A rapidly melting ice sheet produces a distinctive geometry of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a new method, based on a Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. The Kalman smoother technique iteratively calculates the maximum likelihood estimate of Greenland and West Antarctic ice sheet melt rates at each time step, and it allows for data gaps while also permitting the estimation of non-linear trends. We have also implemented a fixed multi-model Kalman filter that allows us to rigorously account for additional contributions to SL changes, such as glacial isostatic adjustment and thermal expansion. We report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records before applying the new methodology to historical tide gauge records. In the historical tide gauge study we infer a global mean SL rise of ~1.5 ± 0.5 mm/yr up to 1970, followed by an acceleration to a rate of ~2.0 ± 0.5 mm/yr in 2008. In addition to its connection to SL, Greenland and its large ice sheet act as a barrier to storm systems traversing the North Atlantic. As a result of the interaction with Greenland, low-pressure systems located in the Irminger Sea, between Iceland and Greenland, often produce strong low-level winds. Through a combination of modeling and the analysis of rare in-situ observations, we explore the evolution of a lee cyclone that resulted in three high-speed-wind events in November 2004. Understanding Greenland’s role in these events is critical in our understanding of local weather in this region.

A rapidly melting ice sheet produces a distinctive geometry of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a new method, based on a Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. The Kalman smoother technique iteratively calculates the maximum likelihood estimate of Greenland and West Antarctic ice sheet melt rates at each time step, and it allows for data gaps while also permitting the estimation of non-linear trends. We have also implemented a fixed multi-model Kalman filter that allows us to rigorously account for additional contributions to SL changes, such as glacial isostatic adjustment and thermal expansion. We report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records before applying the new methodology to historical tide gauge records. In the historical tide gauge study we infer a global mean SL rise of ~1.5 ± 0.5 mm/yr up to 1970, followed by an acceleration to a rate of ~2.0 ± 0.5 mm/yr in 2008. In addition to its connection to SL, Greenland and its large ice sheet act as a barrier to storm systems traversing the North Atlantic. As a result of the interaction with Greenland, low-pressure systems located in the Irminger Sea, between Iceland and Greenland, often produce strong low-level winds. Through a combination of modeling and the analysis of rare in-situ observations, we explore the evolution of a lee cyclone that resulted in three high-speed-wind events in November 2004. Understanding Greenland’s role in these events is critical in our understanding of local weather in this region.

Ce mémoire de thèse vise à proposer de nouvelles méthodes robustes pour l'estimation de la fréquence respiratoire (FR) à partir des signaux physiologiques souvent utilisés dans la clinique comme l'électrocardiogramme (ECG) ou le photoplethysmogramme (PPG), tout en évitant de porter des capteurs encombrants et inconfortables. En effet, la respiration influence les signaux ECG et/ou PPG. Plusieurs modulations qui décrivent la respiration sont extraites basée principalement sur l'amplitude, la fréquence et la ligne de base. Il est toutefois difficile de déterminer la combinaison optimale des modulations pour obtenir une estimation précise de la FR en raison du bruit, la spécificité de chaque patient et de l'activité. Après une revue de la littérature, il ressort que peu de travaux ont étudié la qualité de ces modulations. Nous proposons donc de quantifier la qualité des modulations à l'aide d'indices de qualité respiratoire (IQR), un nouvel indice basé sur une modulation sinusoïdale est introduit. Puis, deux méthodes sont proposées: la première sélectionne automatiquement la modulation avec l'IQR le plus élevé pour une estimation de la FR, la seconde combine les deux meilleurs modulations avec le lisseur de Kalman (LK). Une nouvelle approche de fusion de modulations basée sur un modèle multimodale est également explorée. Ces méthodes sont évaluées sur trois bases de données de différents contextes cliniques: la surveillance dans les soins postopératoires (où les patients sont immobiles), le suivi pendant les activités physiques quotidiennes et la surveillance néonatale. Les résultats expérimentaux montrent que les IQRs associés à un algorithme de fusion augmentent la précision de l'estimation de la FR à partir des modulations dérivées et montrent des résultats supérieurs aux travaux issus de la littérature. / The presented work in this dissertation concerns the development of approaches to estimate the breathing rate (BR) accurately from the electrocardiogram (ECG) and photoplethysmogram (PPG), to avoid wearing cumbersome and uncomfortable sensors for direct measurements. In fact, the respiration influences ECG and PPG signals. Several modulations are extracted to describe breathing cycles based on amplitude, frequency and baseline. However, it is difficult to determine the optimal combination to estimate the BR due to the noise and patient-dependency. Since few works have studied the quality of these modulations, we propose to study the quality of modulations using respiratory quality indices (RQI). To do so, we present two methods: the first automatically selects the modulations with the highest RQI for BR estimation, the second tracks the respiration signal using Kalman smoother. The obtained results show superior performance comparing to the methods in the literature. In addition, an extension of fusion approach is presented based on a multi-mode model. These proposed methods are tested on several datasets with different clinical contexts: monitoring post-operative care (where patients are immobile), daily physical activities and neonatal monitoring. The experimental results show that the RQIs coupled with a fusion algorithm increase the accuracy of the BR estimation from the derived modulations.

In this thesis we have investigated different numerical filters for joint input and state estimation, with the aim of designing a robust algorithm capable of monitoring the continuous motion and loading in a truck chassis. The algorithm has to be able to use sparse measurements of the motion on different parts of the truck as it is excited by road induced vibrations, and transform this data into knowledge of the state in the entire system. To do this, the algorithm has to be supplied with information about the dynamic properties of the current system. In Paper A we have developed and implemented a fixed-lag smoother for joint input and state estimation in linear time-invariant dynamic structures. A fixed-lag smoother maximizes the use of information available in the measurements by allowing a small time lag in the estimation. As input, external forces as well as support motions can be computed. Furthermore, both measurement noise and model errors are accounted for and simulated as stochastic processes. The filter is firstly verified with straightforward numerical simulations of a simply supported beam, followed by a more involved simulation of a truck fuel tank. It is shown that the fixed-lag smoother performs very well, it estimates both input and states with a high accuracy even though the signals are contaminated with noise and the model contains errors. In Paper B the fixed-lag smoother is applied on real measurements. We investigate the capabilities of the proposed filter by analysing acceleration measurements from a truck side skirt excited by road induced vibrations. In this study, we focus on estimating the state in the side skirt body from a minimum number of measurement sensors. The dynamic properties of the side skirt are obtained experimentally from an operational modal analysis. It is shown that the fixed-lag smoother estimates the state very well. The results also shows that the smoothing effect is larger when fewer measurement sensors are used. / <p>QC 20160928</p>

Aircraft flight test data are processed by optimal estimation programs to estimate the aircraft state trajectory (3 DOF) and to identify the unknown parameters, including constant biases and scale factor of the measurement instrumentation system. The methods applied in processing aircraft flight test data are the iterative extended Kalman filter/smoother and fixed-point smoother (IEKFSFPS) method and the two-step estimator (TSE) method. The models of an aircraft flight dynamic system and measurement instrumentation system are established. The principles of IEKFSFPS and TSE methods are derived and summarized, and their algorithms are programmed with MATLAB codes. Several numerical experiments of flight data processing and parameter identification are carried out by using IEKFSFPS and TSE algorithm programs. Comparison and discussion of the simulation results with the two methods are made. The TSE+IEKFSFPS combination method is presented and proven to be effective and practical. Figures and tables of the results are presented.

Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.

Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2008.