Optimisation of a fermentation process for butanol production by particle swarm optimisation (PSO)

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

Kappa-PSO-ARTMAP Fuzzy: uma metodologia para detecção de intrusos baseado em seleção de atributos e otimização de parâmetros numa rede neural ARTMAP Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

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.

Use of radiograph optical densitometry to monitor bone quality in broiler breeders

This study was carried out at Faculdade de Medicina Veterinária e Zootecnia, Universidade Estadual Paulista, Botucatu, SP, Brazil, and evaluated bone quality in broiler breeders. Twenty-three families of Ross broiler breeders were housed in 5.0-m² pens. The families were comprised of 13 females and one male at the onset of the experimental period. The mean number of females per family was 9.34 at the end of the trial. The feeding program and management followed strain guidelines (Agroceres Ross, 2003). Bone analyses were performed in the right tibia and femur using optical radiographic densitometry at 4, 8, 12, 15, 20, 24, 30, 35, 42, 47 and 52 weeks of rearing. Trap nests were used to collect eggs from the breeders two weeks before and after the evaluation weeks. At each evaluation day, five birds were sacrificed after radiographs were taken and the tibias and femurs were collected to perform the following analyses: fatfree dry matter, ash percentage, bone resistance and Seedor index. Therefore, it was possible to establish correlations between bone quality and eggshell quality. Characteristics of bone quality were highly correlated to each other; on the other hand, there were no correlations between bone quality and external egg quality. In conclusion, there was no effect of egg production on egg quality, possibly because there was no reabsorption of bone minerals.

The malarial impact on the nutritional status of Amazonian adult subjects

A avaliação antropométrica (pêso, altura, circunferência branquial, prega cutânea tricipital, prega cutânea subescapular, índice de Quetelet e circunferência muscular do braço) e bioquímica (proteínas e lipides) foi realizado em 120 indivíduos (93 masculinos e 27 do sexo feminino), de 17 a 72 anos de idade, moradores de área endêmica de malária (Humaitá -AM). de acordo com a história da doença (malária) eles foram divididos em 4 grupos: G1 - controle (n = 30), sem história de malária; G2 - controle (n = 40), com história de malária, mas sem manifestação de doença atual; G3 - doentes com Plasmodium vivax (n = 19) e G4 - doentes com Plasmodium faleiparum (n = 31). O diagnóstico de malária foi estabelecido por manifestações clínicas e confirmado laboratorialmente (gota espessa e esfregaço). No global as medidas antropométricas e bioquímicas discriminaram os grupos diferentemente. As medidas antropométricas do pêso, altura, reservas calóricas e estoque proteicos somáticos, apresentaram pouca sensibilidade, discriminado apenas os grupos extremos (Gl > G4). As medidas bioquímicas, no geral diferenciaram dois grandes grupos, os sadios e os doentes (G1+G2) e (G3+G4). Os doentes com Plasmodium falciparum (G4) foram os que se apresentaram em pior estado nutricional para a maioria das variáveis, sem entretanto, nenhuma variável individual que os discriminasse significativamente do G3. Estes dados permitem concluir que a malária resulta em desnutrição do hospedeiro, cuja gravidade está relacionada ao tipo e estágio da doença.

Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Swarm control designs applied to a micro-electro-mechanical gyroscope system (MEMS)

This paper analyzes the non-linear dynamics of a MEMS Gyroscope system, modeled with a proof mass constrained to move in a plane with two resonant modes, which are nominally orthogonal. The two modes are ideally coupled only by the rotation of the gyro about the plane's normal vector. We demonstrated that this model has an unstable behavior. Control problems consist of attempts to stabilize a system to an equilibrium point, a periodic orbit, or more general, about a given reference trajectory. We also developed a particle swarm optimization technique for reducing the oscillatory movement of the nonlinear system to a periodic orbit. © 2010 Springer-Verlag.

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.

Identificação e caracterização de pterígio utilizando floresta de caminhos ótimos e técnicas de otimização

Pós-graduação em Ciência da Computação - IBILCE

Particle swarm optimization aplicada ao planejamento da expansão de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

A influência do controlador gupfc no sistema elétrico de potência visando a estabilidade a pequenas perturbações

Pós-graduação em Engenharia Elétrica - FEIS