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In recent decades, there has been a dramatic increase in utilizing renewable energy resources by many power utilities around the world. The tendency toward using renewable energy resources is mainly due to the environmental concerns and fuel cost escalation associated with conventional fossil generation. Among renewable resources, wind energy is a proven source for power generation that positively contributes to global, social, and economic environments. Nowadays, wind energy is a mature, abundant, and emission-free power generation technology, and a significant percentage of electrical power demand is supplied by wind. However, the intermittent nature of wind generation introduces various challenges for both the operation and planning of power systems. One of the problems of increasing the use of wind generation can be seen from the reliability assessment point of view. Indeed, there is a recognized need to study the contribution of wind generation to overall system reliability and to ensure the adequacy of generation capacity. Wind power generation is different than conventional generation (i.e., fossil-based) in that wind power is variable and non-controllable, which can affect power system reliability. Therefore, modeling wind generation in a reliability assessment calls for reliable stochastic simulation techniques that can properly handle the uncertainty and precisely reflect the variable characteristics of the wind at a particular site. The research presented in this thesis focuses on developing a reliable and appropriate model for the reliability assessment of power system generation, including wind energy sources. This thesis uses the Monte Carlo Markov Chain (MCMC) technique due to its ability to produce synthetic wind power time series data that sufficiently consider the randomness of the wind along with keeping the statistical and temporal characteristics of the measured data. Thereafter, the synthetic wind power time series based on MCMC is coupled with a probabilistic sequential methodology for conventional generation in order to assess the overall adequacy of generating systems. The study presented in this thesis is applied to two test systems, designated the Roy Billinton Test System (RBTS) and the IEEE Reliability Test System (IEEE-RTS). A wide range of reliability indices are then calculated, including loss of load expectation (LOLE), loss of energy expectation (LOEE), loss of load frequency (LOLF), energy not supplied per interruption (ENSPI), demand not supplied per interruption (DNSPI), and expected duration per interruption (EDPI). To show the effectiveness of the proposed methodology, a further study is conducted to compare the obtained reliability indices using the MCMC model and the ARMA model, which is often used in reliability studies. The methodologies and the results illustrated in this thesis aim to provide useful information to planners or developers who endeavor to assess the reliability of power generation systems that contain wind generation.

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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T12:07:15Z No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 4054411 bytes, checksum: c22cd915da573fd5a5ce7b45feb85a0f (MD5) / Made available in DSpace on 2016-07-07T12:07:15Z (GMT). No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 4054411 bytes, checksum: c22cd915da573fd5a5ce7b45feb85a0f (MD5) Previous issue date: 2011-02-18 / The velocity-area method is a standard procedure for measurement of river discharge, with wide application in hydrometric studies, standardized at the international level by the norm ISO 748:2007 of the International Standards Organization. This method requires measurement of velocity at several verticals of the river, at different depths for each vertical. In general, a relatively high number of measurements is necessary do determine the discharge. Recently a technique was proposed which results in a robust estimate of river discharge using a reduced number of measurement points, based on elementary properties of fluid dynamics, stemming from the Navier-Stokes equations, and the use of continuous interpolation between the verticals for calculating velocity across the entire river cross section. In the present work the Monte Carlo Markov Chain (MCMC) method is used to search for the optimum positions for velocity measurement, with the objective of reducing the number of measurement points without significant loss of precision, and therefore maximizing the efficiency of the estimate. A dedicated computer algorithm was developed in C programming language and applied to measurements collected on the river Exu, state of Pernambuco, Brazil, in April 2008. It is found that the discharge estimates with three or more measurement points exhibit variations well within uncertainty limits corresponding to the full 27 point estimate using the traditional velocity-area method. Simulation results indicate that the best positions for velocity measurement are close to the surface, and that significant savings in cost and labor may be accomplished by positioning the measurements at strategic points, without precision loss. / O método área-velocidade é um procedimento utilizado para medir a descarga de rios. Esta é uma técnica bastante difundida na hidrometria, e é normatizada internacionalmente pela ISO 748:2007 da International Standard Organization. Este método requer a medição da velocidade em diversas verticais do rio, e em diferentes profundidades de cada vertical. Em geral é necessário um número relativamente elevado de medições para determinar a vazão. Recentemente foi proposta uma técnica que resulta em uma estimativa robusta da descarga fluvial com reduzido número de pontos de medida, que se baseia nas propriedades básicas da dinâmica de fluidos e nas equações de Navier- Stokes, além de utilizar uma interpolação continua para o cálculo das velocidades em toda a seção vertical. No presente trabalho, o método Monte Carlo Markov Chain (MCMC) é utilizado na busca da melhor posição das medidas de velocidade a serem realizados na seção vertical do rio, tal que seja possível reduzir o número de medições e maximizar a eficiência da estimativa. O algoritmo foi desenvolvido em linguagem C e aplicado em medidas de velocidade colhidas no riacho Exu, no estado de Pernanbuco, em abril de 2008. Estimativas de vazão realizadas a partir de 3 medidas de velocidade sobre a seção vertical mostraram-se eficientes, apresentando diferenças da estimativa obtida com 27 pontos através do método área-velocidade tradicional dentro de limites de incerteza. Os resultados de simulação indicam que os melhores locais de medição da velocidade sob a seção vertical situam-se perto da superfície do rio, e que uma economia significativa no custo e no trabalho pode ser conseguida através posicionamento dos pontos de medição em locais estratégicos, sem perda da precisão da estimativa.

Tato diplomová práce se zabývá odhadováním matice intenzit Markovova pro- cesu se spojitým časem na základě diskrétně pozorovaných dat. Začátek práce je věnován jednoduššímu odhadu ze spojité trajektorie pomocí metody maximální věrohodnosti. Dále je zde popsán odhad z diskrétní trajektorie přes výpočet ma- tice pravděpodobností přechodu. Následně je velmi podrobně rozebrán EM al- goritmus, který předchozí odhad zpřesňuje. Na závěr teoretické části je uvedena metoda odhadu zvaná Monte Carlo Markov Chain. Všechny postupy jsou zároveň implementovány v počítačovém softwaru a prezentace jejich výsledk· je obsahem druhé části práce. V té jsou porovnané odhady pro denní, týdenní a měsíční po- zorování a také pro pětiletou a desetiletou pozorovanou trajektorii. K výsledk·m jsou připojeny odhady rozptyl· a intervaly spolehlivosti. 1

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Forests are a valuable resource for humans providing a range of products and services such as construction timber, paper and fuel wood, recreation, as well as living quarters for indigenous populations and habitats for many animal and bird species. Most recent international political agreements such as the Kyoto Protocol emphasise the role of forests as a major sink for atmospheric carbon dioxide mitigation. However, forest areas are rapidly decreasing world wide. Thus, it is vital that efficient strategies and tools are developed to encourage sustainable ecosystem management. These tools must be based on known ecological principles (such as tree physiological and soil nutrient cycle processes), capable of supplying fast and accurate temporal and spatial predictions of the effects of management on both timber production and carbon sequestration. This thesis had two main objectives. The first was to investigate the environmental factors affecting growth and carbon sequestration of Scots pine (Pinus sylvestris L.) across Scotland, by developing a knowledge base through a statistical analysis of old and novel field datasets. Furthermore, the process-based ecosystem model 3-PGN was developed, by coupling the existing models 3-PG and ICBM. 3-PGN calibrated using a Bayesian approach based on Monte Carlo Markov Chain simulations and it was validated for plantation stands. Sensitivity and uncertainty analyses provided an understanding of the internal feedbacks of the model. Further simulations gave a detailed eco-physiological interpretation of the environmental factors affecting Scots pine growth and it provided an assessment of carbon sequestration under the scenario of sustainable, normal production and its effects from the environment. Finally, the study investigated the spatial and temporal patterns of timber production and carbon sequestration by using the spatial version of the model and applying advanced spatial analyses techniques. The second objective was to help close the gap between environmental research and forest management, by setting a strategic framework for a process-based tool for sustainable ecosystem management. The thesis demonstrated the procedures for a site classification scheme based on modelling results and a yield table validation procedure, which can provide a way forward in supporting policies for forest management and ensuring their continued existence in the face of the present and future challenges.

Η επιρροή του παράγοντα χρονοαπόσταση σε ένα σύστημα ακολουθίας οχημάτων μπορεί να προσδιορίσει την επικινδυνότητα του πλήγματος που υφίσταται το σύστημα. Με βάση μια παρ’ολίγον οπισθο-μετωπική σύγκρουση σε αυτοκινητόδρομο 3 λωρίδων, εξετάζεται ο ρόλος της χρονοαπόστασης μεταξύ των οχημάτων σε συνδυασμό με τους χρόνους αντίδρασης των οδηγών στην αντίληψη του επικείμενου κινδύνου. Το μοντέλο ακολουθίας οχημάτων κατά Brill, που συσχετίζει την χρονοαπόσταση, τον χρόνο αντίδρασης του οδηγού και την επιβράδυνση με τη συχνότητα των ατυχημάτων, χρησιμοποιείται ως κύριο εργαλείο για την εκτίμηση της ευαισθησίας της πιθανότητας ενός ατυχήματος. Μέσω της μικροσκοπικής ανάλυσης του βίντεο καταγραφής του ατυχήματος και της επεξεργασίας των δεδομένων και με πηγή έμπνευσης τα προγενέστερα επίμαχα ερωτήματα που θέτει και απαντά ο G. Davis και οι συνεργάτες του, προκύπτουν οι απαραίτητες πληροφορίες για την αριθμητική περιγραφή του ατυχήματος. Με τη χρήση έπειτα του λογισμικού προγράμματος OpenBUGS, το οποίο βασίζεται στη μέθοδο Monte Carlo Markov Chain, γίνεται προσομοίωση του προτύπου ατυχήματος και υπολογίζονται οι τιμές των παραμέτρων που επηρεάζουν τη μορφή του πλήγματος. Από τα αποτελέσματα προκύπτει ο βαθμός που ο συνδυαστικός παράγοντας χρονοαπόσταση και χρόνος αντίδρασης επηρεάζει το πλήγμα και αξιολογείται. Τέλος, με συγκεκριμένες επεμβάσεις επιχειρείται η βελτίωση ολόκληρου του συστήματος ακολουθίας οχημάτων. / The influence of time headway on a car-following system can determine the severity of a shockwave. Based on a near-miss rear-end collision on a 3-lane highway, this study examines the importance of time headway in combination with the driver’s reaction time upon perception of the upcoming hazard. The car-following model developed by Ed. Brill, relating driver’s reaction time, temporal headway and deceleration response to accident frequency, is used as a main tool for assessing the sensitivity of collision probability. Through a microscopic analysis of the video record and data processing and inspired by earlier critical questions that G. Davis and his associates have posed and answered, all the necessary information for the arithmetical description of the accident is extracted. Using the OpenBUGS software, and based on the Monte Carlo Markov Chain method, simulation of the collision prototype is achieved along with the calculation of other main parameters that affect the shockwave form. Simulation results, revealing the influence that the combined factor headway-reaction time has on a shockwave are derived and evaluated. Through certain modifications, the improvement of the whole car-following system is attempted.