Aplicação do algoritmo Rough Sets em um Data Warehouse

This Project aims to develop methods for data classification in a Data Warehouse for decision-making purposes. We also have as another goal the reduction of an attribute set in a Data Warehouse, in which a given reduced set is capable of keeping the same properties of the original one. Once we achieve a reduced set, we have a smaller computational cost of processing, we are able to identify non-relevant attributes to certain kinds of situations, and finally we are also able to recognize patterns in the database that will help us to take decisions. In order to achieve these main objectives, it will be implemented the Rough Sets algorithm. We chose PostgreSQL as our data base management system due to its efficiency, consolidation and finally, it’s an open-source system (free distribution)

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.

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.

Vortex state in a mesoscopic flat disk with rough surface

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prolapse-free relativistic Gaussian basis sets for the superheavy elements up to Uuo (Z=118) and Lr (Z=103)

Prolapse-free basis sets suitable for four-component relativistic quantum chemical calculations are presented for the superheavy elements UP to (118)Uuo ((104)Rf, (105)Db, (106)Sg, (107)Bh, (108)Hs, (109)Mt, (110)Ds, (111)Rg, (112)Uub, (113)Uut, (114)Uuq, (115)Uup, (116)Uuh, (117)Uus, (118)Uuo) and Lr-103. These basis sets were optimized by minimizing the absolute values of the energy difference between the Dirac-Fock-Roothaan total energy and the corresponding numerical value at a milli-Hartree order of magnitude, resulting in a good balance between cost and accuracy. Parameters for generating exponents and new numerical data for some superheavy elements are also presented. (c) 2007 Elsevier B.V. All rights reserved.

A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Fuzzy Logic and Geostatistical Techniques for Spatialization of Soil Texture in Region with Rough Terrains

From the geotechnical standpoint, it is interesting to analyse the soil texture in regions with rough terrain due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this paper is to present a methodology that provides the soil texture spatialization by using Fuzzy logic and Geostatistic. The results were correlated with maps drawn specifically for the study area. The knowledge of the spatialization of soil properties, such as the texture, can be an important tool for land use planning in order to reduce the potential soil losses during rain seasons. (c) 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Spatial Statistics 2011

Argon-based atmospheric pressure plasma enhances early bone response to rough titanium surfaces

This study investigated the effect of an Argon-based atmospheric pressure plasma (APP) surface treatment operated chairside at atmospheric pressure conditions applied immediately prior to dental implant placement in a canine model. Surfaces investigated comprised: rough titanium surface (Ti) and rough titanium surface + Argon-based APP (Ti-Plasma). Surface energy was characterized by the Owens-Wendt-Rabel-Kaelble method and chemistry by X-ray photoelectron spectroscopy (XPS). Six adult beagles dogs received two plateau-root form implants (n = 1 each surface) in each radii, providing implants that remained 1 and 3 weeks in vivo. Histometric parameters assessed were bone-to-implant contact (BIC) and bone area fraction occupancy (BAFO). Statistical analysis was performed by Kruskall-Wallis (95% level of significance) and Dunn's post-hoc test. The XPS analysis showed peaks of Ti, C, and O for the Ti and Ti- Plasma surfaces. Both surfaces presented carbon primarily as hydrocarbon (C?C, C?H) with lower levels of oxidized carbon forms. The Ti-Plasma presented large increase in the Ti (+11%) and O (+16%) elements for the Ti- Plasma group along with a decrease of 23% in surface-adsorbed C content. At 1 week no difference was found in histometric parameters between groups. At 3 weeks significantly higher BIC (>300%) and mean BAFO (>30%) were observed for Ti-Plasma treated surfaces. From a morphologic standpoint, improved interaction between connective tissue was observed at 1 week, likely leading to more uniform and higher bone formation at 3 weeks for the Ti-Plasma treated implants was observed. (C) 2012 Wiley Periodicals, Inc. J Biomed Mater Res Part A 2012.

Tests of one Brazilian facial reconstruction method using three soft tissue depth sets and familiar assessors

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On the spectrum of stochastic perturbations of the shift and Julia sets

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Boundary of the rauzy fractal sets in R x C generated by P(x)=x(4)-x(3)-x(2)-x-1

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Soliton parameters and chaotic sets

In this work we apply a nonperturbative approach to analyze soliton bifurcation ill the presence of surface tension, which is a reformulation of standard methods based on the reversibility properties of the system. The hypothesis is non-restrictive and the results can be extended to a much wider variety of systems. The usual idea of tracking intersections of unstable manifolds with some invariant set is again used, but reversibility plays an important role establishing in a geometrical point of view some kind of symmetry which, in a classical way, is unknown or nonexistent. Using a computer program we determine soliton solutions and also their bifurcations ill the space of parameters giving a picture of the chaotic structural distribution to phase and amplitude shift phenomena. (C) 2009 Published by Elsevier Ltd.