A framework for cyber-physical system simulation

In these last years, systems engineering has became one of the major research domains. The complexity of systems has increased constantly and nowadays Cyber-Physical Systems (CPS) are a category of particular interest: these, are systems composed by a cyber part (computer-based algorithms) that monitor and control some physical processes. Their development and simulation are both complex due to the importance of the interaction between the cyber and the physical entities: there are a lot of models written in different languages that need to exchange information among each other. Normally people use an orchestrator that takes care of the simulation of the models and the exchange of informations. This orchestrator is developed manually and this is a tedious and long work. Our proposition is to achieve to generate the orchestrator automatically through the use of Co-Modeling, i.e. by modeling the coordination. Before achieving this ultimate goal, it is important to understand the mechanisms and de facto standards that could be used in a co-modeling framework. So, I studied the use of a technology employed for co-simulation in the industry: FMI. In order to better understand the FMI standard, I realized an automatic export, in the FMI format, of the models realized in an existing software for discrete modeling: TimeSquare. I also developed a simple physical model in the existing open source openmodelica tool. Later, I started to understand how works an orchestrator, developing a simple one: this will be useful in future to generate an orchestrator automatically.

Trasformazione di un ambiente fisico in Cyber-Physical Ecosystem

Obiettivo di questo lavoro di tesi è il perfezionamento di un sistema di Health Smart Home, ovvero un ambiente fisico (ad esempio un'abitazione) che incorpora una rete di comunicazione in grado di connettere apparecchi elettronici e servizi controllabili da remoto, con l'obiettivo di facilitare la vita ad anziani, malati o disabili nelle loro case. Questo lavoro di tesi mostrerà come è stato possibile realizzare tale sistema partendo dalle teorie e dalle tecnologie sviluppate per il Web Semantico, al fine di trasformare l'ambiente fisico in un Cyber Physical (Eco)System perfettamente funzionante.

Computazione Embodied e Disembodied: Cloud-based IoT

La tesi esplora la co-esistenza di computazioni embodied e disembodied nei moderni sistemi software, adottando come caso di studio il recente trend che vede sempre più coesi e integrati sistemi per l'Internet of Things e sistemi Cloud-based. Si analizzano i principali modelli di comunicazione, protocolli di comunicazione e architetture situate. Inoltre si realizza una piattaforma IoT Middleware cloud-based per mostrare come la computazione possa essere distribuita lato embodied e disembodied.

Smart freight transport in urban areas: a crowd-shipping model using autonomous vehicles

L'avanzamento dell'e-commerce e l'aumento della densità abitativa nel centro città sono elementi che incentivano l'incremento della richiesta merci all'interno dei centri urbani. L'attenzione all'impatto ambientale derivante da queste attività operative è un punto focale oggetto di sempre maggiore interesse. Attraverso il seguente studio, l'obiettivo è definire attuali e potenziali soluzioni nell'ambito della logistica urbana, con particolare interesse alle consegne dell'ultimo miglio. Una soluzione proposta riguarda la possibilità di sfruttare la capacità disponibile nei flussi generati dalla folla per movimentare merce, pratica nota sotto il nome di Crowd-shipping. L'idea consiste nella saturazione di mezzi già presenti nella rete urbana al fine di ridurre il numero di veicoli commerciali e minimizzare le esternalità negative annesse. A supporto di questa iniziativa, nell'analisi verranno considerati veicoli autonomi elettrici a guida autonoma. La tesi è incentrata sulla definizione di un modello di ottimizzazione matematica, che mira a designare un network logistico-distributivo efficiente per le consegne dell'ultimo miglio e a minimizzare le distanze degli attori coinvolti. Il problema proposto rappresenta una variante del Vehicle Routing Problem con time windows e multi depots. Il problema è NP-hard, quindi computazionalmente complesso per cui sarà necessario, in fase di analisi, definire un approccio euristico che permetterà di ottenere una soluzione sub-ottima in un tempo di calcolo ragionevole per istanze maggiori. L'analisi è stata sviluppata nell'ambiente di sviluppo Eclipse, attraverso il risolutore Cplex, in linguaggio Java. Per poterne comprendere la validità, è prevista un'ultima fase in cui gli output del modello ottimo e dell'euristica vengono confrontati tra loro su parametri caratteristici. Bisogna tuttavia considerare che l' utilizzo di sistemi cyber-fisici a supporto della logistica non può prescindere da un costante sguardo verso il progresso.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Analysis of the impact of stationary energy storage systems in trolleybus grids using Simulink-based modelling

The voltage profile of the catenary between traction substations (TSSs) is affected by the trolleybus current intake and by its position with respect to the TSSs: the higher the current requested by the bus and the further the bus from the TSSs, the deeper the voltage drop. When the voltage drops below 500V, the trolleybus is forced to decrease its consumption by reducing its input current. This thesis deals with the analysis of the improvements that the installation of an BESS produces in the operation of a particularly loaded FS of the DC trolleybus network of the city of Bologna. The stationary BESS is charged by the TSSs during off-peak times and delivers the stored energy when the catenary is overloaded alleviating the load on the TSSs and reducing the voltage drops. Only IMC buses are considered in the prospect of a future disposal of all internal combustion engine vehicles. These trolleybuses cause deeper voltage drops because they absorb enough current to power their traction motor and recharge the on board battery. The control of the BESS aims to keep the catenary voltage within the admissible voltage range and makes sure that all physical limitations are met. A model of FS Marconi Trento Trieste is implemented in Simulink environment to simulate its daily operation and compare the behavior of the trolleybus network with and without BESS. From the simulation without BESS, the best location of the energy storage system is deduced, and the battery control is tuned. Furthermore, from the knowledge of the load curve and the battery control trans-characteristic, it is formulated a prediction of the voltage distribution at BESS connection point. The prediction is then compared with the simulation results to validate the Simulink model. The BESS allows to decrease the voltage drops along the catenary, the Joule losses and the current delivered by the TSSs, indicating that the BESS can be a solution to improve the operation of the trolleybus network.

Integration of ROS for the control of industrial robotic systems in a proprietary environment

The paper deals with the integration of ROS, in the proprietary environment of the Marchesini Group company, for the control of industrial robotic systems. The basic tools of this open-source software are deeply studied to model a full proprietary Pick and Place manipulator inside it, and to develop custom ROS nodes to calculate trajectories; speaking of which, the URDF format is the standard to represent robots in ROS and the motion planning framework MoveIt offers user-friendly high-level methods. The communication between ROS and the Marchesini control architecture is established using the OPC UA standard; the tasks computed are transmitted offline to the PLC, supervisor controller of the physical robot, because the performances of the protocol don’t allow any kind of active control by ROS. Once the data are completely stored at the Marchesini side, the industrial PC makes the real robot execute a trajectory computed by MoveIt, so that it replicates the behaviour of the simulated manipulator in Rviz. Multiple experiments are performed to evaluate in detail the potential of ROS in the planning of movements for the company proprietary robots. The project ends with a small study regarding the use of ROS as a simulation platform. First, it is necessary to understand how a robotic application of the company can be reproduced in the Gazebo real world simulator. Then, a ROS node extracts information and examines the simulated robot behaviour, through the subscription to specific topics.

Cyber threat intelligence: identifying hardcoded secrets in GitHub

The usage of version control systems and the capabilities of storing the source code in public platforms such as GitHub increased the number of passwords, API Keys and tokens that can be found and used causing a massive security issue for people and companies. In this project, SAP's secret scanner Credential Digger is presented. How it can scan repositories to detect hardcoded secrets and how it manages to filter out the false positives between them. Moreover, how I have implemented the Credential Digger's pre-commit hook. A performance comparison between three different implementations of the hook based on how it interacts with the Machine Learning model is presented. This project also includes how it is possible to use already detected credentials to decrease the number false positive by leveraging the similarity between leaks by using the Bucket System.