Desenvolvimento de técnicas eficientes de programação linear na utilização de metaheurísticas para o problema de planejamento da expansão de sistemas de transmissão

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Métodos de solução para um problema de sequenciamento da produção com sincronismo de execução de tarefas

Pós-graduação em Engenharia Mecânica - FEG

Explorando abordagens de aprendizado sequencial para floresta de caminhos ótimos

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

Resolução do problema de sequenciamento de múltiplas linhas de montagem de modelos mistos em ambiente de produção enxuta

This work deals with the sequencing of Multi-Mixed-Model Assembly Lines in a lean manufacturing environment, where an operational structure where several kanbans support several mixed-model assembly lines, so that all assembly lines can receive parts or sub-assemblies from all suppliers. To optimize this system, the sequencing seeks to minimize the distance between the real consumption and the constant ideal consumption of parts or subassemblies, thereby reducing the scaling of kanbans and intermediate stocks. To solve the sequencing problems, the method Clustering Search was applied along with the metaheuristics Variable Neighborhood Search, Simulation Annealing and Iterative Local Search. Instances from the literature and generated instances were tested, thus allowing comparing the methods to each other and with other methods presented in the literature. The performance of the Clustering Search with Iterated Local Search stands out by the quality and robustness of their solutions, and mainly for its efficiency, whereas it converges to better results at a lower computational cost

Heurística especializada aplicada na alocação ótima de bancos de capacitores em sistemas de distribuição radial

Pós-graduação em Engenharia Elétrica - FEIS

Reconfiguração de sistemas de distribuição operando em vários níveis de demanda através de uma meta-heurística de busca em vizinhança variável

Pós-graduação em Engenharia Elétrica - FEIS

Avaliação imunoistoquimica da cérvix de cadelas saudáveis e com o diagnóstico de piometra

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Caratterizzazione e generazione di segnali PWM per amplificatori in classe D ad alta efficienza

The convergence of information technology and consumer electronics towards battery powered portable devices has increased the interest in high efficiency, low dissipation amplifiers. Class D amplifiers are the state of the art in low power consumption and high performance amplification. In this thesis we explore the possibility of exploiting nonlinearities introduced by the PWM modulation, by designing an optimized modulation law which scales its carrier frequency adaptively with the input signal's average power while preserving the SNR, thus reducing power consumption. This is achieved by means of a novel analytical model of the PWM output spectrum, which shows how interfering harmonics and their bandwidth affect the spectrum. This allows for frequency scaling with negligible aliasing between the baseband spectrum and its harmonics. We performed low noise power spectrum measurements on PWM modulations generated by comparing variable bandwidth, random test signals with a variable frequency triangular wave carrier. The experimental results show that power-optimized frequency scaling is both feasible and effective. The new analytical model also suggests a new PWM architecture that can be applied to digitally encoded input signals which are predistorted and compared with a cosine carrier, which is accurately synthesized by a digital oscillator. This approach has been simulated in a realistic noisy model and tested in our measurement setup. A zero crossing search on the obtained PWM modulation law proves that this approach yields an equivalent signal quality with respect to traditional PWM schemes, while entailing the use of signals whose bandwidth is remarkably smaller due to the use of a cosine instead of a triangular carrier.

An Urn Model for Cascading Failures on a Lattice

A cascading failure is a failure in a system of interconnected parts, in which the breakdown of one element can lead to the subsequent collapse of the others. The aim of this paper is to introduce a simple combinatorial model for the study of cascading failures. In particular, having in mind particle systems and Markov random fields, we take into consideration a network of interacting urns displaced over a lattice. Every urn is Pólya-like and its reinforcement matrix is not only a function of time (time contagion) but also of the behavior of the neighboring urns (spatial contagion), and of a random component, which can represent either simple fate or the impact of exogenous factors. In this way a non-trivial dependence structure among the urns is built, and it is used to study default avalanches over the lattice. Thanks to its flexibility and its interesting probabilistic properties, the given construction may be used to model different phenomena characterized by cascading failures such as power grids and financial networks.

Semiparametric Normal Transformation Models for Spatially Correlated Survival Data

There is an emerging interest in modeling spatially correlated survival data in biomedical and epidemiological studies. In this paper, we propose a new class of semiparametric normal transformation models for right censored spatially correlated survival data. This class of models assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables, whose joint distribution is assumed to be multivariate normal with a spatial correlation structure. A key feature of the class of semiparametric normal transformation models is that it provides a rich class of spatial survival models where regression coefficients have population average interpretation and the spatial dependence of survival times is conveniently modeled using the transformed variables by flexible normal random fields. We study the relationship of the spatial correlation structure of the transformed normal variables and the dependence measures of the original survival times. Direct nonparametric maximum likelihood estimation in such models is practically prohibited due to the high dimensional intractable integration of the likelihood function and the infinite dimensional nuisance baseline hazard parameter. We hence develop a class of spatial semiparametric estimating equations, which conveniently estimate the population-level regression coefficients and the dependence parameters simultaneously. We study the asymptotic properties of the proposed estimators, and show that they are consistent and asymptotically normal. The proposed method is illustrated with an analysis of data from the East Boston Ashma Study and its performance is evaluated using simulations.

Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity

We focus on kernels incorporating different kinds of prior knowledge on functions to be approximated by Kriging. A recent result on random fields with paths invariant under a group action is generalised to combinations of composition operators, and a characterisation of kernels leading to random fields with additive paths is obtained as a corollary. A discussion follows on some implications on design of experiments, and it is shown in the case of additive kernels that the so-called class of “axis designs” outperforms Latin hypercubes in terms of the IMSE criterion.

A generalisation of the fractional Brownian field based on non-Euclidean norms

We explore a generalisation of the L´evy fractional Brownian field on the Euclidean space based on replacing the Euclidean norm with another norm. A characterisation result for admissible norms yields a complete description of all self-similar Gaussian random fields with stationary increments. Several integral representations of the introduced random fields are derived. In a similar vein, several non-Euclidean variants of the fractional Poisson field are introduced and it is shown that they share the covariance structure with the fractional Brownian field and converge to it. The shape parameters of the Poisson and Brownian variants are related by convex geometry transforms, namely the radial pth mean body and the polar projection transforms.

Contextual classification of full waveform lidar data in the Wadden Sea

The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3D point cloud. In Wadden Sea areas the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification we combine a Conditional Random Fields framework with a Random Forests approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilise a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighbouring points.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.