Showing papers by "Roberto Nardone published in 2018"

Towards Railway Virtual Coupling

Abstract: Virtual Coupling adds to Automatic Train Control (ATC) systems the further functionality of being able to virtually connect two or more trains, so drastically reducing their headways and increasing line capacity. For this reason it is considered among the most relevant innovations to be researched within the European Horizon 2020 Shift2Rail Joint Undertaking. Indeed, Virtual Coupling also introduces some critical issues related to potential hazards as well as strict requirements on tolerated latency associated to the channels used for train-to-trackside and train-to-train communications. In this paper, we introduce Virtual Coupling in the context of a standard ATC, that is ERTMS/ETCS. Considering a reference ATC simplifies the discussion about implementation and performance issues. We will provide some preliminary hints, models and results and draw conclusions about required safety analyses and future developments.

A Proposal of an Example and Experiments Repository to Foster Industrial Adoption of Formal Methods

Abstract: Formal methods (in a broad sense) have been around almost since the beginning of computer science. Nonetheless, there is a perception in the formal methods community that take-up by industry is low considering the potential benefits. We take a look at possible reasons and give candidate explanations for this effect. To address the issue, we propose a repository of industry-relevant example problems with an accompanying open data storage for experiment results in order to document, disseminate and compare exemplary solutions from formal model based methods. This would allow potential users from industry to better understand the available solutions and to more easily select and adopt a formal method that fits their needs. At the same time, it would foster the adoption of open data and good scientific practice in this research field.

A Model-Based Evaluation Methodology for Smart Energy Systems

Abstract: The huge amount of data collected everyday for different purposes by a multitude of smart devices enables the delivery of added-value services to end-users by means of smart applications. Among them, the applications devoted to optimizing the energy consumption through smart power grids are gaining more and more attention due to their impact on both the environment and the costs for the users. The design and evaluation of Smart Energy systems is very complex due to the heterogeneity of involved devices and technologies, and to the high variability of energy production and consumption profiles. In this regard, in this paper we propose a model-based methodology for the evaluation of Smart Energy systems, which merges system modeling and cognitive computing techniques to obtain a representation of the systems' behavior that takes into account a data-driven characterization of the workload and of the overall context. Such a representation allows to estimate properties of interest in different operative conditions, and can be profitably used to make design choices and to tune the application behavior during operation based on collected data. In order to demonstrate the effectiveness of our proposal, we present an example Smart Energy system modeled by means of the Stochastic Activity Network (SAN) formalism, and we show how it is possible to perform several analyses on the system configuration by means of model simulations.

Automatic generation of formal models for diagnosability of DES

Abstract: This paper aims at defining a model-driven approach for the diagnosability analysis of discrete event systems (DES). The proposed approach can be adopted during the design of modern control systems, in which many sensors and actuators are employed and the diagnosability of faults within a certain delay could be an issue. The proposal represents a first step towards an automatic model-driven process which derive formal models from a complete high-level specification of DESs. The specification activity of our approach relies on the Dynamic STate Machine (DSTM) formalism, a new language that extends state machines with dynamic instantiation, interrupts and asynchronous communication. The paper will describe how we can automatically derive Petri net and Promela models from the high-level DSTM specification. The former model can be used to apply diagnosability analysis approaches proposed in the DES community, while the latter can be used to apply model checking techniques. An application of the proposed model-driven approach is described by deriving both a PN and a Promela model for the well-known railway level crossing benchmark.