O planejamento e o funcionamento do sistema elétrico de potência se baseiam na correta parametrização e caracterização de seus elementos, pois a correta parametrização dos sistemas de proteção permite uma atuação confiável e segura. Ademais, a correta caracterização dos parâmetros de elementos como as linhas de transmissão permite calcular o carregamento ótimo para determinados trechos do sistema interconectado com relação ao fluxo de potência, permitindo um melhor planejamento para expansão e instalação de reforços, dentre outros. Desta forma, foram desenvolvidas metodologias para estimação de parâmetros de sistemas de transmissão, que se baseiam na adoção de um modelo para o sistema e utilização de um método de resolução para se obter uma resposta, ou função de transferência, para as entradas e saídas deste modelo. No entanto, a maioria dos métodos existentes na literatura técnica apresenta certas limitações com relação à presença de ruído nos sinais de medição, ou dificuldade na relação existente entre as correntes longitudinais e as medições terminais, ou imprecisão do método de resolução do sistema de equações para determinada situação. Neste trabalho, foram utilizadas técnicas diferentes para a estimação de parâmetros de uma linha diretamente das medições das correntes e tensões terminais da linha em regime, sendo possível reduzir bastante o erro de estimação, pois não é necessário nenhum método de eliminação dos ruídos das medições. Desta forma, a corrente longitudinal é considerada como equivalente à última componente de corrente terminal, e a metodologia adotada apresenta erros pequenos de estimação independente do modelo adotado. Assim, modelam-se linhas de transmissão em cascata de circuitos ?, obtendo-se as medições ruidosas oriundas desta. Por fim, utiliza-se a metodologia baseada na teoria do Filtro de Kalman Unscented para eliminação do ruído nas grandezas medidas e para estimação dos parâmetros série da linha de transmissão. Através da ferramenta computacional Simulation and model-based design (Simulink), realizam-se a obtenção das medições, inclusão de ruído aleatório a estas e cálculos computacionais para estimação dos estados e parâmetros do sistema em regime permanente. / The planning and operation of a power system are based on the correct parameterization and characterization of its elements. By a correct parameterization of protection systems, a reliable and safe operation is allowed. The correct characterization of the parameters of transmission lines allows calculating the optimum power flow for the interconnected power system, allowing a better planning for expansion and installation of reinforcements, among others. In this sense, methodologies were developed for the estimation of transmission system parameters, which are based on the adoption of a model for the system and usage of a resolution method to obtain a response, or transfer function, for the inputs and outputs of the model. However, the most of existing methods in the technical literature presents certain limitations regarding presence of noise in the measurement signals, or difficulty regarding the relationship between the longitudinal currents and the terminal measurement signals, or imprecision of the solving method for the equations system. In this work, different techniques were applied for the estimation of line parameters directly from the measurements of the terminal currents and terminal voltages of the steady state line, being possible to greatly reduce the estimation error, since no extra method of eliminating measurements noise is necessary. In this way the longitudinal current is considered as equivalent to the last terminal current component, and the adopted methodology presents small estimation errors regardless of the adopted model. Thus, transmission lines are modeled by a pi cascade and noisy measurements are obtained. Finally, the methodology is based on the Unscented Kalman Filter theory for noise elimination in the measurements and for estimation of the transmission line longitudinal parameters. By the computational tool Simulink, measurements are obtained, random noise is inserted and computational calculations for estimation of the states and parameters of the system in steady state are done.

This thesis consists of four papers dealing with the futures price process. In the first paper, we propose a two-factor futures volatility model designed for the US natural gas market, but applicable to any futures market where volatility decreases with maturity and varies with the seasons. A closed form analytical expression for European call options is derived within the model and used to calibrate the model to implied market volatilities. The result is used to price swaptions and calendar spread options on the futures curve. In the second paper, a financial market is specified where the underlying asset is driven by a d-dimensional Wiener process and an M dimensional Markov process. On this market, we provide necessary and, in the time homogenous case, sufficient conditions for the futures price to possess a semi-affine term structure. Next, the case when the Markov process is unobservable is considered. We show that the pricing problem in this setting can be viewed as a filtering problem, and we present explicit solutions for futures. Finally, we present explicit solutions for options on futures both in the observable and unobservable case. The third paper is an empirical study of the SABR model, one of the latest contributions to the field of stochastic volatility models. By Monte Carlo simulation we test the accuracy of the approximation the model relies on, and we investigate the stability of the parameters involved. Further, the model is calibrated to market implied volatility, and its dynamic performance is tested. In the fourth paper, co-authored with Tomas Björk and Camilla Landén, we consider HJM type models for the term structure of futures prices, where the volatility is allowed to be an arbitrary smooth functional of the present futures price curve. Using a Lie algebraic approach we investigate when the infinite dimensional futures price process can be realized by a finite dimensional Markovian state space model, and we give general necessary and sufficient conditions, in terms of the volatility structure, for the existence of a finite dimensional realization. We study a number of concrete applications including the model developed in the first paper of this thesis. In particular, we provide necessary and sufficient conditions for when the induced spot price is a Markov process. We prove that the only HJM type futures price models with spot price dependent volatility structures, generically possessing a spot price realization, are the affine ones. These models are thus the only generic spot price models from a futures price term structure point of view. / Diss. Stockholm : Handelshögskolan, 2004

<p>Diss. (sammanfattning) Stockholm : Handelshögskolan, 2006 [6], xiii, [1] s.: sammanfattning, s. 1-259, [5] s.: 4 uppsatser. Spikblad saknas</p>

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Die Arbeit befasst sich mit der effizienten Simulation des funktionellen Verhaltens von Piezoverbundmodulen als Aktor oder Sensor zur Schwingungsbeeinflussung mechanischer Strukturen. Ausgehend von einem FE-Modell werden über den Ansatz energetischer Äquivalenz die effektiven elektro-mechanischen Materialparameter ermittelt. Zur Berücksichtigung im Inneren der Einheitszelle liegender Elektroden werden die elektrischen Randbedingungen der Homogenisierungslastfälle angepasst. Die Homogenisierungslastfälle werden auch genutzt, um Phasenkonzentrationen für die Beanspruchungen der Verbundkomponenten zu ermitteln. Diese Phasenkonzentrationen werden eingesetzt, um aus dem effektiven Gesamtmodell die Beanspruchungen der Komponenten zu extrahieren. Zur dynamischen Modellbildung wird die Zustandsraumbeschreibung verwendet. Die Überführung einer piezo-mechanischen FE-Diskretisierung in ein Zustandsraummodell gelingt mit der Betrachtung der mechanischen Freiheitsgrade als Zustandsvariablen. Zur Abbildung der elektrischen Impedanz im Zustandsraum muss die elektrische Kapazitätsmatrix als Durchgangsmatrix einbezogen werden. Die Reduktion des Zustandsraums basiert auf der modalen Superposition. Die modale Transformationsbasis wird um Moden ergänzt, die die Verformung bei statischer elektrischer Erregung charakterisieren. Die Zustandsraumbeschreibung wird sowohl für eine Potential- als auch für eine Ladungserregung ausgeführt. Das Zustandsraummodell wird unter Verwendung von Filtermatrizen um Ausgangssignale für die mechanischen und elektrischen Beanspruchungsgrößen erweitert. Dies gestattet eine Kopplung der Zustandsraummodelle mit den Beanspruchungsanalysen. Die Anwendung der Berechnungsmethode wird am Beispiel der im SFB/TRR PT-PIESA entwickelten Piezo-Metall-Module demonstriert, die durch direkte Integration von piezokeramischen Basiselementen in Blechstrukturen gekennzeichnet sind. / This thesis deals with the efficient simulation of the functional behaviour of piezo composite modules for applications as actuators or sensors to influence vibrations of machine structures. Based on a FE-discretisation the effective electro-mechanical material parameters of the piezo composite modules are determined with an ansatz of energetic equivalence. To consider electrodes which are located inside the representative volume element the electrical boundary conditions of the load cases for homogenisation are adapted. The load cases for homogenisation are also used to determine the phase concentrations (or fluctuation fields) of stress/strain and electric field/electric displacement field in the composite constituents. These phase concentrations are required to extract stress and strain of the composite components based on the overall model with effective material parameters. For dynamical modelling a state space representation is used. The transformation of a FE-discretisation of the piezo-mechanical system into a state space model is possible by choosing the mechanical degree of freedom as state variables. For consideration of the electrical impedance in the state space model the electrical stiffness respectively capacitance matrix has to incorporate as feedthrough matrix. The dynamical model reduction of the state space model is based on modal superposition. For the correct reproduction of the electrical impedance the modal transformation basis has to be amended by deformation modes which represent the deformation behaviour due to static electrical excitation at the electrodes. The state space representation is built for potential and charge excitation. The state space model is enhanced by filter matrices to incorporate output signals for stress/strain and also for electric field/electric displacement field. This allows the coupling of the state space models with the stress analyses. The application of the simulation method is demonstrated using the example of the piezo-metal-modules developed in the CRC/TR PT-PIESA (German: SFB/TRR PT-PIESA). These piezo-metal-modules are characterised by direct integration of piezoceramic base elements in sheet metal structures.

The topic of the paper is filtering for non-Gaussian dynamic (state space) models by approximate computation of posterior moments using numerical integration. A Gauss-Hermite procedure is implemented based on the approximate posterior mode estimator and curvature recently proposed in 121. This integration-based filtering method will be illustrated by a dynamic trend model for non-Gaussian time series. Comparision of the proposed method with other approximations ([15], [2]) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

No description available.

This thesis explores the methodology of state, and in particular, parameter estimation for time series datasets. Various approaches are investigated that are suitable for nonlinear models and non-Gaussian observations using state space models. The methodologies are applied to a dataset consisting of the historical lynx and hare populations, typically modeled by the Lotka- Volterra equations. With this model and the observed dataset, particle filtering and parameter estimation methods are implemented as a way to better predict the state of the system. Methods for parameter estimation considered include: maximum likelihood estimation, state augmented particle filtering, multiple iterative filtering and particle Markov chain Monte Carlo (PMCMC) methods. The specific advantages and disadvantages for each technique are discussed. However, in most cases, PMCMC is the preferred parameter estimation solution. It has the advantage over other approaches in that it can well approximate any posterior distribution from which inference can be made. / Master's thesis