Inferência lógica e os sistemas de derivação

Nestes slides são apresentados conceitos sobre a inferência lógica e os sistemas de derivação. Define o conceito de argumento válido, e demonstra que a verificação da validade de argumentos pode ser feita por meio de tabelas-verdade e pelo uso de regras de inferência.

Um sistema para a inferência de variáveis usando redes neurais artificiais

This work aims to obtain a low-cost virtual sensor to estimate the quality of LPG. For the acquisition of data from a distillation tower, software HYSYS ® was used to simulate chemical processes. These data will be used for training and validation of an Artificial Neural Network (ANN). This network will aim to estimate from available simulated variables such as temperature, pressure and discharge flow of a distillation tower, the mole fraction of pentane present in LPG. Thus, allowing a better control of product quality

Análise bayesiana na estimação de correlações genéticas entre escores visuais e características reprodutivas de bovinos Nelore utilizando modelos linear-limiar

Estimaram-se as correlações genéticas entre os escores visuais e as características reprodutivas, utilizando a estatística bayesiana sob modelo animal linear-limiar, em bovinos da raça Nelore. Foram estudadas características categóricas morfológicas, avaliadas visualmente aos oito, 15 e 22 meses de idade; e características contínuas de perímetro escrotal padronizado aos 365 e 450 dias de idade, além da idade ao primeiro parto. As estimativas de correlações genéticas foram de sentido favorável à seleção, apresentando magnitudes moderadas, sugerindo que a seleção de animais para um biótipo desejável pode levar a animais com maior fertilidade e precocidade sexual. As estimativas de correlação genética para o perímetro escrotal padronizado aos 450 dias e a idade ao primeiro parto com as características morfológicas avaliadas aos 22 meses de idade foram maiores do que as obtidas entre as características de escores visuais avaliadas aos oito e 15 meses de idade. A utilização de escores visuais como critério de seleção trará progresso genético também para as características reprodutivas.

Modelo de covariância bayesiana para seleção de protfólios de investimentos

The portfolio theory is a field of study devoted to investigate the decision-making by investors of resources. The purpose of this process is to reduce risk through diversification and thus guarantee a return. Nevertheless, the classical Mean-Variance has been criticized regarding its parameters and it is observed that the use of variance and covariance has sensitivity to the market and parameter estimation. In order to reduce the estimation errors, the Bayesian models have more flexibility in modeling, capable of insert quantitative and qualitative parameters about the behavior of the market as a way of reducing errors. Observing this, the present study aimed to formulate a new matrix model using Bayesian inference as a way to replace the covariance in the MV model, called MCB - Covariance Bayesian model. To evaluate the model, some hypotheses were analyzed using the method ex post facto and sensitivity analysis. The benchmarks used as reference were: (1) the classical Mean Variance, (2) the Bovespa index's market, and (3) in addition 94 investment funds. The returns earned during the period May 2002 to December 2009 demonstrated the superiority of MCB in relation to the classical model MV and the Bovespa Index, but taking a little more diversifiable risk that the MV. The robust analysis of the model, considering the time horizon, found returns near the Bovespa index, taking less risk than the market. Finally, in relation to the index of Mao, the model showed satisfactory, return and risk, especially in longer maturities. Some considerations were made, as well as suggestions for further work

Sistema Híbrido de Inferência Baseado em Análise de Componentes Principais e Redes Neurais Artificiais Aplicado a Plantas de Processamento de Gás Natural

Nowadays, where the market competition requires products with better quality and a constant search for cost savings and a better use of raw materials, the research for more efficient control strategies becomes vital. In Natural Gas Processin Units (NGPUs), as in the most chemical processes, the quality control is accomplished through their products composition. However, the chemical composition analysis has a long measurement time, even when performed by instruments such as gas chromatographs. This fact hinders the development of control strategies to provide a better process yield. The natural gas processing is one of the most important activities in the petroleum industry. The main economic product of a NGPU is the liquefied petroleum gas (LPG). The LPG is ideally composed by propane and butane, however, in practice, its composition has some contaminants, such as ethane and pentane. In this work is proposed an inferential system using neural networks to estimate the ethane and pentane mole fractions in LPG and the propane mole fraction in residual gas. The goal is to provide the values of these estimated variables in every minute using a single multilayer neural network, making it possibly to apply inferential control techniques in order to monitor the LPG quality and to reduce the propane loss in the process. To develop this work a NGPU was simulated in HYSYS R software, composed by two distillation collumns: deethanizer and debutanizer. The inference is performed through the process variables of the PID controllers present in the instrumentation of these columns. To reduce the complexity of the inferential neural network is used the statistical technique of principal component analysis to decrease the number of network inputs, thus forming a hybrid inferential system. It is also proposed in this work a simple strategy to correct the inferential system in real-time, based on measurements of the chromatographs which may exist in process under study

Inferência do ponto de orvalho em amostras de gás natural processado

This dissertation aims to assess the representativeness of the manual chilled mirror analyzer (model II Chanscope 13-1200-CN-2) used for the determination of condensed hydrocarbons of natural gas compared to the indirect methods, based on thermodynamic models equation of state. Additionally, it has been implemented in this study a model for calculating the dew point of natural gas. The proposed model is a modification of the equation of state of Peng-Robinson admits that the groups contribution as a strategy to calculate the binary interaction parameters kij (T) temperature dependence. Experimental data of the work of Brown et al. (2007) were used to compare the responses of the dew point of natural gas with thermodynamic models contained in the UniSim process simulator and the methodology implemented in this study. Then two natural gas compositions were studied, the first being a standard gas mixture gravimetrically synthesized and, second, a mixture of processed natural gas. These experimental data were also compared with the results presented by UniSim process simulator and the thermodynamic model implemented. However, data from the manual analysis results indicated significant differences in temperature, these differences were attributed to the formation of dew point of water, as we observed the appearance of moisture on the mirror surface cooling equipment

Inferência filogenética revela a complexa etiologia das manchas areolada e foliar em seringueira e em outras espécies cultivadas na Amazônia

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

Atributos polarimétricos de imagem radar na inferência de parâmetros morfológicos de macrófitas

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

A inferência causal na filosofia moral de Hume.

Starting from the idea that the result of the Humean analysis of causal inferences must be applied coherently to the remaining part of his work, including its moral theory, the present master thesis aims at investigating whether Hume´s moral philosophy is essentially based on feeling, or whether this would not be rather essentially a consequence of our causal inferences in human actions and deliberations. The main idea consists in showing that our moral inferences, to the extent that they are for Hume empirical , depend on our belief in a connexion between something which has been previously observed and something which is not being observed ( but that it is expected to occur or to be observed in the future). Thus, this very belief must base our moral inferences concerning the actions and deliberations of the individuals. Therefore, must e o ipso induce us to associate actions and behaviors, as well as character and moral claims of men to certain moral feelings. Accordingly, the thesis is unfolded in three chapters. In the first chapter Hume´s theory of the perception is reported as essential part of the explanation or the principles that bind ideas in our mind and constitute our inferences. In the second chapter, the Humean analysis of causal inferences is presented and the way they contribute in the formation of our moral inferences is explained. In the third and last chapter, the formation of our moral inferences and the real contribution of the doctrine of freedom and necessity for the examination or our actions are analysed and discussed.

Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura

In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates