Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-05-26T14:18:10Z No. of bitstreams: 1 mariannaresendeoliveira.pdf: 508490 bytes, checksum: e85c33aa024977254550dda6bfa1f317 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-05-26T15:12:24Z (GMT) No. of bitstreams: 1 mariannaresendeoliveira.pdf: 508490 bytes, checksum: e85c33aa024977254550dda6bfa1f317 (MD5) / Made available in DSpace on 2017-05-26T15:12:24Z (GMT). No. of bitstreams: 1 mariannaresendeoliveira.pdf: 508490 bytes, checksum: e85c33aa024977254550dda6bfa1f317 (MD5) Previous issue date: 2014-03-06 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho estudaremos o problema de ondas com amortecimento friccional. Consideraremos o caso em que a dissipação provocada pelo atrito, representado por αut (onde α é uma constante real positiva), atua em todo o domínio. Estudaremos a existência e unicidade da solução via Método de Galerkin e via Teoria dos Semigrupos. Para o estudo da estabilidade de solução empregaremos o Método de Energia e a técnica de Semigrupos aplicada a sistemas dissipativos. Ao final do trabalho vamos comparar os métodos utilizados para garantir a existência, unicidade e comportamento assintótico da solução. Usaremos a notação usual dos espaços de Sobolev. / In this work we will study the problem of waves with frictional damping. We will consider the case in which dissipation caused by the friction, represented by αut (where α is a positive real constant), operates throughout all the domain. We will study the existence and uniqueness of the solution through the Galerkin Method and the Semigroups Theory. To study the stability of the solution we will employ the Energy Method and the Semigroups technique applied to dissipative systems. At the end of the paper we will compare the methods used to ensure the existence, uniqueness and asymptotic behavior of the solution. We will use the usual notation of Sobolev spaces.

The exponential power (EP) distribution is a very important distribution that was used by survival analysis and related with asymmetrical EP distribution. Many researchers have discussed statistical inference about the parameters in EP distribution using i.i.d random samples. However, sometimes available data might contain only record values, or it is more convenient for researchers to collect record values. We aim to resolve this problem. We estimated two parameters of the EP distribution by MLE using upper record values. According to simulation study, we used the Bias and MSE of the estimators for studying the efficiency of the proposed estimation method. Then, we discussed the prediction on the next upper record value by known upper record values. The study concluded that MLEs of EP distribution parameters by upper record values has satisfactory performance. Also, prediction of the next upper record value performed well

There are some existing commonly used goodness-of-fit tests, such as the Kolmogorov-Smirnov test, the Cramer-Von Mises test, and the Anderson-Darling test. In addition, a new goodness-of-fit test named G test was proposed by Chen and Ye (2009). The purpose of this thesis is to compare the performance of some goodness-of-fit tests by comparing their power. A goodness-of-fit test is usually used when judging whether or not the underlying population distribution differs from a specific distribution. This research focus on testing whether the underlying population distribution is an exponential distribution. To conduct statistical simulation, SAS/IML is used in this research. Some alternative distributions such as the triangle distribution, V-shaped triangle distribution are used. By applying Monte Carlo simulation, it can be concluded that the performance of the Kolmogorov-Smirnov test is better than the G test in many cases, while the G test performs well in some cases.

Wireless Sensor Networks (WSNs) have enticed a strong attention in the research community due to the broad range of applications and services they support. WSNs are composed of intelligent sensor nodes that have the capabilities to monitor different types of environmental phenomena or critical activities. Sensor nodes operate under stringent requirements of scarce power resources, limited storage capacities, limited processing capabilities, and hostile environmental surroundings. However, conserving sensor nodes’ power resources is the top priority requirement in the design of a WSN as it has a direct impact on its lifetime. The IEEE 802.15.4 standard defines a set of specifications for both the PHY layer and the MAC sub-layer that abide by the distinguished requirements of WSNs. The standard’s MAC protocol employs an intelligent backoff algorithm, called the Binary Exponent Backoff (BEB), that minimizes the drainage of power in these networks. In this thesis we present an in-depth study of the IEEE 802.15.4 MAC protocol to highlight both its strong and weak aspects. We show that we have enticing opportunities to improve the performance of this protocol in the context of WSNs. We propose three new backoff algorithms, namely, the Standby-BEB (SB-BEB), the Adaptive Backoff Algorithm (ABA), and the Priority-Based BEB (PB-BEB), to replace the standard BEB. The main contribution of the thesis is that it develops a new design concept that drives the design of efficient backoff algorithms for the IEEE 802.15.4-based WSNs. The concept dictates that controlling the algorithms parameters probabilistically has a direct impact on enhancing the backoff algorithm’s performance. We provide detailed discrete-time Markov-based models (for AB-BEB and ABA) and extensive simulation studies (for the three algorithms) to prove the superiority of our new algorithms over the standard BEB.

[pt] Esta dissertação teve como objetivo o desenvolvimento e a implantação de uma metodologia de previsão de vendas e dimensionamento de lotes de encomenda num distribuidor atacadista de produtos químicos. Para tanto, abordou técnicas quantitativas de previsão de demanda de curto prazo e medidas de variância dos erros de previsão a fim de suportar decisões empresariais na aplicação da metodologia, capazes de projetar padrões passados num cenário futuro. A aplicação da metodologia possibilitará à empresa a formalização de um processo atualmente subjetivo, outorgando maior precisão na previsão de vendas, redução de custos com estoque e uma base mais concreta para alocação de recursos financeiros. / [en] This thesis has as objective the developing and implantation of a methodology for forecasting sales and design of batch ordering in a wholesale distributor of chemical products. For this purpose, it approached short term quantitative techniques of demand forecast and measures of variance of forecast errors in order to support business decisions on the application of the methodology, able to design past patterns on a future scenario. The application of the methodology will enable the company the formalization of a process currently subjective, granting a greater accuracy in forecasting sales, reduction in the inventory costs and a more concrete basis for resource allocation.

The statistical inference about the reliability parameter R involving independent gamma stress and exponential strength is considered. Assuming the shape parameter of gamma is a known arbitrary real number and the scale parameters of gamma and exponential are unknown, the UMVUE and MLE of R are obtained. A pivot is proposed. Some inference about R derived from this pivot is presented. It will be shown that the pivot can be used for testing hypothesis and constructing condence interval. A procedure of constructing the condence interval for R is derived. The performances of the UMVUE and MLE are compared numerically based on extensive Monte Carlo simulation. Simulation studies indicate that the performance of the two estimators is about the same. The MLE is preferred because of the simplicity of its computation.

How are arms export choices made within a state? In this dissertation I use a foreign policy analysis framework to examine this question. I focus on examining each of the three primary levels of analysis in international relations as it relates to the main question. I begin with a typical international relations level and examine the characteristics of the two states that dominate the world arms trade: The United States and Russia. I then examine the full network of relations among all states in the international system that are involved in the sale or purchase of arms. To do this I use an Exponential Random Graph Model (ERGM) to examine these relations, which I derived from data on arms sales from the Stockholm International Peace Research Institute (SIPRI). I examine the arms sales in each decade from 1950 through 2010. In order to answer the question of how arms decisions are made within the state, I focus my inquiry on the United States and Russia. It is these states that have the practical capability to use arms transfers as a foreign policy tool. I examine the foreign policy making mechanisms in each of these states to determine how arms transfers can be used as a foreign policy tool. I examine and the bureaucratic institutions within each state and come up with a state ordering preference for how arms decisions are evaluated in each state. Finally, I use case studies to examine arms relations between the both the U.S. and Russia and three other states in each case. The other states were selected based on the pattern of sales between the two countries. I examine these sales to determine the impact of bureaucratic maneuvering and interest politics on the decision-making process within Russia and the United States. I find in my network analysis that the traditional measures of state power - military spending, regime type, and military alliances - do not account for the overall structure of the arms sale network. The most important factors in the formation of the arms sale network in each of the six decades that I study are specific configurations of triadic relations that suggest a continued hierarchy in the arms sale network. I find in my case study chapters that a simple model of state interest as a decision-making rule accounts for the decisions made by the different bureaucratic actors in the U.S. Russian arms sales are driven by a state imperative to increase Russia's market share, and there is high-level involvement in making different arms deals with other countries.

In 2018, Raul Matsushita, Donald Pianto, Bernardo B. De Andrade, Andre Can§ado & Sergio Da Silva published a paper titled âTouchard distributionâ, which presented a model that is a two-parameter extension of the Poisson distribution. This model has its normalizing constant related to the Touchard polynomials, hence the name of this model. This diploma thesis is concerned with the properties of the Touchard distribution for which delta is known. Two asymptotic tests based on two different statistics were carried out for comparison in a Touchard model with two independent samples, supported by simulations in R.

Embodied agents, be they animals or robots, acquire information about the world through their senses. Embodied agents, however, do not simply lose this information once it passes by, but rather process and store it for future use. The most general theory of how an agent can combine stored knowledge with new observations is Bayesian inference. In this dissertation I present a theory of how embodied agents can learn to implement Bayesian inference with neural networks. By neural network I mean both artificial and biological neural networks, and in my dissertation I address both kinds. On one hand, I develop theory for implementing Bayesian inference in deep generative models, and I show how to train multilayer perceptrons to compute approximate predictions for Bayesian filtering. On the other hand, I show that several models in computational neuroscience are special cases of the general theory that I develop in this dissertation, and I use this theory to model and explain several phenomena in neuroscience. The key contributions of this dissertation can be summarized as follows: - I develop a class of graphical model called nth-order harmoniums. An nth-order harmonium is an n-tuple of random variables, where the conditional distribution of each variable given all the others is always an element of the same exponential family. I show that harmoniums have a recursive structure which allows them to be analyzed at coarser and finer levels of detail. - I define a class of harmoniums called rectified harmoniums, which are constrained to have priors which are conjugate to their posteriors. As a consequence of this, rectified harmoniums afford efficient sampling and learning. - I develop deep harmoniums, which are harmoniums which can be represented by hierarchical, undirected graphs. I develop the theory of rectification for deep harmoniums, and develop a novel algorithm for training deep generative models. - I show how to implement a variety of optimal and near-optimal Bayes filters by combining the solution to Bayes' rule provided by rectified harmoniums, with predictions computed by a recurrent neural network. I then show how to train a neural network to implement Bayesian filtering when the transition and emission distributions are unknown. - I show how some well-established models of neural activity are special cases of the theory I present in this dissertation, and how these models can be generalized with the theory of rectification. - I show how the theory that I present can model several neural phenomena including proprioception and gain-field modulation of tuning curves. - I introduce a library for the programming language Haskell, within which I have implemented all the simulations presented in this dissertation. This library uses concepts from Riemannian geometry to provide a rigorous and efficient environment for implementing complex numerical simulations. I also use the results presented in this dissertation to argue for the fundamental role of neural computation in embodied cognition. I argue, in other words, that before we will be able to build truly intelligent robots, we will need to truly understand biological brains.

The objective of this paper is to retrospectively evaluate forecast models for polling data, to be used prospectively for the Swedish general election in 2022. One of the simplest ways of forecasting an election result is through opinion polls, and using the latest observation as the forecast. This paper considers five different forecasting models on polling data which are evaluated based on different error measures and the results are compared to previous research done on the same topic. The data in this paper consists of time series data of party-preference polls from Statistics Sweden. When forecasting polling data, the naive forecasting model was the most accurate, but forecasting the election in 2018 resulted in the forecast combinations model being the most accurate. Finally, the models are used to make forecasts on the Swedish general election taking place in September of 2022.