A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

Clinical applicability of an US model to calculate the head blood outflow through collateral vessels.

Le vene giugulari interne sembrano essere la via principale attraverso cui il sangue defluisce dal cervello verso il cuore, quando siamo in posizione supina. Nel 2008 il professor Paolo Zamboni ha scoperto che una diminuzione dell'attività giugulare può portare allo sviluppo di una condizione emodinamica chiamata CCSVI. Questa può causare ipossia, ritardi nella perfusione cerebrale e riduzione del drenaggio dei cataboliti, oltre ad un'attivazione infiammatoria delle piccole vene e dei tessuti vicini. Questa condizione è stata da subito associata alla sclerosi multipla e su questo argomento si sono dibattuti molti gruppi di ricerca. Inoltre, altre patologie sembrano essere associate alla CCSVI, come il morbo di Parkinson, l'Alzheimer e la sindrome di Meniere. Proprio quest'ultima è uno degli argomenti che attualmente interessa di più il gruppo di lavoro in cui mi sono inserita. Questa patologia comporta problemi uditivi, come sordità e tinnito, vertigini e nausea. Il gruppo Vascolar Disease Center (VDC) dell'Università di Ferrara ha previsto per l'anno 2015 uno studio multicentrico, in cui si cercherà di verificare la correlazione tra CCSVI e sindrome di Meniere. La mia tesi fa parte di un studio preliminare a quello multicentrico. All'inizio del lavoro mi sono dedicata ad un'analisi critica di un modello emodinamico per la quantificazione dei flussi sanguigni: il modello BMC, pubblicato nel 2013 dal gruppo VDC, effettuando in parallelo una ricerca bibliografica sullo stato dell'arte in materia. In seguito ho cominciato a studiare off-line diversi studi patologici e fisiologici, in modo da prendere confidenza con gli strumenti e con le metodologie da utilizzare. Sono stata poi coinvolta dal gruppo VDC per partecipare attivamente al miglioramento del protocollo legato al modello BMC. Infine ho analizzato, con due metodologie differenti, 35 studi effettuati su pazienti otorinolaringoiatrici. Con i risultati ottenuti ho potuto effettuare diverse analisi statistiche al fine di verificare l'equivalenza delle due metodologie. L'obiettivo ultimo era quello di stabilire quale delle due fosse la tecnica migliore da utilizzare, successivamente, nello studio multicentrico.

Axion-like particles and the 3.5 keV line in 4D string models

This work is focused on axions and axion like particles (ALPs) and their possible relation with the 3.55 keV photon line detected, in recent years, from galaxy clusters and other astrophysical objects. We focus on axions that come from string compactification and we study the vacuum structure of the resulting low energy 4D N=1 supergravity effective field theory. We then provide a model which might explain the 3.55 keV line through the following processes. A 7.1 keV dark matter axion decays in two light axions, which, in turn, are transformed into photons thanks to the Primakoff effect and the existence of a kinetic mixing between two U(1)s gauge symmetries belonging respectively to the hidden and the visible sector. We present two models, the first one gives an outcome inconsistent with experimental data, while the second can yield the desired result.

Characterization of ENSO changes under idealised radiative forcing in an intermediate complexity general circulation model

Intermediate-complexity general circulation models are a fundamental tool to investigate the role of internal and external variability within the general circulation of the atmosphere and ocean. The model used in this thesis is an intermediate complexity atmospheric general circulation model (SPEEDY) coupled to a state-of-the-art modelling framework for the ocean (NEMO). We assess to which extent the model allows a realistic simulation of the most prominent natural mode of variability at interannual time scales: El-Niño Southern Oscillation (ENSO). To a good approximation, the model represents the ENSO-induced Sea Surface Temperature (SST) pattern in the equatorial Pacific, despite a cold tongue-like bias. The model underestimates (overestimates) the typical ENSO spatial variability during the winter (summer) seasons. The mid-latitude response to ENSO reveals that the typical poleward stationary Rossby wave train is reasonably well represented. The spectral decomposition of ENSO features a spectrum that lacks periodicity at high frequencies and is overly periodic at interannual timescales. We then implemented an idealised transient mean state change in the SPEEDY model. A warmer climate is simulated by an alteration of the parametrized radiative fluxes that corresponds to doubled carbon dioxide absorptivity. Results indicate that the globally averaged surface air temperature increases of 0.76 K. Regionally, the induced signal on the SST field features a significant warming over the central-western Pacific and an El-Niño-like warming in the subtropics. In general, the model features a weakening of the tropical Walker circulation and a poleward expansion of the local Hadley cell. This response is also detected in a poleward rearrangement of the tropical convective rainfall pattern. The model setting that has been here implemented provides a valid theoretical support for future studies on climate sensitivity and forced modes of variability under mean state changes.

Model-Based Contrastive Explanations for XAIP: Towards a General Model and Prototype

Planning is an important sub-field of artificial intelligence (AI) focusing on letting intelligent agents deliberate on the most adequate course of action to attain their goals. Thanks to the recent boost in the number of critical domains and systems which exploit planning for their internal procedures, there is an increasing need for planning systems to become more transparent and trustworthy. Along this line, planning systems are now required to produce not only plans but also explanations about those plans, or the way they were attained. To address this issue, a new research area is emerging in the AI panorama: eXplainable AI (XAI), within which explainable planning (XAIP) is a pivotal sub-field. As a recent domain, XAIP is far from mature. No consensus has been reached in the literature about what explanations are, how they should be computed, and what they should explain in the first place. Furthermore, existing contributions are mostly theoretical, and software implementations are rarely more than preliminary. To overcome such issues, in this thesis we design an explainable planning framework bridging the gap between theoretical contributions from literature and software implementations. More precisely, taking inspiration from the state of the art, we develop a formal model for XAIP, and the software tool enabling its practical exploitation. Accordingly, the contribution of this thesis is four-folded. First, we review the state of the art of XAIP, supplying an outline of its most significant contributions from the literature. We then generalise the aforementioned contributions into a unified model for XAIP, aimed at supporting model-based contrastive explanations. Next, we design and implement an algorithm-agnostic library for XAIP based on our model. Finally, we validate our library from a technological perspective, via an extensive testing suite. Furthermore, we assess its performance and usability through a set of benchmarks and end-to-end examples.

Non-line-of-sight localization with frequency-selective metasurfaces

An emerging technology, that Smart Radio Environments rely on to improve wireless link quality, are Reconfigurable Intelligent Surfaces (RISs). A RIS, in general, can be understood as a thin layer of EM composite material, typically mounted on the walls or ceilings of buildings, which can be reconfigured even after its deployment in the network. RISs made by composing artificial materials in an engineered way, in order to obtain unconventional characteristics, are called metasurfaces. Through the programming of the RIS, it is possible to control and/or modify the radio waves that affect it, thus shaping the radio environment. To overcome the limitations of RISs, the metaprism represents an alternative: it is a passive and non-reconfigurable frequency-selective metasurface that acts as a metamirror to improve the efficiency of the wireless link. In particular, using an OFDM (Orthogonal Frequency-Division Multiplexing) signaling it is possible to control the reflection of the signal, suitably selecting the sub-carrier assigned to each user, without having to interact with the metaprism or having to estimate the CSI. This thesis investigates how OFDM signaling and metaprism can be used for localization purposes, especially to extend the coverage area at low cost, in a scenario where the user is in NLoS (Non-line-of-sight) conditions with respect to the base station, both single antenna. In particular, the paper concerns the design of the analytical model and the corresponding Matlab implementation of a Maximum Likelihood (ML) estimator able to estimate the unknown position, behind an obstacle, from which a generic user transmits to a base station, exploiting the metaprism.