Sviluppo di un ambiente software di test e analisi delle prestazioni di INS di livello navigation e tactical grade

In questa tesi abbiamo provato a definire fino a che punto le misure di sensori siano affidabili, creando un simulatore che sia in grado di analizzare, qualitativamente e quantitativamente, le prestazioni di sensori inerziali facenti parte di sistemi di navigazione inerziale. Non ci siamo soffermati troppo sulle dinamiche dovute agli errori deterministici, che sono eliminabili facilmente mediante prove sperimentali e test, ma abbiamo puntato ad uno studio approfondito riguardante gli errori dovuti a processi stocastici casuali. Il simulatore, programmato sulla piattaforma MATLAB/Simulink, prende i dati grezzi contenuti all’interno dei datasheets dei sensori e li simula, riportando risultati numerici e grafici degli errori risultanti dall’utilizzo di quei specifici sensori; in particolare, esso mette in luce l’andamento degli errori di posizione, velocità ed assetto ad ogni istante di tempo della simulazione. L’analisi effettuata all’interno dell’elaborato ha successivamente condotto all’identificazione dei giroscopi laser come i sensori che soffrono meno di questi disturbi non-sistematici, portandoli ad un livello sopraelevato rispetto ai MEMS ed ai FOG.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.

Design and management of a supply chain: a joint location-inventory problem under uncertainty (Part A)

In this paper, a joint location-inventory model is proposed that simultaneously optimises strategic supply chain design decisions such as facility location and customer allocation to facilities, and tactical-operational inventory management and production scheduling decisions. All this is analysed in a context of demand uncertainty and supply uncertainty. While demand uncertainty stems from potential fluctuations in customer demands over time, supply-side uncertainty is associated with the risk of “disruption” to which facilities may be subject. The latter is caused by external factors such as natural disasters, strikes, changes of ownership and information technology security incidents. The proposed model is formulated as a non-linear mixed integer programming problem to minimise the expected total cost, which includes four basic cost items: the fixed cost of locating facilities at candidate sites, the cost of transport from facilities to customers, the cost of working inventory, and the cost of safety stock. Next, since the optimisation problem is very complex and the number of evaluable instances is very low, a "matheuristic" solution is presented. This approach has a twofold objective: on the one hand, it considers a larger number of facilities and customers within the network in order to reproduce a supply chain configuration that more closely reflects a real-world context; on the other hand, it serves to generate a starting solution and perform a series of iterations to try to improve it. Thanks to this algorithm, it was possible to obtain a solution characterised by a lower total system cost than that observed for the initial solution. The study concludes with some reflections and the description of possible future insights.