On singularly perturbed Filippov systems

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

Piecewise Bounded Quadratic Systems in the Plane

In this paper we study the sliding mode of piecewise bounded quadratic systems in the plane given by a non-smooth vector field Z=(X,Y). Analyzing the singular, crossing and sliding sets, we get the conditions which ensure that any solution, including the sliding one, is bounded.

Algoritmo tabu search especializado para o problema de planejamento da expansao de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

On the number of limit cycles in discontinuous piecewise linear differential systems with two pieces separated by a straight line

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

Equações diferenciais implícitas com descontinuidade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Equações diferenciais ordinárias não suaves autônomas e não autônomas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Equações diferenciais ordinárias não suaves autônomas e não autônomas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modeling random corrosion processes via polynomial chaos expansion

Polynomial Chaos Expansion (PCE) is widely recognized as a flexible tool to represent different types of random variables/processes. However, applications to real, experimental data are still limited. In this article, PCE is used to represent the random time-evolution of metal corrosion growth in marine environments. The PCE coefficients are determined in order to represent data of 45 corrosion coupons tested by Jeffrey and Melchers (2001) at Taylors Beach, Australia. Accuracy of the representation and possibilities for model extrapolation are considered in the study. Results show that reasonably accurate smooth representations of the corrosion process can be obtained. The representation is not better because a smooth model is used to represent non-smooth corrosion data. Random corrosion leads to time-variant reliability problems, due to resistance degradation over time. Time variant reliability problems are not trivial to solve, especially under random process loading. Two example problems are solved herein, showing how the developed PCE representations can be employed in reliability analysis of structures subject to marine corrosion. Monte Carlo Simulation is used to solve the resulting time-variant reliability problems. However, an accurate and more computationally efficient solution is also presented.

Design and Computation of Warped Time-Frequency Transforms

In this work we introduce an analytical approach for the frequency warping transform. Criteria for the design of operators based on arbitrary warping maps are provided and an algorithm carrying out a fast computation is defined. Such operators can be used to shape the tiling of time-frequency plane in a flexible way. Moreover, they are designed to be inverted by the application of their adjoint operator. According to the proposed mathematical model, the frequency warping transform is computed by considering two additive operators: the first one represents its nonuniform Fourier transform approximation and the second one suppresses aliasing. The first operator is known to be analytically characterized and fast computable by various interpolation approaches. A factorization of the second operator is found for arbitrary shaped non-smooth warping maps. By properly truncating the operators involved in the factorization, the computation turns out to be fast without compromising accuracy.

Direct and inverse transient eddy current problems

Die vorliegende Arbeit behandelt Vorwärts- sowie Rückwärtstheorie transienter Wirbelstromprobleme. Transiente Anregungsströme induzieren elektromagnetische Felder, welche sogenannte Wirbelströme in leitfähigen Objekten erzeugen. Im Falle von sich langsam ändernden Feldern kann diese Wechselwirkung durch die Wirbelstromgleichung, einer Approximation an die Maxwell-Gleichungen, beschrieben werden. Diese ist eine lineare partielle Differentialgleichung mit nicht-glatten Koeffizientenfunktionen von gemischt parabolisch-elliptischem Typ. Das Vorwärtsproblem besteht darin, zu gegebener Anregung sowie den umgebungsbeschreibenden Koeffizientenfunktionen das elektrische Feld als distributionelle Lösung der Gleichung zu bestimmen. Umgekehrt können die Felder mit Messspulen gemessen werden. Das Ziel des Rückwärtsproblems ist es, aus diesen Messungen Informationen über leitfähige Objekte, also über die Koeffizientenfunktion, die diese beschreibt, zu gewinnen. In dieser Arbeit wird eine variationelle Lösungstheorie vorgestellt und die Wohlgestelltheit der Gleichung diskutiert. Darauf aufbauend wird das Verhalten der Lösung für verschwindende Leitfähigkeit studiert und die Linearisierbarkeit der Gleichung ohne leitfähiges Objekt in Richtung des Auftauchens eines leitfähigen Objektes gezeigt. Zur Regularisierung der Gleichung werden Modifikationen vorgeschlagen, welche ein voll parabolisches bzw. elliptisches Problem liefern. Diese werden verifiziert, indem die Konvergenz der Lösungen gezeigt wird. Zuletzt wird gezeigt, dass unter der Annahme von sonst homogenen Umgebungsparametern leitfähige Objekte eindeutig durch die Messungen lokalisiert werden können. Hierzu werden die Linear Sampling Methode sowie die Faktorisierungsmethode angewendet.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

Transpose return relation method for designing low noise oscillators

In this paper, a new linear method for optimizing compact low noise oscillators for RF/MW applications will be presented. The first part of this paper makes an overview of Leeson's model. It is pointed out, and it is demonstrates that the phase noise is always the same inside the oscillator loop. It is presented a general phase noise optimization method for reference plane oscillators. The new method uses Transpose Return Relations (RRT) as true loop gain functions for obtaining the optimum values of the elements of the oscillator, whatever scheme it has. With this method, oscillator topologies that have been designed and optimized using negative resistance, negative conductance or reflection coefficient methods, until now, can be studied like a loop gain method. Subsequently, the main disadvantage of Leeson's model is overcome, and now it is not only valid for loop gain methods, but it is valid for any oscillator topology. The last section of this paper lists the steps to be performed to use this method for proper phase noise optimization during the linear design process and before the final non-linear optimization. The power of the proposed RRT method is shown with its use for optimizing a common oscillator, which is later simulated using Harmonic Balance (HB) and manufactured. Then, the comparison of the linear, HB and measurements of the phase noise are compared.

Aportación al análisis de espectros de radiación: estructuras y algoritmos

A lo largo del presente trabajo se investiga la viabilidad de la descomposición automática de espectros de radiación gamma por medio de algoritmos de resolución de sistemas de ecuaciones algebraicas lineales basados en técnicas de pseudoinversión. La determinación de dichos algoritmos ha sido realizada teniendo en cuenta su posible implementación sobre procesadores de propósito específico de baja complejidad. En el primer capítulo se resumen las técnicas para la detección y medida de la radiación gamma que han servido de base para la confección de los espectros tratados en el trabajo. Se reexaminan los conceptos asociados con la naturaleza de la radiación electromagnética, así como los procesos físicos y el tratamiento electrónico que se hallan involucrados en su detección, poniendo de relieve la naturaleza intrínsecamente estadística del proceso de formación del espectro asociado como una clasificación del número de detecciones realizadas en función de la energía supuestamente continua asociada a las mismas. Para ello se aporta una breve descripción de los principales fenómenos de interacción de la radiación con la materia, que condicionan el proceso de detección y formación del espectro. El detector de radiación es considerado el elemento crítico del sistema de medida, puesto que condiciona fuertemente el proceso de detección. Por ello se examinan los principales tipos de detectores, con especial hincapié en los detectores de tipo semiconductor, ya que son los más utilizados en la actualidad. Finalmente, se describen los subsistemas electrónicos fundamentales para el acondicionamiento y pretratamiento de la señal procedente del detector, a la que se le denomina con el término tradicionalmente utilizado de Electrónica Nuclear. En lo que concierne a la espectroscopia, el principal subsistema de interés para el presente trabajo es el analizador multicanal, el cual lleva a cabo el tratamiento cualitativo de la señal, y construye un histograma de intensidad de radiación en el margen de energías al que el detector es sensible. Este vector N-dimensional es lo que generalmente se conoce con el nombre de espectro de radiación. Los distintos radionúclidos que participan en una fuente de radiación no pura dejan su impronta en dicho espectro. En el capítulo segundo se realiza una revisión exhaustiva de los métodos matemáticos en uso hasta el momento ideados para la identificación de los radionúclidos presentes en un espectro compuesto, así como para determinar sus actividades relativas. Uno de ellos es el denominado de regresión lineal múltiple, que se propone como la aproximación más apropiada a los condicionamientos y restricciones del problema: capacidad para tratar con espectros de baja resolución, ausencia del concurso de un operador humano (no supervisión), y posibilidad de ser soportado por algoritmos de baja complejidad capaces de ser instrumentados sobre procesadores dedicados de alta escala de integración. El problema del análisis se plantea formalmente en el tercer capítulo siguiendo las pautas arriba mencionadas y se demuestra que el citado problema admite una solución en la teoría de memorias asociativas lineales. Un operador basado en este tipo de estructuras puede proporcionar la solución al problema de la descomposición espectral deseada. En el mismo contexto, se proponen un par de algoritmos adaptativos complementarios para la construcción del operador, que gozan de unas características aritméticas especialmente apropiadas para su instrumentación sobre procesadores de alta escala de integración. La característica de adaptatividad dota a la memoria asociativa de una gran flexibilidad en lo que se refiere a la incorporación de nueva información en forma progresiva.En el capítulo cuarto se trata con un nuevo problema añadido, de índole altamente compleja. Es el del tratamiento de las deformaciones que introducen en el espectro las derivas instrumentales presentes en el dispositivo detector y en la electrónica de preacondicionamiento. Estas deformaciones invalidan el modelo de regresión lineal utilizado para describir el espectro problema. Se deriva entonces un modelo que incluya las citadas deformaciones como una ampliación de contribuciones en el espectro compuesto, el cual conlleva una ampliación sencilla de la memoria asociativa capaz de tolerar las derivas en la mezcla problema y de llevar a cabo un análisis robusto de contribuciones. El método de ampliación utilizado se basa en la suposición de pequeñas perturbaciones. La práctica en el laboratorio demuestra que, en ocasiones, las derivas instrumentales pueden provocar distorsiones severas en el espectro que no pueden ser tratadas por el modelo anterior. Por ello, en el capítulo quinto se plantea el problema de medidas afectadas por fuertes derivas desde el punto de vista de la teoría de optimización no lineal. Esta reformulación lleva a la introducción de un algoritmo de tipo recursivo inspirado en el de Gauss-Newton que permite introducir el concepto de memoria lineal realimentada. Este operador ofrece una capacidad sensiblemente mejorada para la descomposición de mezclas con fuerte deriva sin la excesiva carga computacional que presentan los algoritmos clásicos de optimización no lineal. El trabajo finaliza con una discusión de los resultados obtenidos en los tres principales niveles de estudio abordados, que se ofrecen en los capítulos tercero, cuarto y quinto, así como con la elevación a definitivas de las principales conclusiones derivadas del estudio y con el desglose de las posibles líneas de continuación del presente trabajo.---ABSTRACT---Through the present research, the feasibility of Automatic Gamma-Radiation Spectral Decomposition by Linear Algebraic Equation-Solving Algorithms using Pseudo-Inverse Techniques is explored. The design of the before mentioned algorithms has been done having into account their possible implementation on Specific-Purpose Processors of Low Complexity. In the first chapter, the techniques for the detection and measurement of gamma radiation employed to construct the spectra being used throughout the research are reviewed. Similarly, the basic concepts related with the nature and properties of the hard electromagnetic radiation are also re-examined, together with the physic and electronic processes involved in the detection of such kind of radiation, with special emphasis in the intrinsic statistical nature of the spectrum build-up process, which is considered as a classification of the number of individual photon-detections as a function of the energy associated to each individual photon. Fbr such, a brief description of the most important matter-energy interaction phenomena conditioning the detection and spectrum formation processes is given. The radiation detector is considered as the most critical element in the measurement system, as this device strongly conditions the detection process. Fbr this reason, the characteristics of the most frequent detectors are re-examined, with special emphasis on those of semiconductor nature, as these are the most frequently employed ones nowadays. Finally, the fundamental electronic subsystems for preaconditioning and treating of the signal delivered by the detector, classically addresed as Nuclear Electronics, is described. As far as Spectroscopy is concerned, the subsystem most interesting for the scope covered by the present research is the so-called Multichannel Analyzer, which is devoted to the cualitative treatment of the signal, building-up a hystogram of radiation intensity in the range of energies in which the detector is sensitive. The resulting N-dimensional vector is generally known with the ñame of Radiation Spectrum. The different radio-nuclides contributing to the spectrum of a composite source will leave their fingerprint in the resulting spectrum. Through the second chapter, an exhaustive review of the mathematical methods devised to the present moment to identify the radio-nuclides present in the composite spectrum and to quantify their relative contributions, is reviewed. One of the more popular ones is the so-known Múltiple Linear Regression, which is proposed as the best suited approach according to the constraints and restrictions present in the formulation of the problem, i.e., the need to treat low-resolution spectra, the absence of control by a human operator (un-supervision), and the possibility of being implemented as low-complexity algorithms amenable of being supported by VLSI Specific Processors. The analysis problem is formally stated through the third chapter, following the hints established in this context, and it is shown that the addressed problem may be satisfactorily solved under the point of view of Linear Associative Memories. An operator based on this kind of structures may provide the solution to the spectral decomposition problem posed. In the same context, a pair of complementary adaptive algorithms useful for the construction of the solving operator are proposed, which share certain special arithmetic characteristics that render them specially suitable for their implementation on VLSI Processors. The adaptive nature of the associative memory provides a high flexibility to this operator, in what refers to the progressive inclusión of new information to the knowledge base. Through the fourth chapter, this fact is treated together with a new problem to be considered, of a high interest but quite complex nature, as is the treatment of the deformations appearing in the spectrum when instrumental drifts in both the detecting device and the pre-acconditioning electronics are to be taken into account. These deformations render the Linear Regression Model proposed almost unuseful to describe the resulting spectrum. A new model including the drifts is derived as an extensión of the individual contributions to the composite spectrum, which implies a simple extensión of the Associative Memory, which renders this suitable to accept the drifts in the composite spectrum, thus producing a robust analysis of contributions. The extensión method is based on the Low-Amplitude Perturbation Hypothesis. Experimental practice shows that in certain cases the instrumental drifts may provoke severe distortions in the resulting spectrum, which can not be treated with the before-mentioned hypothesis. To cover also these less-frequent cases, through the fifth chapter, the problem involving strong drifts is treated under the point of view of Non-Linear Optimization Techniques. This reformulation carries the study to the consideration of recursive algorithms based on the Gauss-Newton methods, which allow the introduction of Feed-Back Memories, computing elements with a sensibly improved capability to decompose spectra affected by strong drifts. The research concludes with a discussion of the results obtained in the three main levéis of study considerad, which are presented in chapters third, fourth and fifth, toghether with the review of the main conclusions derived from the study and the outline of the main research lines opened by the present work.

Path-following analysis of the dynamical response of a piecewise-linear capsule system

Acknowledgements The first author has been supported by a Georg Forster Research Fellowship granted by the Alexander von Humboldt Foundation, Germany

Comparative study of RPSALG algorithm for convex semi-infinite programming

The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.