinâmica em campos de velocidades peculiares primordiais

We investigate the cosmology of the vacuum energy decaying into cold dark matter according to thermodynamics description of Alcaniz & Lima. We apply this model to analyze the evolution of primordial density perturbations in the matter that gave rise to the first generation of structures bounded by gravity in the Universe, called Population III Objects. The analysis of the dynamics of those systems will involve the calculation of a differential equation system governing the evolution of perturbations to the case of two coupled fluids (dark matter and baryonic matter), modeled with a Top-Hat profile based in the perturbation of the hydrodynamics equations, an efficient analytical tool to study the properties of dark energy models such as the behavior of the linear growth factor and the linear growth index, physical quantities closely related to the fields of peculiar velocities at any time, for different models of dark energy. The properties and the dynamics of current Universe are analyzed through the exact analytical form of the linear growth factor of density fluctuations, taking into account the influence of several physical cooling mechanisms acting on the density fluctuations of the baryonic component of matter during the evolution of the clouds of matter, studied from the primordial hydrogen recombination. This study is naturally extended to more general models of dark energy with constant equation of state parameter in a flat Universe

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Avaliação espectrofotométrica do manchamento de resinas odontológicas contendo nanopartículas

Composite resins have been subjected to structural modifications aiming at improved optical and mechanical properties. The present study consisted in an in vitro evaluation of the staining behavior of two nanohybrid resins (NH1 and NH2), a nanoparticulated resin (NP) and a microhybrid resin (MH). Samples of these materials were prepared and immersed in commonly ingested drinks, i.e., coffee, red wine and acai berry for periods of time varying from 1 to 60 days. Cylindrical samples of each resin were shaped using a metallic die and polymerized during 30 s both on the bottom and top of its disk. All samples were polished and immersed in the staining solutions. After 24 hours, three samples of each resin immersed in each solution were removed and placed in a spectrofotome ter for analysis. To that end, the samples were previously diluted in HCl at 50%. Tukey tests were carried out in the statistical analysis of the results. The results revealed that there was a clear difference in the staining behavior of each material. The nanoparticulated resin did not show better color stability compared to the microhybrid resin. Moreover, all resins stained with time. The degree of staining decreased in the sequence nanoparticulated, microhybrid, nanohybrid MH2 and MH1. Wine was the most aggressive drink followed by coffee and acai berry. SEM and image analysis revealed significant porosity on the surface of MH resin and relatively large pores on a NP sample. The NH2 resin was characterized by homogeneous dispersion of particles and limited porosity. Finally, the NH1 resin depicted the lowest porosity level. The results revealed that staining is likely related to the concentration of inorganic pa rticles and surface porosity