Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

Sistema inteligente para detecção de manchas de óleo na superfície marinha através de imagens de SAR

Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents

Modelagem de Superfícies Seletivas de Freqüência e Antenas de Microfita utilizando Redes Neurais Artificiais

This work has as main objective the application of Artificial Neural Networks, ANN, in the resolution of problems of RF /microwaves devices, as for example the prediction of the frequency response of some structures in an interest region. Artificial Neural Networks, are presently a alternative to the current methods of analysis of microwaves structures. Therefore they are capable to learn, and the more important to generalize the acquired knowledge, from any type of available data, keeping the precision of the original technique and adding the low computational cost of the neural models. For this reason, artificial neural networks are being increasily used for modeling microwaves devices. Multilayer Perceptron and Radial Base Functions models are used in this work. The advantages/disadvantages of these models and the referring algorithms of training of each one are described. Microwave planar devices, as Frequency Selective Surfaces and microstrip antennas, are in evidence due the increasing necessities of filtering and separation of eletromagnetic waves and the miniaturization of RF devices. Therefore, it is of fundamental importance the study of the structural parameters of these devices in a fast and accurate way. The presented results, show to the capacities of the neural techniques for modeling both Frequency Selective Surfaces and antennas

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

Previsão do módulo de elasticidade transversal de compósitos unidirecionais através de redes neurais mistas

The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from different networks were developed, one with only two inputs, one with three inputs and the third with mixed architecture combining an ANN with a model developed by Halpin-Tsai. After algorithm training, the results demonstrate that the use of ANNs is quite promising, given that when they were compared with those of the Halpín-Tsai mathematical model, higher correlation coefficient values and lower root mean square values were observed

As quatro operações básicas: uma compreensão dos procedimentos algorítmicos

The current research had as main objective to analyze the possibility of knowledge elaboration/re-elaboration about ideas and algorithmic procedures related to basic operations by pupils of the 6th degree fundamental teaching in a significant learning process. This way the study had as basis a methodological intervention developed in a 6th degree class of a Fundamental Teaching Municipal School in the city of João Pessoa, PB. The research had as central steps the application of pre-tests (1 and 2); the execution of semi-structured interviews with the pupils involved in the theme deep studies; the elaboration and development of teaching activities, having as referential the significant learning and the application of a pre-test. The data collected in the pre-tests (1 and 2) showed a low level of the pupils comprehension about the contents related to the four operations. The answers to the post-test questions were analyzed mainly from the qualitative point of view based on the mathematic concepts comprehension theory proposed by Skemp (1980) having as complementary subsidy data collected through interviews. The analysis of the results obtained in the post-test showed that the major part of pupils reached a relational comprehension about the ideas and algorithmic procedures related to addition, subtraction, multiplication, and division. Such results showed us that the application of a teaching methodology that privileges the content comprehension, considering the pupils previous knowledge and the reflection about the action along the activities proposed, made possible the elaboration or re-elaboration of knowledge by pupils regarding to contents adopted as theme for our research

Ondaletas e movimento browniano fracionário: aplicação à caracterização de poços de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Propriedades críticas do modelo z(4) em sistemas bi e tridimensionais

A real space renormalization group method is used to investigate the criticality (phase diagrams, critical expoentes and universality classes) of Z(4) model in two and three dimensions. The values of the interaction parameters are chosen in such a way as to cover the complete phase diagrams of the model, which presents the following phases: (i) Paramagnetic (P); (ii) Ferromagnetic (F); (iii) Antiferromagnetic (AF); (iv) Intermediate Ferromagnetic (IF) and Intermediate Antiferromagnetic (IAF). In the hierarquical lattices, generated by renormalization the phase diagrams are exact. It is also possible to obtain approximated results for square and simple cubic lattices. In the bidimensional case a self-dual lattice is used and the resulting phase diagram reproduces all the exact results known for the square lattice. The Migdal-Kadanoff transformation is applied to the three dimensional case and the additional phases previously suggested by Ditzian et al, are not found

Modelo de Ashkin-Teller anisotrópico: uma abordagem por grupo de renormalização

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Propagação de danos para o modelo Ising em uma rede quadrada totalmente frustrada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Evolução do reparo por excisão de nucleotídeo

The phylogeny is one of the main activities of the modern taxonomists and a way to reconstruct the history of the life through comparative analysis of these sequences stored in their genomes aimed find any justification for the origin or evolution of them. Among the sequences with a high level of conservation are the genes of repair because it is important for the conservation and maintenance of genetic stability. Hence, variations in repair genes, as the genes of the nucleotide excision repair (NER), may indicate a possible gene transfer between species. This study aimed to examine the evolutionary history of the components of the NER. For this, sequences of UVRA, UVRB, UVRC and XPB were obtained from GenBank by Blast-p, considering 10-15 as cutoff to create a database. Phylogenetic studies were done using algorithms in PAUP programs, BAYES and PHYLIP package. Phylogenetic trees were build with protein sequences and with sequences of 16S ribosomal RNA for comparative analysis by the methods of parsimony, likelihood and Bayesian. The XPB tree shows that archaeal´s XPB helicases are similar to eukaryotic helicases. According to this data, we infer that the eukaryote nucleotide excision repair system had appeared in Archaea. At UVRA, UVRB and UVRC trees was found a monophyletic group formed by three species of epsilonproteobacterias class, three species of mollicutes class and archaeabacterias of Methanobacteria and Methanococci classes. This information is supported by a tree obtained with the proteins, UVRA, UVRB and UVRC concatenated. Thus, although there are arguments in the literature defending the horizontal transfer of the system uvrABC of bacteria to archaeabacterias, the analysis made in this study suggests that occurred a vertical transfer, from archaeabacteria, of both the NER genes: uvrABC and XPs. According the parsimony, this is the best way because of the occurrence of monophyletic groups, the time of divergence of classes and number of archaeabacterias species with uvrABC system

Uso de técnicas de Geoprocessamento e Sensoriamento Remoto no levantamento e integração de dados necessários a gestão ambiental dos campos de extração de óleo e gás do Canto do Amaro e Alto da Pedra no município de Mossoró - RN

The objective of this work is to identify, to chart and to explain the evolution of the soil occupation and the envirionment vulnerability of the areas of Canto do Amaro and Alto da Pedra, in the city of Mossoró-RN, having as base analyzes it multiweather of images of orbital remote sensors, the accomplishment of extensive integrated works of field to a Geographic Information System (GIS). With the use of inserted techniques of it analyzes space inserted in a (GIS), and related with the interpretation and analyzes of products that comes from the Remote Sensoriamento (RS.), make possible resulted significant to reach the objectives of this works. Having as support for the management of the information, the data set gotten of the most varied sources and stored in digital environment, it comes to constitute the geographic data base of this research. The previous knowledge of the spectral behavior of the natural or artificial targets, and the use of algorithms of Processing of Digital images (DIP), it facilitates the interpretation task sufficiently and searchs of new information on the spectral level. Use as background these data, was generated a varied thematic cartography was: Maps of Geology, Geomorfológicals Units soils, Vegetation and Use and Occupation of the soil. The crossing in environment SIG, of the above-mentioned maps, generated the maps of Natural and Vulnerability envirionmental of the petroliferous fields of I Canto do Amaro and Alto da Pedra-RN, working in an ambient centered in the management of waters and solid residuos, as well as the analysis of the spatial data, making possible then a more complex analysis of the studied area

Estudo da transformada rápida wavelet e sua conexão com banco de filtros

In this work we presented an exhibition of the mathematical theory of orthogonal compact support wavelets in the context of multiresoluction analysis. These are particularly attractive wavelets because they lead to a stable and very efficient algorithm, that is Fast Transform Wavelet (FWT). One of our objectives is to develop efficient algorithms for calculating the coefficients wavelet (FWT) through the pyramid algorithm of Mallat and to discuss his connection with filters Banks. We also studied the concept of multiresoluction analysis, that is the context in that wavelets can be understood and built naturally, taking an important step in the change from the Mathematical universe (Continuous Domain) for the Universe of the representation (Discret Domain)

Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Determinação de parâmetros (sólidos solúveis, pH e acidez titulável) em ameixas intactas usando espectroscopia no infravermelho próximo e seleção de comprimento de onda

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive method to determine the soluble solid content (SSC), pH and titratable acidity of intact plums. Samples of plum with a total solids content ranging from 5.7 to 15%, pH from 2.72 to 3.84 and titratable acidity from 0.88 a 3.6% were collected from supermarkets in Natal-Brazil, and NIR spectra were acquired in the 714 2500 nm range. A comparison of several multivariate calibration techniques with respect to several pre-processing data and variable selection algorithms, such as interval Partial Least Squares (iPLS), genetic algorithm (GA), successive projections algorithm (SPA) and ordered predictors selection (OPS), was performed. Validation models for SSC, pH and titratable acidity had a coefficient of correlation (R) of 0.95 0.90 and 0.80, as well as a root mean square error of prediction (RMSEP) of 0.45ºBrix, 0.07 and 0.40%, respectively. From these results, it can be concluded that NIR spectroscopy can be used as a non-destructive alternative for measuring the SSC, pH and titratable acidity in plums