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Dottorando presso: Dipartimento di Ingegneria "Enzo Ferrari"

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2020 - An Architecture for Predictive Maintenance of Railway Points Based on Big Data Analytics [Relazione in Atti di Convegno]
Salierno, G.; Morvillo, S.; Leonardi, L.; Cabri, G.

Massive amounts of data produced by railway systems are a valuable resource to enable Big Data analytics. Despite its richness, several challenges arise when dealing with the deployment of a big data architecture into a railway system. In this paper, we propose a four-layers big data architecture with the goal of establishing a data management policy to manage massive amounts of data produced by railway switch points and perform analytical tasks efficiently. An implementation of the architecture is given along with the realization of a Long Short-Term Memory prediction model for detecting failures on the Italian Railway Line of Milano - Monza - Chiasso.

2019 - A Survey of the Use of Software Agents in Digital Factories [Relazione in Atti di Convegno]
Bicocchi, N.; Cabri, G.; Leonardi, L.; Salierno, G.

Digital factories represent an abstraction of real factories, which is useful to manage at a high level the processes as well as the interactions inside the factories but also the interactions between factories. This abstraction can automatize several processes and can enable to dynamically adapt the factory production to unexpected situations. Software agents can meet the requirements of digital factories by means of their features of autonomy, reactivity, proactivity and sociality. In this paper, we survey the use of software agents in the context of digital factories, showing how they can be exploited. A discussion about the advantages brought by software agents and the limitation of agent-based approaches completes the paper.

2019 - Different Perspectives of a Factory of the Future: An Overview [Relazione in Atti di Convegno]
Salierno, Giulio; Cabri, Giacomo; Leonardi, Letizia

Digitalfactory,andCloudManufacturingaretwoapproaches that aim at addressing the Factory of the Future, i.e., to provide digital support to manufacturing factories. They find their roots in two different geographical areas, respectively Europe and China, and therefore presents some differences as well as the same goal of building the factory of the future. In this paper, we present both the digital factory and the cloud manufacturing approaches and discuss their differences.

2019 - Intelligent agents supporting digital factories [Relazione in Atti di Convegno]
Bicocchi, N.; Cabri, G.; Leonardi, L.; Salierno, G.

Intelligent agents represent a widely exploited paradigm of the Distributed Artificial Intelligence (DAI). They have been applied in many fields, and recently they have appeared also in the digital factory field. Digital factories are abstractions of real factories, which enable high-level management of factories' processes, along with their automatization. So, the real factories can dynamically adapt their processes to unexpected situations. In this paper, we survey different works at the state of the art that show how intelligent agents can support digital factories, along with the limitations of their application. A discussion about the advantages of intelligent agents and the open issues completes the paper.

2019 - QoS evaluation of constrained cloud manufacturing service composition [Relazione in Atti di Convegno]
Salierno, G.; Cabri, G.; Leonardi, L.

Cloud manufacturing is a paradigm that represents distributed cyber-physical systems in which software abstractions and services work in strict connection to manufacturing equipment and machines. In cloud manufacturing, the cloud platform receives high-level tasks that are decomposed into subtasks, which are fulfilled by appropriate services. Despite different approaches have been proposed to compose services in order to accomplish manufacturing tasks, physical constraints have not been considered.In this paper, we propose an approach that enacts a trade-off between quality of service and physical constraints of manufacturing services in order to adapt to equipment constraints. To show the effectiveness of the proposed approach, we quantitatively compare it with a straightforward approach which does not consider any physical constraint. Experimental results of our algorithm show that the trade-off between quality of service and capacity of physical manufacturing equipment is acceptable for efficient cloud service compositions.