Multitemporal analysis of satellite imagery: implementation of a land evolution model in ALCS (ATSR LAND CLASSIFICATION SYSTEM)

In this report it was designed an innovative satellite-based monitoring approach applied on the Iraqi Marshlands to survey the extent and distribution of marshland re-flooding and assess the development of wetland vegetation cover. The study, conducted in collaboration with MEEO Srl , makes use of images collected from the sensor (A)ATSR onboard ESA ENVISAT Satellite to collect data at multi-temporal scales and an analysis was adopted to observe the evolution of marshland re-flooding. The methodology uses a multi-temporal pixel-based approach based on classification maps produced by the classification tool SOIL MAPPER ®. The catalogue of the classification maps is available as web service through the Service Support Environment Portal (SSE, supported by ESA). The inundation of the Iraqi marshlands, which has been continuous since April 2003, is characterized by a high degree of variability, ad-hoc interventions and uncertainty. Given the security constraints and vastness of the Iraqi marshlands, as well as cost-effectiveness considerations, satellite remote sensing was the only viable tool to observe the changes taking place on a continuous basis. The proposed system (ALCS – AATSR LAND CLASSIFICATION SYSTEM) avoids the direct use of the (A)ATSR images and foresees the application of LULCC evolution models directly to „stock‟ of classified maps. This approach is made possible by the availability of a 13 year classified image database, conceived and implemented in the CARD project (http://earth.esa.int/rtd/Projects/#CARD).The approach here presented evolves toward an innovative, efficient and fast method to exploit the potentiality of multi-temporal LULCC analysis of (A)ATSR images. The two main objectives of this work are both linked to a sort of assessment: the first is to assessing the ability of modeling with the web-application ALCS using image-based AATSR classified with SOIL MAPPER ® and the second is to evaluate the magnitude, the character and the extension of wetland rehabilitation.

Implementation of a non ground meta interpreter for defeasible logic

Human reasoning is a fascinating and complex cognitive process that can be applied in different research areas such as philosophy, psychology, laws and financial. Unfortunately, developing supporting software (to those different areas) able to cope such as complex reasoning it’s difficult and requires a suitable logic abstract formalism. In this thesis we aim to develop a program, that has the job to evaluate a theory (a set of rules) w.r.t. a Goal, and provide some results such as “The Goal is derivable from the KB5 (of the theory)”. In order to achieve this goal we need to analyse different logics and choose the one that best meets our needs. In logic, usually, we try to determine if a given conclusion is logically implied by a set of assumptions T (theory). However, when we deal with programming logic we need an efficient algorithm in order to find such implications. In this work we use a logic rather similar to human logic. Indeed, human reasoning requires an extension of the first order logic able to reach a conclusion depending on not definitely true6 premises belonging to a incomplete set of knowledge. Thus, we implemented a defeasible logic7 framework able to manipulate defeasible rules. Defeasible logic is a non-monotonic logic designed for efficient defeasible reasoning by Nute (see Chapter 2). Those kind of applications are useful in laws area especially if they offer an implementation of an argumentation framework that provides a formal modelling of game. Roughly speaking, let the theory is the set of laws, a keyclaim is the conclusion that one of the party wants to prove (and the other one wants to defeat) and adding dynamic assertion of rules, namely, facts putted forward by the parties, then, we can play an argumentative challenge between two players and decide if the conclusion is provable or not depending on the different strategies performed by the players. Implementing a game model requires one more meta-interpreter able to evaluate the defeasible logic framework; indeed, according to Göedel theorem (see on page 127), we cannot evaluate the meaning of a language using the tools provided by the language itself, but we need a meta-language able to manipulate the object language8. Thus, rather than a simple meta-interpreter, we propose a Meta-level containing different Meta-evaluators. The former has been explained above, the second one is needed to perform the game model, and the last one will be used to change game execution and tree derivation strategies.

Towards model driven software development for Arduino platforms: a DSL and automatic code generation

La tesi ha lo scopo di esplorare la produzione di sistemi software per Embedded Systems mediante l'utilizzo di tecniche relative al mondo del Model Driven Software Development. La fase più importante dello sviluppo sarà la definizione di un Meta-Modello che caratterizza i concetti fondamentali relativi agli embedded systems. Tale modello cercherà di astrarre dalla particolare piattaforma utilizzata ed individuare quali astrazioni caratterizzano il mondo degli embedded systems in generale. Tale meta-modello sarà quindi di tipo platform-independent. Per la generazione automatica di codice è stata adottata una piattaforma di riferimento, cioè Arduino. Arduino è un sistema embedded che si sta sempre più affermando perché coniuga un buon livello di performance ed un prezzo relativamente basso. Tale piattaforma permette lo sviluppo di sistemi special purpose che utilizzano sensori ed attuatori di vario genere, facilmente connessi ai pin messi a disposizione. Il meta-modello definito è un'istanza del meta-metamodello MOF, definito formalmente dall'organizzazione OMG. Questo permette allo sviluppatore di pensare ad un sistema sotto forma di modello, istanza del meta-modello definito. Un meta-modello può essere considerato anche come la sintassi astratta di un linguaggio, quindi può essere definito da un insieme di regole EBNF. La tecnologia utilizzata per la definizione del meta-modello è stata Xtext: un framework che permette la scrittura di regole EBNF e che genera automaticamente il modello Ecore associato al meta-modello definito. Ecore è l'implementazione di EMOF in ambiente Eclipse. Xtext genera inoltre dei plugin che permettono di avere un editor guidato dalla sintassi, definita nel meta-modello. La generazione automatica di codice è stata realizzata usando il linguaggio Xtend2. Tale linguaggio permette di esplorare l'Abstract Syntax Tree generato dalla traduzione del modello in Ecore e di generare tutti i file di codice necessari. Il codice generato fornisce praticamente tutta la schematic part dell'applicazione, mentre lascia all'application designer lo sviluppo della business logic. Dopo la definizione del meta-modello di un sistema embedded, il livello di astrazione è stato spostato più in alto, andando verso la definizione della parte di meta-modello relativa all'interazione di un sistema embedded con altri sistemi. Ci si è quindi spostati verso un ottica di Sistema, inteso come insieme di sistemi concentrati che interagiscono. Tale difinizione viene fatta dal punto di vista del sistema concentrato di cui si sta definendo il modello. Nella tesi viene inoltre introdotto un caso di studio che, anche se abbastanza semplice, fornisce un esempio ed un tutorial allo sviluppo di applicazioni mediante l'uso del meta-modello. Ci permette inoltre di notare come il compito dell'application designer diventi piuttosto semplice ed immediato, sempre se basato su una buona analisi del problema. I risultati ottenuti sono stati di buona qualità ed il meta-modello viene tradotto in codice che funziona correttamente.

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%.