Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

Avaliação e implementação de métodos de estimação de tempo de atraso de sinais de ultra-som

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Identificação do teor de nitrogênio foliar em cana-de-açúcar no estágio inicial de crescimento utilizando infravermelho próximo e sistemas inteligentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Neural network based estimation of torque in induction motors for real-time applications

Induction motors are largely used in several industry sectors. The selection of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this article is to use artificial neural networks for torque estimation with the purpose of best selecting the induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Since proposed approach estimates the torque behavior from the transient to the steady state, one of its main contributions is the potential to also be implemented in control schemes for real-time applications. Simulation results are also presented to validate the proposed approach.

Configuration-dependent diffusion can shift the kinetic transition state and barrier height of protein folding

We show that diffusion can play an important role in protein-folding kinetics. We explicitly calculate the diffusion coefficient of protein folding in a lattice model. We found that diffusion typically is configuration- or reaction coordinate-dependent. The diffusion coefficient is found to be decreasing with respect to the progression of folding toward the native state, which is caused by the collapse to a compact state constraining the configurational space for exploration. The configuration- or position-dependent diffusion coefficient has a significant contribution to the kinetics in addition to the thermodynamic free-energy barrier. It effectively changes (increases in this case) the kinetic barrier height as well as the position of the corresponding transition state and therefore modifies the folding kinetic rates as well as the kinetic routes. The resulting folding time, by considering both kinetic diffusion and the thermodynamic folding free-energy profile, thus is slower than the estimation from the thermodynamic free-energy barrier with constant diffusion but is consistent with the results from kinetic simulations. The configuration- or coordinate-dependent diffusion is especially important with respect to fast folding, when there is a small or no free-energy barrier and kinetics is controlled by diffusion. Including the configurational dependence will challenge the transition state theory of protein folding. The classical transition state theory will have to be modified to be consistent. The more detailed folding mechanistic studies involving phi value analysis based on the classical transition state theory also will have to be modified quantitatively.

Real-time monitoring and kinetic parameter estimation of the affinity interaction of jArtinM and rArtinM with peroxidase glycoprotein by the electrogravimetric technique

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

Estimation of water transit time in soils under Amazon forest cover using variations in sigma18O values.

In an attempt to estimate the soil-water transit time using the variation in 18O values, a statistical model was used. This model is based on linear regression analysis applied to the values observed for soil water and rain water. The time obtained from these correlations represents the mean time necessary for the water to run from one collecting point to the next.-from Authors

Estimation using 18O of the water residence time in small watersheds

From the 18O hydrograph separation method, it was found that groundwater contribution is the principal component of the total discharge produced by these two catchment areas. The weighted mean of the 18O concentration in the precipitation, obtained for a four year period, was close to -6.0‰, with a variation in range of +2.3‰ to -16.3‰. For Bufalos stream water the weighted mean of 18O values during the same period (1984-1987) was -6.3‰, with a variation from -2.5‰ to -10.1‰, whereas for Paraiso this mean was -6.4‰, with extreme values of -3.1‰ and -9.8‰. From these values it was found that the amplitude damping (Ariver/Aprecipitation) was 0.41 for the Bufalos watershed and 0.36 for Paraiso. Using the appropriate equation to estimate the mean residence time of water in the subsurface reservoir of the Bufalos and Paraiso watersheds, the results of 4.3 and 5.0 months, respectively, were obtained. -from Authors

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

Real-time estimation of transmission line impedance based on modal analysis theory

The objective of this paper is to show a methodology to estimate the longitudinal parameters of transmission lines. The method is based on the modal analysis theory and developed from the currents and voltages measured at the sending and receiving ends of the line. Another proposal is to estimate the line impedance in function of the real-time load apparent power and power factor. The procedure is applied for a non-transposed 440 kV three-phase line. © 2011 IEEE.

Estimation of genetic parameters for partial egg production periods by means of random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Comparison of methodologies for degree-day estimation using numerical methods

O desenvolvimento de projetos relacionados ao desempenho de diversas culturas tem recebido aperfeiçoamento cada vez maior, incorporado a modelos matemáticos sendo indispensável à utilização de equações cada vez mais consistentes que possibilitem previsão e maior aproximação do comportamento real, diminuindo o erro na obtenção das estimativas. Entre as operações unitárias que demandam maior estudo estão aquelas relacionadas com o crescimento da cultura, caracterizadas pela temperatura ideal para o acréscimo de matéria seca. Pelo amplo uso dos métodos matemáticos na representação, análise e obtenção de estimativas de graus-dia, juntamente com a grande importância que a cultura da cana-de-açúcar tem para a economia brasileira, foi realizada uma avaliação dos modelos matemáticos comumente usados e dos métodos numéricos de integração na estimativa da disponibilidade de graus-dia para essa cultura, na região de Botucatu, Estado de São Paulo. Os modelos de integração, com discretização de 6 em 6 h, apresentaram resultados satisfatórios na estimativa de graus-dia. As metodologias tradicionais apresentaram desempenhos satisfatórios quanto à estimativa de grausdia com base na curva de temperatura horária para cada dia e para os agrupamentos de três, sete, 15 e 30 dias. Pelo método numérico de integração, a região de Botucatu, Estado de São Paulo, apresentou disponibilidade térmica anual média de 1.070,6 GD para a cultura da cana-de-açúcar.

Using neural networks for estimation of aquifer dynamical behavior

The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.

Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.