Saudi Cultural Missions Theses & Dissertations
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Item Restricted Bayesian System Identification of Dynamic Structures for Active Vibration Control(The University of Sheffield, 2025-03-10) AlQahtani, Nasser Ayidh N; Sims, Neil D.Bayesian system identification is a remarkable approach used in either modelling or estimation problems, and especially when addressing uncertainty in structural systems. With the increasing use of flexible structures in aerospace engineering, medicine and robot applications, control techniques are often used to carry out such tasks as unwanted vibration mitigation and damage detection. Therefore understanding the dynamics of the nonlinear structure from both a design and a control perspective is an important step. To the author’s knowledge, limited cases have been made of the Bayesian approaches either in modelling or control in the context of data driven control for vibration control. This is an area with much potential for offering a new perspective on tackling vibration problems. This thesis seeks to fill this gap in the literature. In doing so, several Bayesian systems identification methods have been proposed, ranging from incorporating well known identification structures, such as Wiener-Hammerstein model and NARX, with GP models to Bayesian state space model. These are then used to inform the design of a new kind of active vibration control, making use of linear and nonlinear structural systems. This thesis concludes by drawing attention to the feasibility of Bayesian methods in active vibration control.21 0Item Restricted Hierarchical Bayesian Modelling of Spatio-Temporal Air Pollution Patterns in Athens, Greece(University of Sheffield, 2024) Aldawsari, Hilah; Johnson, Jill SAir pollution is one of the biggest health risks of our time, mainly due to industrial development worldwide. Persistent exposure to air pollution causes many severe diseases, including lung cancer. It also is responsible for millions of deaths annually worldwide. As a result, various researchers have been developing models to estimate air pollution levels throughout the past decades, aiming to better understand their dynamics. These models can also be used to forecast air pollution levels. This forecasted information is crucial for enabling relevant authorities to adopt precautionary measures and to alert the public in advance, especially when the levels are very harmful to human health. Up to date, modelling air pollution is a challenging task due to the various factors that affect its behaviour. One advanced field that enables the modelling of air pollution levels is spatio-temporal modelling. The main focus of this dissertation is to develop a spatio-temporal model to estimate parameters and forecast air pollution levels across different locations in Athens, Greece. Among various pollutants, this work focuses on exploring the ozone (O3) pollutant, along with three meteorological variables, to examine their influence on O3 levels. Two statistical approaches were developed for modelling and forecasting monthly concentrations of O3 in Athens, Greece. The first approach is based on time series modelling using seasonal autoregressive integrated moving average (SARIMA) and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models. The second approach is based on spatio-temporal modelling using hierarchical Bayesian spatiotemporal Gaussian process (GP) models. This model has the ability to capture the complex dynamic of O3 levels, which vary based on several factors, including geographic location and meteorology. This model also has the ability to provide predictions for both space and time. Both SARIMA and SARIMAX models showed a reasonable fit of the data. The SARIMAX outperformed SARIMA models in both training and testing tests in most stations. The hierarchical Bayesian spatio-temporal GP model fits the data well and shows reasonable performance in both forecasting and prediction abilities, with a spatial decay parameter ϕ of 0.13 corresponding to an effective range of 23 km. All statistical data analysis in this dissertation was implemented using the statistical software R.21 0