Bedarfsorientierter ÖPNV im ländlichen Raum - Simulationsstudie und Potentialanalyse

Im DLR-Projekt MOVEMENT wurden flexible, bedarfsorientierter öffentlicher Personennahverkehr im ländlichen Raum erforscht. Dieser Vortrag zeigt Ergebnisse aus durchgeführten Simulationsstudien, um das Potential von On-Demand-ÖV zu analysieren

Deliverable D6.4: Assessment report: Experimenting with CONNECT in Systems of Systems, and Mobile Environments

The core objective of WP6 is to evaluate the CONNECT technologies under realistic situations. To achieve this goal, WP6 concentrated a significant amount of its 4th year effort on the finalization of the implementation of the GMES scenario defined during the 3rd year. The GMES scenario allows the consortium to assess the validity of CONNECT claims and to investigate the exploitation of CONNECT technologies to deal with the integration of real systems. In particular, GMES requires the connection of highly heterogeneous and independently built systems provided by the industry partners. WP6 contributed also in providing mobile collaborative applications and case studies showing the exploitation of CONNECTORs on mobile devices

Computer Science Exercises in a Virtual University

Abstract—In distance teaching, direct feedback to the students is difficult to give, but crucial for their learning success. The system Asterix was developed in order to support this direct feedback. It contains different interactive tasks from the area of the formal foundations of computer science. Asterix is available over an internet interface. The implementation of the interactive presentation and correction of the tasks is generated automatically from high-level XML specifications. In this work we give a detailed overview of the system architecture and the supported task types, both, from the students ’ and the teacher’s point of view and we show how Asterix is used in the context of distance teaching at the FernUniversität in Hagen. The presentation is driven by a series of real world examples. Index Terms—distance learning, self-test tasks, formal foundations of computer science, XML-specifications I

Active Learning of Decomposable Systems

Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systems---with a decomposable set of actions---that make learning in parts possible. The systems are themselves decomposable through non-intersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry

Automatically Learning Formal Models: An Industrial Case from Autonomous Driving Development

The correctness of autonomous driving software is of utmost importance as incorrect behaviour may have catastrophic consequences. Though formal model-based engineering techniques can help guarantee correctness, challenges exist in widespread industrial adoption. One among them is the model construction problem. Manual construction of formal models is expensive, error-prone, and intractable for large systems. Automating model construction would be a great enabler for the use of formal methods to guarantee software correctness and thereby for safe deployment of autonomous vehicles. Such automated techniques can be beneficial in software design, re-engineering, and reverse engineering. In this industrial case study, we apply active learning techniques to obtain formal models from an existing autonomous driving software (in development) implemented in MATLAB. We demonstrate the feasibility of active automata learning algorithms for automotive industrial use. Furthermore, we discuss the practical challenges in applying automata learning and possible directions for integrating automata learning into automotive software development workflow

Combining Black-Box and White-Box Techniques for Learning Register Automata

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