Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

Maximização da penetração da geração distribuída através do algoritmo de otimização nuvem de partículas

This work develops a methodology for defining the maximum active power being injected into predefined nodes in the studied distribution networks, considering the possibility of multiple accesses of generating units. The definition of these maximum values is obtained from an optimization study, in which further losses should not exceed those of the base case, i.e., without the presence of distributed generation. The restrictions on the loading of the branches and voltages of the system are respected. To face the problem it is proposed an algorithm, which is based on the numerical method called particle swarm optimization, applied to the study of AC conventional load flow and optimal load flow for maximizing the penetration of distributed generation. Alternatively, the Newton-Raphson method was incorporated to resolution of the load flow. The computer program is performed with the SCILAB software. The proposed algorithm is tested with the data from the IEEE network with 14 nodes and from another network, this one from the Rio Grande do Norte State, at a high voltage (69 kV), with 25 nodes. The algorithm defines allowed values of nominal active power of distributed generation, in percentage terms relative to the demand of the network, from reference values

Avaliacao de dados de secagem de suspensoes de polpas de frutas em leito de jorro com alimentacao intermitente

The objective of this work was the development and improvement of the mathematical models based on mass and heat balances, representing the drying transient process fruit pulp in spouted bed dryer with intermittent feeding. Mass and energy balance for drying, represented by a system of differential equations, were developed in Fortran language and adapted to the condition of intermittent feeding and mass accumulation. Were used the DASSL routine (Differential Algebraic System Solver) for solving the differential equation system and used a heuristic optimization algorithm in parameter estimation, the Particle Swarm algorithm. From the experimental data food drying, the differential models were used to determine the quantity of water and the drying air temperature at the exit of a spouted bed and accumulated mass of powder in the dryer. The models were validated using the experimental data of drying whose operating conditions, air temperature, flow rate and time intermittency, varied within the limits studied. In reviewing the results predicted, it was found that these models represent the experimental data of the kinetics of production and accumulation of powder and humidity and air temperature at the outlet of the dryer

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

Uma análise experimental de abordagens heurísticas aplicadas ao problema do caixeiro viajante

Due to great difficulty of accurate solution of Combinatorial Optimization Problems, some heuristic methods have been developed and during many years, the analysis of performance of these approaches was not carried through in a systematic way. The proposal of this work is to make a statistical analysis of heuristic approaches to the Traveling Salesman Problem (TSP). The focus of the analysis is to evaluate the performance of each approach in relation to the necessary computational time until the attainment of the optimal solution for one determined instance of the TSP. Survival Analysis, assisted by methods for the hypothesis test of the equality between survival functions was used. The evaluated approaches were divided in three classes: Lin-Kernighan Algorithms, Evolutionary Algorithms and Particle Swarm Optimization. Beyond those approaches, it was enclosed in the analysis, a memetic algorithm (for symmetric and asymmetric TSP instances) that utilizes the Lin-Kernighan heuristics as its local search procedure

Uma abordagem por nuvem de partículas para problemas de otimização combinatória

Combinatorial optimization problems have the goal of maximize or minimize functions defined over a finite domain. Metaheuristics are methods designed to find good solutions in this finite domain, sometimes the optimum solution, using a subordinated heuristic, which is modeled for each particular problem. This work presents algorithms based on particle swarm optimization (metaheuristic) applied to combinatorial optimization problems: the Traveling Salesman Problem and the Multicriteria Degree Constrained Minimum Spanning Tree Problem. The first problem optimizes only one objective, while the other problem deals with many objectives. In order to evaluate the performance of the algorithms proposed, they are compared, in terms of the quality of the solutions found, to other approaches

Algorítmo evolucionário para a distribuição de produtos de petróleo por redes de polidutos

The distribution of petroleum products through pipeline networks is an important problem that arises in production planning of refineries. It consists in determining what will be done in each production stage given a time horizon, concerning the distribution of products from source nodes to demand nodes, passing through intermediate nodes. Constraints concerning storage limits, delivering time, sources availability, limits on sending or receiving, among others, have to be satisfied. This problem can be viewed as a biobjective problem that aims at minimizing the time needed to for transporting the set of packages through the network and the successive transmission of different products in the same pipe is called fragmentation. This work are developed three algorithms that are applied to this problem: the first algorithm is discrete and is based on Particle Swarm Optimization (PSO), with local search procedures and path-relinking proposed as velocity operators, the second and the third algorithms deal of two versions based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed algorithms are compared to other approaches for the same problem, in terms of the solution quality and computational time spent, so that the efficiency of the developed methods can be evaluated

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Detecção de falhas em estruturas inteligentes usando otimização por nuvem de partículas: fundamentos e estudo de casos

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

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

A multi-objective evaluation of the impact of the penetration of Distributed Generation

This paper proposes a methodology to consider the effects of the integration of DG on planning. Since DG has potential to defer investments in networks, the impact of DG on grid capacity is evaluated. A multi-objective optimization tool based on the meta-heuristic MEPSO is used, supporting an alternative approach to exploiting the Pareto front features. Tests were performed in distinct conditions with two well-known distribution networks: IEEE-34 and IEEE-123. The results combined minimization and maximization in order to produce different Pareto fronts and determine the extent of the impact caused by DG. The analysis provides useful information, such as the identification of futures that should be considered in planning. A future means a set of realizations of all uncertainties. MEPSO also presented a satisfactory performance in obtaining the Pareto fronts. © 2011 IEEE.