O planejamento de curto prazo em minas a céu aberto exige a definição de poligonais, que representam os sucessivos avanços de lavra. As poligonais, tradicionalmente, são desenhadas em um processo laborioso na tentativa de delinear como minério em qualidade e quantidade de acordo com os limites determinados. O minério delimitado deve apresentar a menor variabilidade em qualidade possível, com o objetivo de maximizar a recuperação na usina de processamento. Essa dissertação visa desenvolver um fluxo do trabalho para definir poligonais de curto prazo de forma automática, além disso, sequenciar todos os blocos de minério de cada polígono de modo a definir uma sequência interconectada lavrável de poligonais. O fluxo do trabalho foi aplicada à incerteza de teores, obtida através de simulações estocásticas. Algoritmos genéticos foram desenvolvidos em linguagem de programação Python e implementados na forma de plug-in no software geoestatístico Ar2GeMS. Múltiplas iterações são criadas para cada avanço individual, gerando regiões (ou poligonais). Então, a região que apresenta menor variabilidade de teores é selecionada. A distribuição de probabilidade dos teores dos blocos em cada avanço é comparada com a distribuição global de teores, calculada a partir de todos os blocos do corpo de minério. Os resultados mostraram que os teores dos blocos abrangidos pelas poligonais criadas dessa forma apresentam teores similares à distribuição de referência, permitindo o sequenciamento de lavra com distribuição de teores mais próximo possível da distribuição global. Modelos equiprováveis permitem avaliar a incerteza associada à solução proposta. / Open-pit short-term planning requieres the definition of polygons identifying the successive mining advances. These polygons are drawn in a labour intensive task attempting to delineate ore with the quantity and quality within established ranges. The ore delineated by the polygons should have the least possible quality variability among them, helping in maximizing ore recovery at the processing plant. This thesis aims at developíng a workflow for drawing short-term polygons automatically, sequencing all ore blocks within each polygon and leading to a mineable and connected sequence of polygons. This workflow is also tested under grade uncertainty obtained through multiple syochastic simulated models. For this, genetics algorithms were developed in Python programming language and pluged in Ar2GeMS geostatistical software. Multiple iterations were generated for each of the individual advances, generating regions or polygons, and selecting the regions of lower grade variability. The blocks probability distribution within each advance were compared to the global distribution, including all blocks within the ore body. Results show that the polygons generated are comprised by block grades similar to the ones from the reference distribution, leading to mining sequence as close as possible to the global maintaining a quasi-satationarity. Equally probable models provide the means to access the uncertainy in the solution provided.

O estudo de variaÃÃo de Ãrea de preservaÃÃo permanente do reservatÃrio OrÃs utilizou o software ArcGis 10.2 para a modelagem computacional, a delimitaÃÃo foi realizada conforme os referenciais legais da atual legislaÃÃo ambiental (Lei 12.651/2012) e da anterior (Lei 4.771/65) comparativamente, quanto a referÃncia altimÃtrica estabelecida pela atual legislaÃÃo, utilizou-se a cota mÃxima maximorum original do projeto de 1960, e uma cota mÃxima maximorum modelada estatisticamente a partir de dados pluviomÃtricos atuais, quanto a referÃncia altimÃtrica estabelecida pela legislaÃÃo anterior modelou-se um Buffer de 100 metros medidos a partir da cota mÃxima operativa normal, assim comparando-se a restritividade ou a permissividade da legislaÃÃo em vigor. A modelagem de atualizaÃÃo de cota mÃxima maximorum para determinaÃÃo da Ãrea de preservaÃÃo permanente do reservatÃrio OrÃs se deu por meio de processos estatÃsticos, utilizando distribuiÃÃes de probabilidade, dentre as quais oito tipos foram selecionadas: Weibull 3 ParÃmetros, Menores Valores Extremos, Weibull, LogÃstica, Normal, Gamma, LogNormal e Maiores Valores Extremos. O desempenho destas foram avaliados pelo nÃvel de confianÃa, este estabelecido em 95% e quanto a aderÃncia pelo mÃtodo de Anderson Darling, destacando-se a distribuiÃÃo do tipo Weibull 3 ParÃmetros, a qual obteve o melhor desempenho global, modelando uma cota mÃxima maximorum de 208 metros, um metro a cima da estabelecida pelo projeto original. Estabelecido os referenciais, iniciou-se a modelagem computacional das APPâs, identificou-se que a atual legislaÃÃo, referenciada pela cota mÃxima maximorum do projeto original do DNOCS, Ã menos restritiva do que a legislaÃÃo anterior, prever uma Ãrea 26% menor do que a delimitada pela legislaÃÃo anterior, promovendo alteraÃÃes quanto a definiÃÃo territorial dos municÃpios contemplados pelo reservatÃrio. Assim, foram observados ganhos de APP em alguns municÃpios e em outros, perda, processo este nÃo ocorrendo de forma homogÃnea. / The study of ranging permanent preservation areas of OrÃs reservoir, used the ArcGIS 10.2 software for computer modeling, the delimitation was carried out according to legal references of current environmental legislation, and the previous legislation, using altimetry reference established by current legislation, was used two altimetric elevations, the maximum maximorum quota of original project 1960, and a maximum maximorum quota statistically modeled from current rainfall data, using altimetry reference established by previous legislation was modeled one buffer of 100 meters measured from the maximum normal operating height, so it was possible to compare the restrictiveness or permissiveness of the legislation. Modeling maximum maximorum quota update to determine the area of permanent preservation of OrÃs reservoir was through statistical process using probability distributions, among which eight types were selected: Weibull three parameters, Minor Values Extremes, Weibull, Logistics, Normal, Gamma, lognormal and Largest Values Extremes. The performance of these distributions were evaluated by confidence level, this posted at 95% and the adherence by Anderson Darling method, highlighting the type distribution Weibull three parameters, which obtained the best overall performance, modeling a maximum maximorum quota of 208 meters, one meter above the established by the original project. Established the demarcation reference, began the computational modeling of the APPs, it was identified that the current legislation by reference to the maximum maximorum quota of the original project of DNOCS, is less restrictive than the previous legislation establishes an area 26% smaller that established by previous legislation, promoting changes as the territorial definition of the municipalities covered by the reservoir. Thus, APP gains were observed in some municipalities and other municipalities was observed loss of APP, the process does not occur homogeneously.

Made available in DSpace on 2017-07-10T18:56:25Z (GMT). No. of bitstreams: 1 Wagner Alessandro Pansera.pdf: 570958 bytes, checksum: 6984d964a2c6069ce6ee03e50131b7a1 (MD5) Previous issue date: 2010-02-01 / The rainfall is the only way of entry of water in river basins, therefore the knowledge of their spatial and temporal behavior as well as their frequency is of vital importance for the planning, operation and design of hydraulic works. To this end, probabilistic models are built based on observations made in the place of interest in order to obtain future risks. It often happens that a network of hydrological monitoring data does not show the place of interest is necessary to use procedures that allow the transport of this information. Hydrological regionalization is a statistical procedure that allows, within a homogeneous region, estimate probabilities of occurrence, by data from neighboring stations, in site of interest. The problem is how to define homogeneous region. The solution proposed in this paper was the use of multivariate clustering methods, k-means and hierarquical cluster, validated by quality index. The clusters were then subjected to measures of discordancy and heterogeneity to evaluate homogeneity of groups. The clustering methodology that obtained the best performance was a hybrid approach between k-means and ward, getting errors of at most 10% in the estimation of quantiles regional dimensionless. / O conhecimento do comportamento espacial e temporal da precipitação pluvial e sua frequência são vitais para o planejamento, operação e dimensionamento de obras hidráulicas. Para este fim, são construídos modelos probabilísticos baseados nas observações efetuadas nos locais de interesse com a finalidade de obter os riscos de ocorrência futura. No entanto, uma rede de monitoramento hidrológico não apresenta dados de todos os locais de interesse, sendo necessário recorrer a procedimentos que possibilitem o transporte desta informação. A regionalização hidrológica é um procedimento estatístico que permite, dentro de uma região homogênea, estimar probabilidades de ocorrência, pelos dados das estações vizinhas, no local de interesse. O problema está em como determinar uma região homogênea. A solução proposta neste trabalho foi a utilização de metodologias de agrupamento multivariados, k-médias e hierárquicos, validados por índices de qualidade de agrupamento. Foram usadas 227 estações localizadas no estado do Paraná com dados mensais referentes ao período de 1976-2006. Os agrupamentos obtidos foram submetidos às medidas de discordância e heterogeneidade para avaliar a homogeneidade dos grupos. A metodologia de agrupamento que obteve o melhor desempenho foi a metodologia híbrida entre k-médias e ward, obtendo erros de, no máximo, 10% na estimativa de quantis regionais adimensionais.

The aim of this thesis is to examine optimization sensitivity in SCORE to the accuracy of particular input data models used in a simulation model of a production line. The purpose is to evaluate if it is sufficient to model input data using sample mean and default distributions instead of fitted distributions. An existing production line has been modeled for the simulation study. SCORE is based on maximizing any key performance measure of the production line while simultaneously minimizing the number of improvements necessary to achieve maximum performance. The sensitivity to the input models should become apparent the more changes required. The experiments concluded that the optimization struggles to obtain convergence when fitted distribution models were used. Configuring the input parameters to the optimization might yield better optimization result. The final conclusion is that the optimization is sensitive to what input data models are used in the simulation model.

Survival analysis today is widely implemented in the fields of medical and biological sciences, social sciences, econometrics, and engineering. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. Events or outcomes are defined by a transition from one discrete state to another at an instantaneous moment in time. In the recent years, research in the area of survival analysis has increased greatly because of its large usage in areas related to bio sciences and the pharmaceutical studies. After identifying the probability density function that best characterizes the tumors and survival times of breast cancer women, one purpose of this research is to compare the efficiency between competing estimators of the survival function. Our study includes evaluation of parametric, semi-parametric and nonparametric analysis of probability survival models. Artificial Neural Networks (ANNs), recently applied to a number of clinical, business, forecasting, time series prediction, and other applications, are computational systems consisting of artificial neurons called nodes arranged in different layers with interconnecting links. The main interest in neural networks comes from their ability to approximate complex nonlinear functions. Among the available wide range of neural networks, most research is concentrated around feed forward neural networks called Multi-layer perceptrons (MLPs). One of the important components of an artificial neural network (ANN) is the activation function. This work discusses properties of activation functions in multilayer neural networks applied to breast cancer stage classification. There are a number of common activation functions in use with ANNs. The main objective in this work is to compare and analyze the performance of MLPs which has back-propagation algorithm using various activation functions for the neurons of hidden and output layers to evaluate their performance on the stage classification of breast cancer data. Survival analysis can be considered a classification problem in which the application of machine-learning methods is appropriate. By establishing meaningful intervals of time according to a particular situation, survival analysis can easily be seen as a classification problem. Survival analysis methods deals with waiting time, i.e. time till occurrence of an event. Commonly used method to classify this sort of data is logistic regression. Sometimes, the underlying assumptions of the model are not true. In model building, choosing an appropriate model depends on complexity and the characteristics of the data that affect the appropriateness of the model. Two such strategies, which are used nowadays frequently, are artificial neural network (ANN) and decision trees (DT), which needs a minimal assumption. DT and ANNs are widely used methodological tools based on nonlinear models. They provide a better prediction and classification results than the traditional methodologies such as logistic regression. This study aimed to compare predictions of the ANN, DT and logistic models by breast cancer survival. In this work our goal is to design models using both artificial neural networks and logistic regression that can precisely predict the output (survival) of breast cancer patients. Finally we compare the performances of these models using receiver operating characteristic (ROC) analysis.

Thesis is based on original self-made application programmed at VBA in MS Excel 2007. The reason to build this application was integration of simulation Monte Carlo and chosen optimization methods. The application allows do simulation of the knapsack problem and of the assignment problem with uncertainty. The parameters of these models are possible to set up as changing values in dependence of chosen probability distribution. Output of the simulation is a probability recommendation which objects should be used. Choose of objects depend on optimized models. Results of both models are represented by statistical indexes, tables of parameters and graph.

O planejamento de curto prazo em minas a céu aberto exige a definição de poligonais, que representam os sucessivos avanços de lavra. As poligonais, tradicionalmente, são desenhadas em um processo laborioso na tentativa de delinear como minério em qualidade e quantidade de acordo com os limites determinados. O minério delimitado deve apresentar a menor variabilidade em qualidade possível, com o objetivo de maximizar a recuperação na usina de processamento. Essa dissertação visa desenvolver um fluxo do trabalho para definir poligonais de curto prazo de forma automática, além disso, sequenciar todos os blocos de minério de cada polígono de modo a definir uma sequência interconectada lavrável de poligonais. O fluxo do trabalho foi aplicada à incerteza de teores, obtida através de simulações estocásticas. Algoritmos genéticos foram desenvolvidos em linguagem de programação Python e implementados na forma de plug-in no software geoestatístico Ar2GeMS. Múltiplas iterações são criadas para cada avanço individual, gerando regiões (ou poligonais). Então, a região que apresenta menor variabilidade de teores é selecionada. A distribuição de probabilidade dos teores dos blocos em cada avanço é comparada com a distribuição global de teores, calculada a partir de todos os blocos do corpo de minério. Os resultados mostraram que os teores dos blocos abrangidos pelas poligonais criadas dessa forma apresentam teores similares à distribuição de referência, permitindo o sequenciamento de lavra com distribuição de teores mais próximo possível da distribuição global. Modelos equiprováveis permitem avaliar a incerteza associada à solução proposta. / Open-pit short-term planning requieres the definition of polygons identifying the successive mining advances. These polygons are drawn in a labour intensive task attempting to delineate ore with the quantity and quality within established ranges. The ore delineated by the polygons should have the least possible quality variability among them, helping in maximizing ore recovery at the processing plant. This thesis aims at developíng a workflow for drawing short-term polygons automatically, sequencing all ore blocks within each polygon and leading to a mineable and connected sequence of polygons. This workflow is also tested under grade uncertainty obtained through multiple syochastic simulated models. For this, genetics algorithms were developed in Python programming language and pluged in Ar2GeMS geostatistical software. Multiple iterations were generated for each of the individual advances, generating regions or polygons, and selecting the regions of lower grade variability. The blocks probability distribution within each advance were compared to the global distribution, including all blocks within the ore body. Results show that the polygons generated are comprised by block grades similar to the ones from the reference distribution, leading to mining sequence as close as possible to the global maintaining a quasi-satationarity. Equally probable models provide the means to access the uncertainy in the solution provided.

Master's thesis is focused on solution of the statistical problem to find a probability distribution of a discrete random variable on the basis of the observed data. These estimates are obtained by minimizing pseudo-quasinorm which is introduced here.The thesis further focuses on atributes of this pseudo-quasinorm. It also contains practical application of these methods.

In this paper, we discuss methods for developing a stochastic model which incorporates behavior differences in the predation movements of Anelosimus studiosus (a subsocial spider). Stochastic models for animal movement and, in particular, spider predation movement have been developed previously; however, this paper focuses on the development and implementation of the necessary mathematical and statistical methods required to expand such a model in order to capture a variety of distinct behaviors. A least squares optimization algorithm is used for parameter estimation to fit a single stochastic model to an individual spider during predation resulting in unique parameter values for each spider. Similarities and variations between parameter values across the spiders are analyzed and used to estimate probability distributions for the variable parameter values. An aggregate stochastic model is then created which incorporates the individual dynamics. The comparison between the optimal individual models to the aggregate model indicate the methodology and algorithm developed in this paper are appropriate for simulating a range of individualistic behaviors.

Dissertation thesis is focused on solution of the statistical problem to find a probability distribution of a discrete random variable on the basis of the observed data. These estimates are obtained by minimizing quasi-norms with given constraints. The thesis further focuses on deriving confidence intervals for estimated probabilities. It also contains practical application of these methods.