Journal ArticleDOI
Enabling the usage of UML in the verification of railway systems : The DAM-rail approach
Simona Bernardi,Francesco Flammini,Stefano Marrone,Nicola Mazzocca,José Merseguer,Roberto Nardone,Valeria Vittorini +6 more
TLDR
This paper addresses the definition of a Model-Driven approach for the evaluation of RAM attributes in railway applications to automatically generate formal models and shows that the MARTE-DAM framework can be successfully specialized for the railway domain.About:
This article is published in Reliability Engineering & System Safety.The article was published on 2013-12-01. It has received 44 citations till now. The article focuses on the topics: Applications of UML & Model-driven architecture.read more
Citations
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Journal ArticleDOI
An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways' condition, planning and cost
Isidro Sergio Durazo-Cardenas,Andrew Starr,Christopher Turner,Christopher Turner,Ashutosh Tiwari,Ashutosh Tiwari,Leigh Kirkwood,Maurizio Bevilacqua,Antonios Tsourdos,Essam Shehab,Paul Baguley,Yuchun Xu,Christos Emmanouilidis +12 more
TL;DR: The design strategy of a novel in-paralleled system for automatic job scheduling is presented and real track incident and inspection datasets were analyzed to raise gradation alarms that initiate the automatic scheduling of maintenance tasks.
Journal ArticleDOI
A model-driven framework for design and verification of embedded systems through SystemVerilog
TL;DR: UML profile for SystemVerilog (UMLSV) is proposed to model the design and verification requirements of embedded systems in the context of Model Based System Engineering (MBSE) and a temporal extension of Object Constraint Language is used to capture the verification requirements in U MLSV.
Journal ArticleDOI
A multiformalism modular approach to ertms/etcs failure modeling
TL;DR: The results show that the multiformalism modeling approach helps to cope with complexity, eases the verification of availability requirements and can be successfully applied to the analysis of complex critical systems.
Journal ArticleDOI
Towards Model-Driven V&V assessment of railway control systems
TL;DR: The usage of appropriate Unified Modeling Language profiles featuring system analysis and test case specification capabilities, together with tool chains for model transformations and analysis, seems a viable way to allow end-users to concentrate on high-level holistic models and specification of non-functional requirements and support the automation of the V&V process.
Journal ArticleDOI
Synchronization of faulty processors in coarse-grained TMR protected partially reconfigurable FPGA designs
TL;DR: Four different synchronization approaches for soft core processors, which balance differently the trade-off between synchronization speed and hardware overhead are posed.
References
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Basic concepts and taxonomy of dependable and secure computing
TL;DR: The aim is to explicate a set of general concepts, of relevance across a wide range of situations and, therefore, helping communication and cooperation among a number of scientific and technical communities, including ones that are concentrating on particular types of system, of system failures, or of causes of systems failures.
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ATL: A model transformation tool
TL;DR: ATL: a model transformation language and its execution environment based on the Eclipse framework is presented and ATL tools provide support for the major tasks involved in using a language: editing, compiling, executing, and debugging.
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Improving the analysis of dependable systems by mapping fault trees into Bayesian networks
TL;DR: It is shown that any FT can be directly mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed from the former, i.e. reliability of the Top Event or of any sub-system, criticality of components, etc.
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Bayesian networks without tears: making Bayesian networks more accessible to the probabilistically unsophisticated
TL;DR: Bayesian networks as discussed by the authors have become popular within the AI probability and uncertainty community, and it is probably fair to say that Bayesian networks are to a large segment of the AI-uncertainty community what resolution theorem proving is to the AIlogic community.