Progettazione, ottimizzazione e sviluppo di un sistema WOPI (Water Out Pressure In) per airbox

Un airbox deve fornire al motore sia aria pura, ovvero priva di particelle estranee (motivo per cui al suo interno viene collocato un elemento filtrante), che priva di gocce d’acqua, la cui presenza è possibile sia a causa della condensazione dell’umidità dell’aria aspirata, sia, in volume anche maggiore, in caso di pioggia, aspetto su cui si focalizzerà questo elaborato. Attualmente, per eliminare la presenza di queste particelle d’acqua, vengono praticati fori, più o meno grandi, sulla parete dell’airbox. Questa semplice tecnica, permette di eliminarla, ma causa anche una depressurizzazione. Per contrastare questo fenomeno, è già noto l’utilizzo di condotti elastici, normalmente chiusi o semichiusi, che, nel frattempo, permettono la lenta discesa gravitazionale dell’acqua. Questo sistema, però, non è in grado di sigillare il foro di scolo, provocando quindi anch’esso un calo della pressurizzazione del box. Inoltre, esso potrebbe intasarsi, rendendo inefficace lo scolo. In più, nel tempo, potrebbe sopraggiungere una perdita di elasticità, causando un peggioramento della funzionalità. Scopo del WOPI è eliminare tutti i precedenti inconvenienti nel modo più efficiente possibile.

Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment

The computer controlled screwdriver is a modern technique to perform automatic screwing/unscrewing operations.The main focus is to study the integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment.This thesis describes a concept of automatic screwing mechanism composed by universal robots, in which one arm of the robot is for inserting cables and the other is for screwing the cables on the control panel switch gear box. So far this mechanism is carried out by human operators and is a fairly complex one to perform, due to the multiple cables and connections involved. It's for this reason that an automatic cabling and screwing process would be highly preferred within automotive/automation industries. A study is carried out to analyze the difficulties currently faced and a controller based algorithm is developed to replace the manual human efforts using universal robots, thereby allowing robot arms to insert the cables and screw them onto the control panel switch gear box. Experiments were conducted to evaluate the insertion and screwing strategy, which shows the result of inserting and screwing cables on the control panel switch gearbox precisely.

Sperimentazione della piattaforma IoT Cloud per lo sviluppo di sistemi di monitoraggio

L’Internet of Things (IoT) è un termine utilizzato nel mondo della telecomunicazione che fa riferimento all’estensione di Internet al mondo degli oggetti, che acquisiscono una propria identità, venendo così definiti “intelligenti”. L’uomo in questo ambito avrà sempre meno incidenza sul campo poiché sono le macchine ad interagire tra loro scambiandosi informazioni. Gli ambiti applicativi che comprendono IoT sono innumerevoli ed eterogenei; pertanto, non esiste un'unica soluzione tecnologica che possa coprire qualsiasi scenario. Una delle tecnologie che si prestano bene a svolgere lavori in IoT sono le LoRaWAN. Un punto e una sfida essenziali nell'applicazione della tecnologia LoRaWAN è garantire la massima autonomia dei dispositivi ottenendo il più basso consumo di energia possibile e la ricerca di soluzioni di alimentazione efficienti. L'obiettivo in questo elaborato è quello di realizzare un sistema capace di trasmettere un flusso continuo di informazioni senza l'ausilio e il costante monitoraggio dell'uomo. Viene trattato come controllare dei sensori da remoto e come garantire una migliore autonomia dei dispositivi ottenendo un più basso consumo energetico.