Identificação e controle de sistemas dinâmicos utilizando redes wavelets

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimization of annatto (Bixa orellana L.) drying in fixed bed

ABSTRACT: The drying of annatto seeds (Bixa orellana L.), red piave cultivate, was studied in a fixed bed dryer. The best conditions were estimated to minimize the loss of coloring and to obtain final moisture of the seeds in appropriate levels to its conservation and maintenance of quality. The quantification of the influence of entrance variables in the final contents of bixin and moisture seeds and the identification of the optimal point was performed through the techniques of factorial design, response surfaces methodology, canonical analysis and desirability function. It was verified that the final moisture of the seeds may be estimated by a second-order polynomial model and that the final content of bixin is only significantly influenced by the time of drying being described properly by a linear model, for the seeds used in this study.

Bio-Optical Properties of the Inner Continental Shelf off Santos Estuarine System, Southeastern Brazil, and their Implications for Ocean Color Algorithm Performance

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

A smooth evolutionary structural optimization procedure applied to plane stress problem

Topological optimization problems based on stress criteria are solved using two techniques in this paper. The first technique is the conventional Evolutionary Structural Optimization (ESO), which is known as hard kill, because the material is discretely removed; that is, the elements under low stress that are being inefficiently utilized have their constitutive matrix has suddenly reduced. The second technique, proposed in a previous paper, is a variant of the ESO procedure and is called Smooth ESO (SESO), which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it no longer influences the structure; its removal is thus performed smoothly. This procedure is known as "soft-kill"; that is, not all of the elements removed from the structure using the ESO criterion are discarded. Thus, the elements returned to the structure must provide a good conditioning system that will be resolved in the next iteration, and they are considered important to the optimization process. To evaluate elasticity problems numerically, finite element analysis is applied, but instead of using conventional quadrilateral finite elements, a plane-stress triangular finite element was implemented with high-order modes for solving complex geometric problems. A number of typical examples demonstrate that the proposed approach is effective for solving problems of bi-dimensional elasticity. (C) 2014 Elsevier Ltd. All rights reserved.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Harmony Search applied for Support Vector Machines Training Optimization

Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.

Espectrometria de massa para análise isotópica dos bio-elementos

Mass spectrometry is an analytical technique widely used in several areas of academic research. It allows the knowledge of the information about the micro-world of atoms leading to significant advances in science today. The analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen and sulfur, also known as bio-elements, shows itself as a major area of interest in using the proposed method. The development of techniques and equipment coupled with mass spectrometry promises to deliver even greater progress in this field, in particular, for the biological sciences and related areas. The pyrolytic method in reduction of organic compounds at high temperatures provides simultaneous isotopic analysis of bio-elements H and O, by the gases released, H2 and CO after the pyrolitic process, significantly reducing analysis time and the amount of material to sample. This paper presents a review of mass spectrometry with its basic principles of operation, and pyrolytic method for reducing compounds at temperatures above 1400 ° C for isotopic analysis of bio-elements

Ethanol production by Zymomonas mobilis during sucrose fermentation: Optimization of culture conditions using factorial design

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

Construction of pediatric homogeneous phantoms for optimization of chest and skull radiographs

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Coupling remote sensing bio-optical and three-dimensional hydrodynamic modeling to study the phytoplankton dynamics in a tropical hydroelectric reservoir

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

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

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

First insight on the impact of an osteoblastic layer on the bio-tribocorrosion performance of Ti6Al4V hip implants

In uncemented Ti6Al4V hip implants, the bone-stem interface is subjected to cyclic loading motion driven by the daily activities of the patients, which may lead to the complete failure of the implant in the long term. It may also compromise the proliferation and differentiation processes of osteoblastic cells (bone-forming cells). The main objective of this work is to approach for the first time the role of these organic materials on the bio-tribocorrosion mechanisms of cultured Ti6Al4V alloys. The colonized materials with MG63 osteoblastic-like cells were characterized through cell viability/proliferation and enzymatic activity. Tribocorrosion tests were performed under a reciprocating sliding configuration and low contact pressure. Electrochemical techniques were used to measure the corrosion kinetics of the system, under free potential conditions. All tests were performed at a controlled atmosphere. The morphology and topography of the wear scar were evaluated. The results showed that the presence of an osteoblastic cell layer on the implant surface significantly influences the tribocorrosion behavior of Ti6Al4V alloy. It was concluded that the cellular material was able to form an extra protective layer that inhibits further wear degradation of the alloy and decreases its corrosion tendency.

Bacterial diversity from environmental sample applied to bio-hydrogen production

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

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Graded meshes in bio-thermal problems with transmission-line modeling method

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