This thesis focuses on the use of computer simulation for modeling risk of a multi-state repairable component. In production processes, maintenance decisions are often made based on uncertain assessment of risk, not only in the probability when a process component goes into a state of failure but also in the cost of lost production and preventive maintenance. In this thesis work, preventive maintenance of a component is modeled and simulated, in order to minimize risk (cost), as: a Markov process with multiple states and fixed transition probabilities, under the assumption that with a sufficient number of states the Markovian property is valid, a non Markov process with two possible states and non-fixed transition probabilities for a periodically decreasing reliability component, and a non Markov process with two possible states and non-fixed transition probabilities for a continuously decreasing reliability component. / Engineering Management

No description available.

Multi-State-Markov (MSM) models can be used to characterize the behaviour of categorical outcomes measured repeatedly over time. Kalbfleisch and Lawless (1985) and Gentleman et al. (1994) examine the MSM model under the assumption of time-homogeneous transition intensities. In the context of non-homogeneous intensities, current methods use piecewise constant approximations which are less than ideal. We propose a local likelihood method, based on Tibshirani and Hastie (1987) and Loader (1996), to estimate the transition intensities as continuous functions of time. In particular the local EM algorithm suggested by Betensky et al. (1999) is employed to estimate the in-homogeneous intensities in the presence of missing data. A simulation comparing the piecewise constant method with the local EM method is examined using two different sets of underlying intensities. In addition, model assessment tools such as bandwidth selection, grid size selection, and bootstrapped percentile intervals are examined. Lastly, the method is applied to an HIV data set to examine the intensities with regard to depression scores. Although computationally intensive, it appears that this method is viable for estimating non-homogeneous intensities and outperforms existing methods.

Multi-State-Markov (MSM) models can be used to characterize the behaviour of categorical outcomes measured repeatedly over time. Kalbfleisch and Lawless (1985) and Gentleman et al. (1994) examine the MSM model under the assumption of time-homogeneous transition intensities. In the context of non-homogeneous intensities, current methods use piecewise constant approximations which are less than ideal. We propose a local likelihood method, based on Tibshirani and Hastie (1987) and Loader (1996), to estimate the transition intensities as continuous functions of time. In particular the local EM algorithm suggested by Betensky et al. (1999) is employed to estimate the in-homogeneous intensities in the presence of missing data. A simulation comparing the piecewise constant method with the local EM method is examined using two different sets of underlying intensities. In addition, model assessment tools such as bandwidth selection, grid size selection, and bootstrapped percentile intervals are examined. Lastly, the method is applied to an HIV data set to examine the intensities with regard to depression scores. Although computationally intensive, it appears that this method is viable for estimating non-homogeneous intensities and outperforms existing methods.

No description available.

Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

Renewable energy applications in electric power systems have undergone rapid development and increased use due to global environmental concerns associated with conventional energy sources. Photovoltaics and wind energy sources are considered to be very promising alternatives for power generation because of their tremendous environmental, social and economic benefits, together with public support. </p> <p>Electrical power generation from wind and solar energy behaves quite differently from that of conventional sources. The fundamentally different operating characteristics of these facilities therefore affect power system reliability in a different manner than those of conventional systems. The research work presented in this thesis is focused on the development of appropriate models and techniques for wind energy conversion and photovoltaic conversion systems to assess the adequacy of composite power systems containing wind or solar energy.</p> <p>This research shows that a five-state wind energy conversion system or photovoltaic conversion system model can be used to provide a reasonable assessment in practical power system adequacy studies using an analytical method or a state sampling simulation approach. The reliability benefits of adding single or multiple wind/solar sites in a composite generation and transmission system are examined in this research. The models, methodologies, results and discussion presented in this thesis provide valuable information for system planners assessing the adequacy of composite electric power systems incorporating wind or solar energy conversion systems.

No description available.

Gillespie's stochastic simulation algorithm (SSA) has been a conventional method for stochastic modeling and simulation of biochemical systems. However, its population-based scheme faces the challenge from multi-state situations in many biochemical models. To tackle this problem, Morton-Firth and Bray's stochastic simulator (StochSim) was proposed with a particle-based scheme. The thesis first provides a detailed comparison between these two methods, and then proposes improvements on StochSim and a hybrid method to combine the advantages of the two methods. Analysis and numerical experiment results demonstrate that the hybrid method exhibits extraordinary performance for systems with both the multi-state feature and a high total population. In order to deal with the combinatorial complexity caused by the multi-state situation, the rules-based modeling was proposed by Hlavacek's group and the particle-based Network-Free Algorithm (NFA) has been used for its simulation. In this thesis, we improve the NFA so that it has both the population-based and particle-based features. We also propose a population-based method for simulation of the rule-based models. The bacterial chemotaxis model has served as a good biological example involving multi-state species. We implemented different simulation methods on this model. Then we constructed a graphical interface and compared the behaviors of the bacterium under different mechanisms, including simplified mathematical models and chemically reacting networks which are simulated stochastically. / Master of Science

Pelo fato da maioria das mortes e morbidades associadas à gravidez ocorrerem em torno do parto, a qualidade do cuidado nesse período é crucial para as mães e seus bebês. Para acompanhar as mulheres nessa etapa, o partograma tem sido a ferramenta mais utilizada nas últimas décadas e, devido à sua simplicidade, é frequentemente usado em países com baixa e média renda. No entanto, sua utilização é altamente questionada devido à ausência de evidências que justifiquem uma contribuição ao parto. Para melhorar a qualidade do parto nessas circunstâncias, o projeto BOLD tem sido desenvolvido com o intuito de reduzir a ocorrência de problemas indesejados e com a finalidade desenvolver uma ferramenta moderna, chamada de SELMA, que projetase como uma alternativa ao partograma. Com a finalidade de associar características fixas e dinâmicas avaliadas no parto e identificar quais elementos intra parto podem ser utilizados como gatilhos para realização de uma intervenção e, assim, prevenir um desfecho indesejado, propomos nesta tese a utilização de modelos de sobrevivência com covariáveis dependentes do tempo. Inicialmente, consideramos a modelagem de dados longitudinais e de sobrevivência utilizando funções de risco paramétricas flexíveis. Nesse caso, propomos a utilização de cinco generalizações da distribuição Weibull, da distribuição Nagakami e utilizamos um procedimento geral de seleção de modelos paramétricos usuais via distribuição Gamma generalizada, inédito na modelagem conjunta. Realizamos um extenso estudo de simulação para avaliar as estimativas de máxima verossimilhança e os critérios de discriminação. Além disso, a própria natureza do parto nos leva a um contexto de eventos múltiplos, nos remetendo à utilização dos modelos multiestados. Eles são definidos como modelos para um processo estocástico que a qualquer momento ocupa um conjunto discreto de estados. De uma forma geral, são os modelos mais comuns para descrever o desenvolvimento de dados de tempo de falha longitudinais e são frequentemente utilizados em aplicações médicas. Considerando o contexto de eventos múltiplos, propomos a inclusão de uma covariável dependente do tempo no modelo multiestados a partir de uma modificação dos dados, o que nos trouxe resultados satisfatórios e similares ao esperado na prática clínica. / As most pregnancy-related deaths and morbidities are clustered around the time of child birth, the quality of care during this period is crucial for mothers and their babies. To monitor the women at this stage, the partograph has been the central tool used in recent decades and, motivated by its simplicity, is frequently used in low-and middle-income countries. However, its use is highly questioned due to lack of evidence to justify a contribution to labor. To improve the quality of labor in these circumstances, the BOLD project has been developed in order to reduce the occurrence of pregnancy-related problems and in order to develop a modern tool, called SELMA, which is projected as an alternative to partograph. Aiming to associate fixed and dynamic characteristics evaluated in the delivery and to identify which elements can be used as triggers for performing an intervention, and thus preventing a bad outcome, this thesis proposes the use of survival models with time dependent covariates. Initially, we consider the joint modeling of survival and longitudinal data using flexible parametric hazard functions. In this sense, we propose the use of five generalizations of Weibull distribution, the Nagakami model and an inedited framework to discriminate usual parametric models via the generalized Gamma distribution, performing an extensive simulation study to evaluate the maximum likelihood estimations and the proposed discrimination criteria. Indeed, by its own nature, the birth leads us to a context of multiple events, referring to the use of multi-state models. These are models for a stochastic process which at any time occupies one of a few possible states. In general, they are the most common models to describe the development of longitudinal failure time data and are often used in medical applications. Considering this context, we proposed the inclusion of a time dependent covariate in the multi-state model using a modified version of the input data, which gave us satisfactory results similar to those expected in clinical practice.