A Bayesian approach to robust identification: application to fault detection

In the Control Engineering field, the so-called Robust Identification techniques deal with the problem of obtaining not only a nominal model of the plant, but also an estimate of the uncertainty associated to the nominal model. Such model of uncertainty is typically characterized as a region in the parameter space or as an uncertainty band around the frequency response of the nominal model. Uncertainty models have been widely used in the design of robust controllers and, recently, their use in model-based fault detection procedures is increasing. In this later case, consistency between new measurements and the uncertainty region is checked. When an inconsistency is found, the existence of a fault is decided. There exist two main approaches to the modeling of model uncertainty: the deterministic/worst case methods and the stochastic/probabilistic methods. At present, there are a number of different methods, e.g., model error modeling, set-membership identification and non-stationary stochastic embedding. In this dissertation we summarize the main procedures and illustrate their results by means of several examples of the literature. As contribution we propose a Bayesian methodology to solve the robust identification problem. The approach is highly unifying since many robust identification techniques can be interpreted as particular cases of the Bayesian framework. Also, the methodology can deal with non-linear structures such as the ones derived from the use of observers. The obtained Bayesian uncertainty models are used to detect faults in a quadruple-tank process and in a three-bladed wind turbine

Real-time Forecasting and Control for Oscillating Wave Energy Devices

Ocean wave energy represents a signicant resource of renewable energy and can make an important contribution to the development of a more sustainable solution in support of the contemporary society, which is becoming more and more energy intensive. A perspective is given on the benefits that wave energy can introduce, in terms of variability of the power supply, when combined with oshore wind. Despite its potential, however, the technology for the generation of electricity from ocean waves is not mature yet. In order to raise the economic performance of Wave energy converters (WECs), still far from being competitive, a large scope exists for the improvement of their capacity factor through more intelligent control systems. Most control solutions proposed in the literature, for the enhancement of the power absorption of WECs, are not implemented in practise because they require future knowledge of the wave elevation or wave excitation force. The non-causality of the unconstrained optimal conditions, termed complex-conjugate control, for the maximum wave energy absorption of WECs consisting of oscillating systems, is analysed. A link between fundamental properties of the radiation of the floating body and the prediction horizon required for an effective implementation of complex-conjugate control is identified. An extensive investigation of the problem of wave elevation and wave excitation force forecasting is then presented. The prediction is treated as a purely stochastic problem, where future values of the wave elevation or wave excitation force are estimated from past measurements at the device location only. The correlation of ocean waves, in fact, allows the achievement of accurate predictions for 1 or 2 wave periods into the future, with linear Autoregressive (AR) models. A relationship between predictability of the excitation force and excitation properties of the floating body is also identified. Finally, a controller for an oscillating wave energy device is developed. Based on the assumption that the excitation force is a narrow-banded harmonic process, the controller is effectively tuned through a single parameter of immediate physical meaning, for performance and motion constraint handling. The non-causality is removed by the parametrisation, the only input of the controller being an on-line estimate of the frequency and amplitude of the excitation force. Simulations in (synthetic and real) irregular waves demonstrate that the solution allows the achievement of levels of power capture that are very close to non-causal complex-conjugate control, in the unconstrained case, and Model predictive control (MPC), in the constrained case. In addition, the hierarchical structure of the proposed controller allows the treatment of the issue of robustness to model uncertainties in quite a straightforward and effective way

Semantic Validation in Structure from Motion

The Structure from Motion (SfM) challenge in computer vision is the process of recovering the 3D structure of a scene from a series of projective measurements that are calculated from a collection of 2D images, taken from different perspectives. SfM consists of three main steps; feature detection and matching, camera motion estimation, and recovery of 3D structure from estimated intrinsic and extrinsic parameters and features. A problem encountered in SfM is that scenes lacking texture or with repetitive features can cause erroneous feature matching between frames. Semantic segmentation offers a route to validate and correct SfM models by labelling pixels in the input images with the use of a deep convolutional neural network. The semantic and geometric properties associated with classes in the scene can be taken advantage of to apply prior constraints to each class of object. The SfM pipeline COLMAP and semantic segmentation pipeline DeepLab were used. This, along with planar reconstruction of the dense model, were used to determine erroneous points that may be occluded from the calculated camera position, given the semantic label, and thus prior constraint of the reconstructed plane. Herein, semantic segmentation is integrated into SfM to apply priors on the 3D point cloud, given the object detection in the 2D input images. Additionally, the semantic labels of matched keypoints are compared and inconsistent semantically labelled points discarded. Furthermore, semantic labels on input images are used for the removal of objects associated with motion in the output SfM models. The proposed approach is evaluated on a data-set of 1102 images of a repetitive architecture scene. This project offers a novel method for improved validation of 3D SfM models

Models for learning reverberant environments

Reverberation is present in all real life enclosures. From our workplaces to our homes and even in places designed as auditoria, such as concert halls and theatres. We have learned to understand speech in the presence of reverberation and also to use it for aesthetics in music. This thesis investigates novel ways enabling machines to learn the properties of reverberant acoustic environments. Training machines to classify rooms based on the effect of reverberation requires the use of data recorded in the room. The typical data for such measurements is the Acoustic Impulse Response (AIR) between the speaker and the receiver as a Finite Impulse Response (FIR) filter. Its representation however is high-dimensional and the measurements are small in number, which limits the design and performance of deep learning algorithms. Understanding properties of the rooms relies on the analysis of reflections that compose the AIRs and the decay and absorption of the sound energy in the room. This thesis proposes novel methods for representing the early reflections, which are strong and sparse in nature and depend on the position of the source and the receiver. The resulting representation significantly reduces the coefficients needed to represent the AIR and can be combined with a stochastic model from the literature to also represent the late reflections. The use of Finite Impulse Response (FIR) for the task of classifying rooms is investigated, which provides novel results in this field. The aforementioned issues related to AIRs are highlighted through the analysis. This leads to the proposal of a data augmentation method for the training of the classifiers based on Generative Adversarial Networks (GANs), which uses existing data to create artificial AIRs, as if they were measured in real rooms. The networks learn properties of the room in the space defined by the parameters of the low-dimensional representation that is proposed in this thesis.Open Acces

The relationship between angular momentum of the lower trunk and shoulder joint forces in overarm throwing athletes

Overarm throwing athletes utilize the kinetic chain, which allows forces generated by the lower body to be transmitted to the throwing arm in a proximal-to-distal sequence. Efficient force transmission from the lower body to the throwing arm can improve performance and reduce risk for injury. The purpose of this thesis was to explore the relationship between the lower trunk (pelvis) maximum angular momentum and the joint resultant forces at the shoulder during the overarm throwing motion of baseball athletes. I hypothesized that there would be a negative correlation between the maximum angular momentum about the superior-inferior axis of the lower trunk during the arm cocking phase and the throwing shoulder joint anterior shear force at ball release, and that there would be a negative correlation between the maximum angular momentum about the superior-inferior axis of the lower trunk during the arm cocking phase and the throwing shoulder joint compressive force at ball release. Two high-speed video cameras were used to record twenty-four competitive male baseball players executing an overarm throw. The videos were digitized, and 3D landmark coordinates were obtained using the Direct Linear Transformation procedure. Lower trunk angular momentum, shoulder joint compressive force, and shoulder joint anterior shear force were calculated from the 3D landmark coordinates and anthropometric data. Bivariate correlations were computed to determine if an association existed between maximum lower trunk angular momentum and shoulder joint anterior shear force at release or shoulder joint compressive force at release. There was no association between lower trunk maximum angular momentum and shoulder joint anterior shear force (r = 0.149, p = 0.244). There was also no association between lower trunk maximum angular momentum and shoulder joint compressive force (r = 0.222, p = 0.149). The lack of association between the lower trunk maximum angular momentum and shoulder joint forces may indicate that this relationship is not determinative of overarm throwing technique. An alternative explanation is that the subjects exhibited inefficient mechanics and an improper timing sequence of the kinetic chain. Future work should investigate the sequencing of force transmission between the lower body and upper body