Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Approximate factorization constraint preconditioners for saddle-point matrices

We consider the application of the conjugate gradient method to the solution of large, symmetric indefinite linear systems. Special emphasis is put on the use of constraint preconditioners and a new factorization that can reduce the number of flops required by the preconditioning step. Results concerning the eigenvalues of the preconditioned matrix and its minimum polynomial are given. Numerical experiments validate these conclusions.

Performance evaluation of wall mounted convector for pre-heating naturally ventilated spaces

This paper presents the results of performance monitoring under real winter weather conditions, controlled laboratory testing and computational fluid dynamics (CFD) analysis of a wall mounted ventilation air inlet heat convector. For real winter weather monitoring, the wall-mounted convector was installed in a laboratory room of the Engineering Building of the School of Construction Management and Engineering. Air and hot water temperatures and air speeds were measured at the entrance to the convector and in the room. The hot water temperature was controlled at 40, 60 and 80 °C. The monitoring results were later used as boundary conditions for a CFD simulation to investigate the air movement in the room. Controlled laboratory testing was conducted in laboratories at the University of Reading, UK and at Wetterstad Consultancy, Sweden. The results of the performance investigation showed that the system contributed greatly to the room heating, particularly at a water temperature of 80 °C. Also adequate fresh air was supplied to the room. Such a system is able to provide an energy efficient method of eliminating problems associated with cold winter draughts.

Iterative Methods for Equality-Constrained Least Squares Problems

We consider the linear equality-constrained least squares problem (LSE) of minimizing ${\|c - Gx\|}_2 $, subject to the constraint $Ex = p$. A preconditioned conjugate gradient method is applied to the Kuhn–Tucker equations associated with the LSE problem. We show that our method is well suited for structural optimization problems in reliability analysis and optimal design. Numerical tests are performed on an Alliant FX/8 multiprocessor and a Cray-X-MP using some practical structural analysis data.

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

Antenas de microfita com patch quase-fractal para aplicações em Redes WPAN/WLAN

The microstrip antennas are in constant evidence in current researches due to several advantages that it presents. Fractal geometry coupled with good performance and convenience of the planar structures are an excellent combination for design and analysis of structures with ever smaller features and multi-resonant and broadband. This geometry has been applied in such patch microstrip antennas to reduce its size and highlight its multi-band behavior. Compared with the conventional microstrip antennas, the quasifractal patch antennas have lower frequencies of resonance, enabling the manufacture of more compact antennas. The aim of this work is the design of quasi-fractal patch antennas through the use of Koch and Minkowski fractal curves applied to radiating and nonradiating antenna s edges of conventional rectangular patch fed by microstrip inset-fed line, initially designed for the frequency of 2.45 GHz. The inset-fed technique is investigated for the impedance matching of fractal antennas, which are fed through lines of microstrip. The efficiency of this technique is investigated experimentally and compared with simulations carried out by commercial software Ansoft Designer used for precise analysis of the electromagnetic behavior of antennas by the method of moments and the neural model proposed. In this dissertation a study of literature on theory of microstrip antennas is done, the same study is performed on the fractal geometry, giving more emphasis to its various forms, techniques for generation of fractals and its applicability. This work also presents a study on artificial neural networks, showing the types/architecture of networks used and their characteristics as well as the training algorithms that were used for their implementation. The equations of settings of the parameters for networks used in this study were derived from the gradient method. It will also be carried out research with emphasis on miniaturization of the proposed new structures, showing how an antenna designed with contours fractals is capable of a miniaturized antenna conventional rectangular patch. The study also consists of a modeling through artificial neural networks of the various parameters of the electromagnetic near-fractal antennas. The presented results demonstrate the excellent capacity of modeling techniques for neural microstrip antennas and all algorithms used in this work in achieving the proposed models were implemented in commercial software simulation of Matlab 7. In order to validate the results, several prototypes of antennas were built, measured on a vector network analyzer and simulated in software for comparison

Modelos de dispositivos de microondas e ópticos através de redes neurais artificiais de alimentação direta

This dissertation contributes for the development of methodologies through feed forward artificial neural networks for microwave and optical devices modeling. A bibliographical revision on the applications of neuro-computational techniques in the areas of microwave/optical engineering was carried through. Characteristics of networks MLP, RBF and SFNN, as well as the strategies of supervised learning had been presented. Adjustment expressions of the networks free parameters above cited had been deduced from the gradient method. Conventional method EM-ANN was applied in the modeling of microwave passive devices and optical amplifiers. For this, they had been proposals modular configurations based in networks SFNN and RBF/MLP objectifying a bigger capacity of models generalization. As for the training of the used networks, the Rprop algorithm was applied. All the algorithms used in the attainment of the models of this dissertation had been implemented in Matlab

Estimativa do fluxo de calor latente pelo balanço de energia em cultivo protegido de pimentão

O objetivo deste trabalho foi caracterizar e relacionar a radiação líquida com o calor latente equivalente, em mm de água, nos cultivos protegido e de campo, na cultura de pimentão. O experimento foi feito em Botucatu, SP. A estimativa do fluxo de calor latente foi feita pelo método do balanço de energia, por meio da razão de Bowen. Foram feitas medidas instantâneas da radiação líquida (Rn), dos fluxos convectivos de calor latente (LE) e sensível (H), do fluxo de calor no solo (G), e dos gradientes psicrométricos sobre a cultura. O cultivo protegido, apesar de receber menor quantidade de radiação solar global, foi mais eficiente na conversão da radiação líquida disponível em matéria seca total e na produtividade de frutos. No balanço de energia, o cultivo protegido apresentou razões G/Rn e LE/Rn inferiores e H/Rn superior, com um fluxo de calor latente, equivalente em milímetros, 45,43% menor que no cultivo no campo. Apresentou, ainda, menor quantidade de radiação líquida disponível e menores perdas de energia, mostrando-se mais eficiente no uso da água.

Estimativa da evapotranspiração espacial em uma região semiárida utilizando sensoriamento remoto

With the need to deploy management and monitoring systems of natural resources in areas susceptible to environmental degradation, as is the case of semiarid regions, several works have been developed in order to find effective models and technically and economically viable. Therefore, this study aimed to estimate the daily actual evapotranspiration (ETr) through the application of the Surface Energy Balance Algorithm for Land (SEBAL), from remote sensing products, in a semiarid region, Seridó of the Rio Grande do Norte, and do the validation of these estimates using ETr values obtained by the Penman-Monteith (standard method of the Food and Agriculture Organization-FAO). The SEBAL is based on energy balance method, which allows obtaining the vertical latent heat flux (LE) with orbital images and, consequently, of the evapotranspiration through the difference of flows, also vertical, of heat in the soil (G), sensitive heat (H) and radiation balance (Rn). The study area includes the surrounding areas of the Dourado reservoir, located in the Currais Novos/RN city. For the implementation of the algorithm were used five images TM/Landsat-5. The work was divided in three chapters in order to facilitate a better discussion of each part of the SEBAL processing, distributed as follows: first chapter addressing the spatio-temporal variability of the biophysical variables; second chapter dealing with spatio-temporal distribution of instant and daily radiation balance; and the third chapter discussing the heart of the work, the daily actual evapotranspiration estimation and the validation than to the study area

Development of practical HPLC methods for the separation and determination of eggplant steroidal glycoalkaloids and their aglycones

A practical set of HPLC methods was developed for the separation and determination of the eggplant steroidal glycoalkaloids, solanine, chaconine, solasonine, solamargine, and their aglycones, solasodine and solanidine. A gradient method was initially developed, but proved to be neither robust nor practical. Three separate isocratic methods using acetonitrile and ammonium dihydrogen phosphate were developed and shown to be more repeatable, less subject to fluctuations in mobile phase composition, and less time consuming. The effect of adjusting buffer pH, column temperature, and buffer type (triethylammonium phosphate vs. ammonium dihydrogen phosphate) were evaluated. It was also discovered that, by addition of 10% methanol to the acetonitrile portion of the mobile phase, more control over the separations was possible. The use of methanol as a mobile phase entrainer greatly improved separations in some cases and its effectiveness was also dependent upon column temperature. Assessments of the method recovery, limit of detection, and limit of quantitation were made using extracts from S. melongena and S. linnaeanum.

Otimização vetorial e técnicas de mistura de herbicidas aplicadas ao controle de plantas daninhas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo dos efeitos da compensação série no desempenho transitório dos sistemas de energia elétrica sob o enfoque da energia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de bebida a base de leite de soja acrescida de suco de graviola

Pós-graduação em Alimentos e Nutrição - FCFAR

Caracterização físico-química, energética e sensorial de refresco adoçado de jabuticaba

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Identificação e controle de sistemas dinâmicos utilizando redes wavelets

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)