Estudo e implementação de um filtro ativo paralelo de dois quadrantes conectado no lado de corrente contínua de um retificador monofásico com filtro indutivo

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2010O presente trabalho aborda o estudo e a implementação de um filtro ativo paralelo de dois quadrantes aplicado a um retificador monofásico com filtro indutivo. Seu propósito principal é de corrigir o fator de potência e reduzir o conteúdo harmônico da corrente de entrada desse retificador. Outra motivação, para a aplicação do filtro ativo no retificador indutivo, é a ampliação de sua faixa de operação no modo de condução contínua. Primeiramente, o estudo do retificador é apresentado, juntamente com uma análise da corrente de entrada. Conhecidas as características do retificador, o funcionamento e o equacionamento da estrutura, com o filtro ativo proposto, são detalhados. De posse das funções de transferência do sistema, a estratégia de controle é proposta e o procedimento para a determinação dos compensadores analógicos é desenvolvido. O projeto de todos os circuitos necessários para a implementação prática da estrutura, com a finalidade de confirmar a teoria, também é apresentado. Os resultados obtidos por simulação e experimentalmente validam toda a análise e modelagem realizada e, ainda, comprovam os objetivos do trabalho

Voting with Random Classifiers (VORACE)

In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets

Interval-valued soft constraint problems

Constraints and quantitative preferences, or costs, are very useful for modelling many real-life problems. However, in many settings, it is difficult to specify precise preference values, and it is much more reasonable to allow for preference intervals. We define several notions of optimal solutions for such problems, providing algorithms to find optimal solutions and also to test whether a solution is optimal. Most of the time these algorithms just require the solution of soft constraint prob- lems, which suggests that it may be possible to handle this form of uncertainty in soft constraints without significantly increasing the computational effort needed to reason with such problems. This is supported also by experimental results. We also identify classes of problems where the same results hold if users are allowed to use multiple disjoint intervals rather than a single one

Bribery in voting with soft constraints

Abstract We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality

Safety evaluations of a synthetic antimicrobial peptide administered intravenously in rats and dogs

The antimicrobial peptide SET-M33 is under study for the development of a new antibiotic against major Gram-negative pathogens. Here we report the toxicological evaluation of SET-M33 administered intravenously to rats and dogs. Dose range finding experiments determined the doses to use in toxicokinetic evaluation, clinical biochemistry analysis, necroscopy and in neurological and respiratory measurements. Clinical laboratory investigations in dogs and rats showed a dose-related increase in creatinine and urea levels, indicating that the kidneys are the target organ. This was also confirmed by necroscopy studies of animal tissues, where signs of degeneration and regeneration were found in kidney when SET-M33 was administered at the highest doses in the two animal species. Neurological toxicity measurements by the Irwin method and respiratory function evaluation in rats did not reveal any toxic effect even at the highest dose. Finally, repeated administration of SET-M33 by short infusion in dogs revealed a no-observed-adverse-effect-level of 0.5 mg/kg/day