Tecniche avanzate per l'analisi di sensitività globale: applicazione alla contaminazione delle acque sotterranee.

The thesis is framed within the field of the stochastic approach to flow and transport themes of solutes in natural porous materials. The methodology used to characterise the uncertainty associated with the modular predictions is completely general and can be reproduced in various contexts. The theme of the research includes the following among its main objectives: (a) the development of a Global Sensitivity Analysis on contaminant transport models in the subsoil to research the effects of the uncertainty of the most important parameters; (b) the application of advanced techniques, such as Polynomial Chaos Expansion (PCE), for obtaining surrogate models starting from those which conduct traditionally developed analyses in the context of Monte Carlo simulations, characterised by an often not negligible computational burden; (c) the analyses and the understanding of the key processes at the basis of the transport of solutes in natural porous materials using the aforementioned technical and analysis resources. In the complete picture, the thesis looks at the application of a Continuous Injection transport model of contaminants, of the PCE technique which has already been developed and applied by the thesis supervisors, by way of numerical code, to a Slug Injection model. The methodology was applied to the aforementioned model with original contribution deriving from surrogate models with various degrees of approximation and developing a Global Sensitivity Analysis aimed at the determination of Sobol’ indices.

Machine Learning Unsupervised Methods in the Design of an On-board Health Monitoring System for Satellite Applications

The dissertation starts by providing a description of the phenomena related to the increasing importance recently acquired by satellite applications. The spread of such technology comes with implications, such as an increase in maintenance cost, from which derives the interest in developing advanced techniques that favor an augmented autonomy of spacecrafts in health monitoring. Machine learning techniques are widely employed to lay a foundation for effective systems specialized in fault detection by examining telemetry data. Telemetry consists of a considerable amount of information; therefore, the adopted algorithms must be able to handle multivariate data while facing the limitations imposed by on-board hardware features. In the framework of outlier detection, the dissertation addresses the topic of unsupervised machine learning methods. In the unsupervised scenario, lack of prior knowledge of the data behavior is assumed. In the specific, two models are brought to attention, namely Local Outlier Factor and One-Class Support Vector Machines. Their performances are compared in terms of both the achieved prediction accuracy and the equivalent computational cost. Both models are trained and tested upon the same sets of time series data in a variety of settings, finalized at gaining insights on the effect of the increase in dimensionality. The obtained results allow to claim that both models, combined with a proper tuning of their characteristic parameters, successfully comply with the role of outlier detectors in multivariate time series data. Nevertheless, under this specific context, Local Outlier Factor results to be outperforming One-Class SVM, in that it proves to be more stable over a wider range of input parameter values. This property is especially valuable in unsupervised learning since it suggests that the model is keen to adapting to unforeseen patterns.

Surface-enhanced vibrational techniques for the detection of synthetic organic colorants

The research project object of this thesis is focused on the development of an advanced analytical system based on the combination of an improved thin layer chromatography (TLC) plate coupled with infrared (FTIR) and Raman microscopies for the detection of synthetic dyes. Indeed, the characterization of organic colorants, which are commonly present in mixtures with other components and in a very limited amount, still represents a challenging task in scientific analyses of cultural heritage materials. The approach provides selective spectral fingerprints for each compound, foreseeing the complementary information obtained by micro ATR-RAIRS-FTIR and SERS-Raman analyses, which can be performed on the same separated spot. In particular, silver iodide (AgI) applied on a gold coated slide is proposed as an efficient stationary phase for the discrimination of complex analyte mixtures, such as dyes present in samples of art-historical interest. The gold-AgI-TLC plate shows high performances related both to the chromatographic separation of analytes and to the spectroscopic detection of components. The use of a mid-IR transparent inorganic salt as the stationary phase avoids interferences of the background absorption in FTIR investigations. Moreover, by ATR microscopy measurements performed on the gold-AgI surface, a considerable enhancement in the intensity of spectra is observed. Complementary information can be obtained by Raman analyses, foreseeing a SERS activity of the AgI substrate. The method has been tested for the characterization of a mixture of three synthetic organic colorants widely used in dyeing processes: Brilliant Green (BG1), Rhodamine B (BV10) and Methylene Blue (BB9).

Applications of advanced and dual energy computed tomography in proton therapy

This thesis focuses on advanced reconstruction methods and Dual Energy (DE) Computed Tomography (CT) applications for proton therapy, aiming at improving patient positioning and investigating approaches to deal with metal artifacts. To tackle the first goal, an algorithm for post-processing input DE images has been developed. The outputs are tumor- and bone-canceled images, which help in recognising structures in patient body. We proved that positioning error is substantially reduced using contrast enhanced images, thus suggesting the potential of such application. If positioning plays a key role in the delivery, even more important is the quality of planning CT. For that, modern CT scanners offer possibility to tackle challenging cases, like treatment of tumors close to metal implants. Possible approaches for dealing with artifacts introduced by such rods have been investigated experimentally at Paul Scherrer Institut (Switzerland), simulating several treatment plans on an anthropomorphic phantom. In particular, we examined the cases in which none, manual or Iterative Metal Artifact Reduction (iMAR) algorithm were used to correct the artifacts, using both Filtered Back Projection and Sinogram Affirmed Iterative Reconstruction as image reconstruction techniques. Moreover, direct stopping power calculation from DE images with iMAR has also been considered as alternative approach. Delivered dose measured with Gafchromic EBT3 films was compared with the one calculated in Treatment Planning System. Residual positioning errors, daily machine dependent uncertainties and film quenching have been taken into account in the analyses. Although plans with multiple fields seemed more robust than single field, results showed in general better agreement between prescribed and delivered dose when using iMAR, especially if combined with DE approach. Thus, we proved the potential of these advanced algorithms in improving dosimetry for plans in presence of metal implants.

Motion Control of an Advanced Electric Implement for Agricultural Autonomous Vehicles

Nowadays, the development of intelligent and autonomous vehicles used to perform agricultural activities is essential to improve quantity and quality of agricultural productions. Moreover, with automation techniques it is possible to reduce the usage of agrochemicals and minimize the pollution. The University of Bologna is developing an innovative system for orchard management called ORTO (Orchard Rapid Transportation System). This system involves an autonomous electric vehicle capable to perform agricultural activities inside an orchard structure. The vehicle is equipped with an implement capable to perform different tasks. The purpose of this thesis project is to control the vehicle and the implement to perform an inter-row grass mowing. This kind of task requires a synchronized motion between the traction motors and the implement motors. A motion control system has been developed to generate trajectories and manage their synchronization. Two main trajectories type have been used: a five order polynomial trajectory and a trapezoidal trajectory. These two kinds of trajectories have been chosen in order to perform a uniform grass mowing, paying a particular attention to the constrains of the system. To synchronize the motions, the electronic cams approach has been adopted. A master profile has been generated and all the trajectories have been linked to the master motion. Moreover, a safety system has been developed. The aim of this system is firstly to improve the safety during the motion, furthermore it allows to manage obstacle detection and avoidance. Using some particular techniques obstacles can be detected and recovery action can be performed to overcome the problem. Once the measured force reaches the predefined force threshold, then the vehicle stops immediately its motion. The whole project has been developed by employing Matlab and Simulink. Eventually, the software has been translated into C code and executed on the TI Lauchpad XL board.

Innovative layout design and radiated harmonic analysis for advanced frequency-diverse array solutions

Wireless power transfer is becoming a crucial and demanding task in the IoT world. Despite the already known solutions exploiting a near-field powering approach, far-field WPT is definitely more challenging, and commercial applications are not available yet. This thesis proposes the recent frequency-diverse array technology as a potential candidate for realizing smart and reconfigurable far-field WPT solutions. In the first section of this work, an analysis on some FDA systems is performed, identifying the planar array with circular geometry as the most promising layout in terms of radiation properties. Then, a novel energy aware solution to handle the critical time variability of the FDA beam pattern is proposed. It consists on a time-control strategy through a triangular pulse, and it allows to achieve ad-hoc and real time WPT. Moreover, an essential frequency domain analysis of the radiating behaviour of a pulsed FDA system is presented. This study highlights the benefits of exploiting the intrinsic pulse harmonics for powering purposes, thus minimising the power loss. Later, the electromagnetic design of a radial FDA architecture is addressed. In this context, an exhaustive investigation on miniaturization techniques is carried out; the use of multiple shorting pins together with a meandered feeding network has been selected as a powerful solution to halve the original prototype dimension. Finally, accurate simulations of the designed radial FDA system are performed, and the obtained results are given.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.