Uso de redes neurais com adaptação de pesos por modos deslizantes para controle de sistemas e aplicações em máquinas elétricas

Pós-graduação em Engenharia Elétrica - FEIS

Tm-afm nonlinear motion control with robustness analysis to parametric errors in the control signal determination

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

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

Parametric analyses of anxiety in zebrafish scototaxis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Power flow for primary distribution networks considering uncertainty in demand and user connection

This paper presents a method for calculating the power flow in distribution networks considering uncertainties in the distribution system. Active and reactive power are used as uncertain variables and probabilistically modeled through probability distribution functions. Uncertainty about the connection of the users with the different feeders is also considered. A Monte Carlo simulation is used to generate the possible load scenarios of the users. The results of the power flow considering uncertainty are the mean values and standard deviations of the variables of interest (voltages in all nodes, active and reactive power flows, etc.), giving the user valuable information about how the network will behave under uncertainty rather than the traditional fixed values at one point in time. The method is tested using real data from a primary feeder system, and results are presented considering uncertainty in demand and also in the connection. To demonstrate the usefulness of the approach, the results are then used in a probabilistic risk analysis to identify potential problems of undervoltage in distribution systems. (C) 2012 Elsevier Ltd. All rights reserved.

Remarks on Parametric Surface Waves in a Nonlinear and Non-Ideally Excited Tank

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Mapping an uncertainty zone between interpolated types of a categorical variable

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Parametric evaluation of methotrimeprazine-midazolam-ketamine and methotrimeprazine-midazolam-ketamine-xylazine combination in dogs

OBJETIVO: Avaliar os parâmetros de cães anestesiados com diferentes protocolos de fármacos dissociativos por infusão intravenosa contínua. MÉTODOS: Foram utilizados 30 cães, machos e fêmeas, clinicamente sadios, distribuídos aleatoriamente em três grupos (G1,G2 e G3) (*)). em G1 utilizou-se levomepromazina como medicação pré-anestésica (MPA), midazolam-cetamina pela via intravenosa em bolus para indução e midazolam-cetamina em infusão intravenosa contínua por 60 minutos para manutenção. em G2 procedeu-se da mesma forma que em G1 elevando-se, porém, a dose de midazolam durante a manutenção. em G3 repetiu-se o tratamento empregado em G2, acrescentando-se a xilazina à manutenção. Após a indução, iniciou-se imediatamente a manutenção anestésica, realizando-se aferições, 15 minutos depois da MPA, em intervalos de 10 minutos, durante a manutenção (M0 a M7). RESULTADOS: em G3 ocorreu bradicardia, bloqueio átrio-ventricular, bradipnéia e hipoxemia e em G1 e G2, discreta hipotensão. CONCLUSÃO: A via intravenosa contínua apresentou vantagens quanto a: não oscilação dos parâmetros e redução no período de recuperação anestésica. A elevação da dose de midazolam resultou em discretas variações paramétricas, estas, acentuadas pelo uso da xilazina, que causou hipoxemia, bradiarritmia, diminuição da freqüência respiratória e volume minuto.

DERIVATION OF THE ENERGY-TIME UNCERTAINTY RELATION

A derivation from first principles is given of the energy-time uncertainty relation in quantum mechanics. A canonical transformation is made in classical mechanics to a new canonical momentum, which is energy E, and a new canonical coordinate T, which is called tempus, conjugate to the energy. Tempus T, the canonical coordinate conjugate to the energy, is conceptually different from the time t in which the system evolves. The Poisson bracket is a canonical invariant, so that energy and tempus satisfy the same Poisson bracket as do p and q. When the system is quantized, we find the energy-time uncertainty relation DELTAEDELTAT greater-than-or-equal-to HBAR/2. For a conservative system the average of the tempus operator T is the time t plus a constant. For a free particle and a particle acted on by a constant force, the tempus operators are constructed explicitly, and the energy-time uncertainty relation is explicitly verified.

Transmission network expansion planning considering uncertainty in demand

This paper presents two mathematical models and one methodology to solve a transmission network expansion planning problem considering uncertainty in demand. The first model analyzed the uncertainty in the system as a whole; then, this model considers the uncertainty in the total demand of the power system. The second one analyzed the uncertainty in each load bus individually. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The models presented are solved using a specialized genetic algorithm. The results obtained for several known systems from literature show that cheaper plans can be found satisfying the uncertainty in demand.

DIAGNOSIS of REGIONAL CEREBRAL BLOOD FLOW ABNORMALITIES USING SPECT: AGREEMENT BETWEEN INDIVIDUALIZED STATISTICAL PARAMETRIC MAPS and VISUAL INSPECTION BY NUCLEAR MEDICINE PHYSICIANS WITH DIFFERENT LEVELS of EXPERTISE IN NUCLEAR NEUROLOGY

INTRODUCTION: Visual analysis is widely used to interpret regional cerebral blood flow (rCBF) SPECT images in clinical practice despite its limitations. Automated methods are employed to investigate between-group rCBF differences in research Studies but have rarely been explored in individual analyses.OBJECTIVES: To compare visual inspection by nuclear physicians with the automated statistical parametric mapping program using a SPECT dataset of patients with neurological disorders and normal control images.METHODS: Using statistical parametric mapping, 14 SPECT images from patients with various neurological disorders were compared individually with a databank of 32 normal images using a statistical threshold of p<0.05 (corrected for multiple comparisons at the level of individual voxels or clusters). Statistical parametric mapping results were compared with Visual analyses by a nuclear physician highly experienced in neurology (A) as well as a nuclear physician with a general background of experience (B) who independently classified images as normal or altered, and determined the location of changes and the severity.RESULTS: of the 32 images of the normal databank, 4 generated maps showing rCBF abnormalities (p<0.05, corrected). Among the 14 images from patients with neurological disorders, 13 showed rCBF alterations. Statistical parametric mapping and physician A completely agreed on 84.37% and 64.28% of cases from the normal databank and neurological disorders, respectively. The agreement between statistical parametric mapping and ratings of physician B were lower (71.18% and 35.71%, respectively).CONCLUSION: Statistical parametric mapping replicated the findings described by the more experienced nuclear physician. This finding suggests that automated methods for individually analyzing rCBF SPECT images may be a valuable resource to complement visual inspection in clinical practice.

Robust control to parametric uncertainties in smart structures using linear matrix inequalities

The study of algorithms for active vibrations control in flexible structures became an area of enormous interest, mainly due to the countless demands of an optimal performance of mechanical systems as aircraft and aerospace structures. Smart structures, formed by a structure base, coupled with piezoelectric actuators and sensor are capable to guarantee the conditions demanded through the application of several types of controllers. This article shows some steps that should be followed in the design of a smart structure. It is discussed: the optimal placement of actuators, the model reduction and the controller design through techniques involving linear matrix inequalities (LMI). It is considered as constraints in LMI: the decay rate, voltage input limitation in the actuators and bounded output peak (output energy). Two controllers robust to parametric variation are designed: the first one considers the actuator in non-optimal location and the second one the actuator is put in an optimal placement. The performance are compared and discussed. The simulations to illustrate the methodology are made with a cantilever beam with bonded piezoelectric actuators.

Estimates of covariance functions for growth of Nelore cattle applying a parametric correlation structure to model within-animal correlations

A total of 20,065 weights recorded on 3016 Nelore animals were used to estimate covariance functions for growth from birth to 630 days of age, assuming a parametric correlation structure to model within-animal correlations. The model of analysis included fixed effects of contemporary groups and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Genetic effects of the animal and its dam and maternal permanent environmental effects were modelled by random regressions on Legendre polynomials of age at recording. Changes in direct permanent environmental effect variances were modelled by a polynomial variance function, together with a parametric correlation function to account for correlations between ages. Stationary and nonstationary models were used to model within-animal correlations between different ages. Residual variances were considered homogeneous or heterogeneous, with changes modelled by a step or polynomial function of age at recording. Based on Bayesian information criterion, a model with a cubic variance function combined with a nonstationary correlation function for permanent environmental effects, with 49 parameters to be estimated, fitted best. Modelling within-animal correlations through a parametric correlation structure can describe the variation pattern adequately. Moreover, the number of parameters to be estimated can be decreased substantially compared to a model fitting random regression on Legendre polynomial of age. © 2004 Elsevier B.V. All rights reserved.

Análise da incerteza na representação de classes de cobertura do solo urbano resultantes da aplicação de uma rede neural artificial

The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.

Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. 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 estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2007.