Volume 1 – Symposium: Tuesday, March 8

Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Components:Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Component

Ernst Denert Award for Software Engineering 2019

This open access book provides an overview of the dissertations of the five nominees for the Ernst Denert Award for Software Engineering in 2019. The prize, kindly sponsored by the Gerlind & Ernst Denert Stiftung, is awarded for excellent work within the discipline of Software Engineering, which includes methods, tools and procedures for better and efficient development of high quality software. An essential requirement for the nominated work is its applicability and usability in industrial practice. The book contains five papers describing the works by Sebastian Baltes (U Trier) on Software Developers’Work Habits and Expertise, Timo Greifenberg’s thesis on Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte, Marco Konersmann’s (U Duisburg-Essen) work on Explicitly Integrated Architecture, Marija Selakovic’s (TU Darmstadt) research about Actionable Program Analyses for Improving Software Performance, and Johannes Späth’s (Paderborn U) thesis on Synchronized Pushdown Systems for Pointer and Data-Flow Analysis – which actually won the award. The chapters describe key findings of the respective works, show their relevance and applicability to practice and industrial software engineering projects, and provide additional information and findings that have only been discovered afterwards, e.g. when applying the results in industry. This way, the book is not only interesting to other researchers, but also to industrial software professionals who would like to learn about the application of state-of-the-art methods in their daily work

Timing in Technischen Sicherheitsanforderungen für Systementwürfe mit heterogenen Kritikalitätsanforderungen

Traditionally, timing requirements as (technical) safety requirements have been avoided through clever functional designs. New vehicle automation concepts and other applications, however, make this harder or even impossible and challenge design automation for cyber-physical systems to provide a solution. This thesis takes upon this challenge by introducing cross-layer dependency analysis to relate timing dependencies in the bounded execution time (BET) model to the functional model of the artifact. In doing so, the analysis is able to reveal where timing dependencies may violate freedom from interference requirements on the functional layer and other intermediate model layers. For design automation this leaves the challenge how such dependencies are avoided or at least be bounded such that the design is feasible: The results are synthesis strategies for implementation requirements and a system-level placement strategy for run-time measures to avoid potentially catastrophic consequences of timing dependencies which are not eliminated from the design. Their applicability is shown in experiments and case studies. However, all the proposed run-time measures as well as very strict implementation requirements become ever more expensive in terms of design effort for contemporary embedded systems, due to the system's complexity. Hence, the second part of this thesis reflects on the design aspect rather than the analysis aspect of embedded systems and proposes a timing predictable design paradigm based on System-Level Logical Execution Time (SL-LET). Leveraging a timing-design model in SL-LET the proposed methods from the first part can now be applied to improve the quality of a design -- timing error handling can now be separated from the run-time methods and from the implementation requirements intended to guarantee them. The thesis therefore introduces timing diversity as a timing-predictable execution theme that handles timing errors without having to deal with them in the implemented application. An automotive 3D-perception case study demonstrates the applicability of timing diversity to ensure predictable end-to-end timing while masking certain types of timing errors.Traditionell wurden Timing-Anforderungen als (technische) Sicherheitsanforderungen durch geschickte funktionale Entwürfe vermieden. Neue Fahrzeugautomatisierungskonzepte und Anwendungen machen dies jedoch schwieriger oder gar unmöglich; Aufgrund der Problemkomplexität erfordert dies eine Entwurfsautomatisierung für cyber-physische Systeme heraus. Diese Arbeit nimmt sich dieser Herausforderung an, indem sie eine schichtenübergreifende Abhängigkeitsanalyse einführt, um zeitliche Abhängigkeiten im Modell der beschränkten Ausführungszeit (BET) mit dem funktionalen Modell des Artefakts in Beziehung zu setzen. Auf diese Weise ist die Analyse in der Lage, aufzuzeigen, wo Timing-Abhängigkeiten die Anforderungen an die Störungsfreiheit auf der funktionalen Schicht und anderen dazwischenliegenden Modellschichten verletzen können. Für die Entwurfsautomatisierung ergibt sich daraus die Herausforderung, wie solche Abhängigkeiten vermieden oder zumindest so eingegrenzt werden können, dass der Entwurf machbar ist: Das Ergebnis sind Synthesestrategien für Implementierungsanforderungen und eine Platzierungsstrategie auf Systemebene für Laufzeitmaßnahmen zur Vermeidung potentiell katastrophaler Folgen von Timing-Abhängigkeiten, die nicht aus dem Entwurf eliminiert werden. Ihre Anwendbarkeit wird in Experimenten und Fallstudien gezeigt. Allerdings werden alle vorgeschlagenen Laufzeitmaßnahmen sowie sehr strenge Implementierungsanforderungen für moderne eingebettete Systeme aufgrund der Komplexität des Systems immer teurer im Entwurfsaufwand. Daher befasst sich der zweite Teil dieser Arbeit eher mit dem Entwurfsaspekt als mit dem Analyseaspekt von eingebetteten Systemen und schlägt ein Entwurfsparadigma für vorhersagbares Timing vor, das auf der System-Level Logical Execution Time (SL-LET) basiert. Basierend auf einem Timing-Entwurfsmodell in SL-LET können die vorgeschlagenen Methoden aus dem ersten Teil nun angewandt werden, um die Qualität eines Entwurfs zu verbessern -- die Behandlung von Timing-Fehlern kann nun von den Laufzeitmethoden und von den Implementierungsanforderungen, die diese garantieren sollen, getrennt werden. In dieser Arbeit wird daher Timing Diversity als ein Thema der Timing-Vorhersage in der Ausführung eingeführt, das Timing-Fehler behandelt, ohne dass sie in der implementierten Anwendung behandelt werden müssen. Anhand einer Fallstudie aus dem Automobilbereich (3D-Umfeldwahrnehmung) wird die Anwendbarkeit von Timing-Diversität demonstriert, um ein vorhersagbares Ende-zu-Ende-Timing zu gewährleisten und gleichzeitig in der Lage zu sein, bestimmte Arten von Timing-Fehlern zu maskieren

STAB-Jahresbericht 2023 - Proceedings of the 21st STAB-Workshop 2023 in Göttingen

Die STAB-Jahresberichte werden als Sammlung der Kurzfassungen der Vorträge an die Teilnehmer der abwechselnd stattfindenden Symposien und Workshops verteilt. So erscheint der vorliegende STAB-Jahresbericht 2023 anlässlich des 21. STAB-Workshops, der am 7. und 8. November 2023 in Göttingen stattfinden wird. Der Bericht enthält 77 Mitteilungen über Arbeiten aus den Fachgruppen, die auf dem Workshop vorgestellt werden. Den Mitteilungen ist ein Inhaltsverzeichnis (Seite 14 bis Seite 19) vorangestellt, das nach Fachgruppen gegliedert ist. Innerhalb der Fachgruppen sind die Beiträge alphabetisch nach Autoren geordnet. Die Beiträge verteilen sich (bezogen auf die Erstautoren) zu 4 % auf die Industrie, zu 39 % auf Hochschulen und zu 57 % auf Forschungseinrichtungen (DLR, DNW, ISL). Die Autoren und Koautoren dieses Berichts sind auf den Seiten 174 und 175 aufgeführt

On the connection of probabilistic model checking, planning, and learning for system verification

This thesis presents approaches using techniques from the model checking, planning, and learning community to make systems more reliable and perspicuous. First, two heuristic search and dynamic programming algorithms are adapted to be able to check extremal reachability probabilities, expected accumulated rewards, and their bounded versions, on general Markov decision processes (MDPs). Thereby, the problem space originally solvable by these algorithms is enlarged considerably. Correctness and optimality proofs for the adapted algorithms are given, and in a comprehensive case study on established benchmarks it is shown that the implementation, called Modysh, is competitive with state-of-the-art model checkers and even outperforms them on very large state spaces. Second, Deep Statistical Model Checking (DSMC) is introduced, usable for quality assessment and learning pipeline analysis of systems incorporating trained decision-making agents, like neural networks (NNs). The idea of DSMC is to use statistical model checking to assess NNs resolving nondeterminism in systems modeled as MDPs. The versatility of DSMC is exemplified in a number of case studies on Racetrack, an MDP benchmark designed for this purpose, flexibly modeling the autonomous driving challenge. In a comprehensive scalability study it is demonstrated that DSMC is a lightweight technique tackling the complexity of NN analysis in combination with the state space explosion problem.Diese Arbeit präsentiert Ansätze, die Techniken aus dem Model Checking, Planning und Learning Bereich verwenden, um Systeme verlässlicher und klarer verständlich zu machen. Zuerst werden zwei Algorithmen für heuristische Suche und dynamisches Programmieren angepasst, um Extremwerte für Erreichbarkeitswahrscheinlichkeiten, Erwartungswerte für Kosten und beschränkte Varianten davon, auf generellen Markov Entscheidungsprozessen (MDPs) zu untersuchen. Damit wird der Problemraum, der ursprünglich mit diesen Algorithmen gelöst wurde, deutlich erweitert. Korrektheits- und Optimalitätsbeweise für die angepassten Algorithmen werden gegeben und in einer umfassenden Fallstudie wird gezeigt, dass die Implementierung, namens Modysh, konkurrenzfähig mit den modernsten Model Checkern ist und deren Leistung auf sehr großen Zustandsräumen sogar übertrifft. Als Zweites wird Deep Statistical Model Checking (DSMC) für die Qualitätsbewertung und Lernanalyse von Systemen mit integrierten trainierten Entscheidungsgenten, wie z.B. neuronalen Netzen (NN), eingeführt. Die Idee von DSMC ist es, statistisches Model Checking zur Bewertung von NNs zu nutzen, die Nichtdeterminismus in Systemen, die als MDPs modelliert sind, auflösen. Die Vielseitigkeit des Ansatzes wird in mehreren Fallbeispielen auf Racetrack gezeigt, einer MDP Benchmark, die zu diesem Zweck entwickelt wurde und die Herausforderung des autonomen Fahrens flexibel modelliert. In einer umfassenden Skalierbarkeitsstudie wird demonstriert, dass DSMC eine leichtgewichtige Technik ist, die die Komplexität der NN-Analyse in Kombination mit dem State Space Explosion Problem bewältigt

Aircraft Noise

Noise generated by aircraft continues to be a pressing issue for society, as an increasing number of people residing in close proximity to airports make noise complaints on a regular basis. The reduction in aircraft noise is therefore a very important engineering task that would require the careful identification of different acoustic sources around the airplane, the understanding of noise source behavior and ranking along flight trajectories, sophisticated measurement techniques, and robust and accurate numerical tools aimed at predicting the generation of noise, the propagation through the atmosphere, and the resulting noise impact along approach and departure flights. For an overall assessment of the situation, it has to be assessed along entire flight trajectories rather than assessing limited operating conditions only. Furthermore, it is highly recommended to apply multiple acoustic metrics and account for different and widespread observer locations along the flight. Only then can the overall situation be adequately captured. Obviously, this is a highly multidisciplinary effort and no single discipline can address this problem. This reprint includes selected research studies with that multidisciplinary context that deal with numerical or experimental investigations that range from the investigation of specific noise sources to the assessment of noise generated by the overall aircraft in operation. Both basic and applied research studies involving the modelling and simulation of aircraft noise are included

Scalability Engineering for Parallel Programs Using Empirical Performance Models

Performance engineering is a fundamental task in high-performance computing (HPC). By definition, HPC applications should strive for maximum performance. As HPC systems grow larger and more complex, the scalability of an application has become of primary concern. Scalability is the ability of an application to show satisfactory performance even when the number of processors or the problems size is increased. Although various analysis techniques for scalability were suggested in past, engineering applications for extreme-scale systems still occurs ad hoc. The challenge is to provide techniques that explicitly target scalability throughout the whole development cycle, thereby allowing developers to uncover bottlenecks earlier in the development process. In this work, we develop a number of fundamental approaches in which we use empirical performance models to gain insights into the code behavior at higher scales. In the first contribution, we propose a new software engineering approach for extreme-scale systems. Specifically, we develop a framework that validates asymptotic scalability expectations of programs against their actual behavior. The most important applications of this method, which is especially well suited for libraries encapsulating well-studied algorithms, include initial validation, regression testing, and benchmarking to compare implementation and platform alternatives. We supply a tool-chain that automates large parts of the framework, thus allowing it to be continuously applied throughout the development cycle with very little effort. We evaluate the framework with MPI collective operations, a data-mining code, and various OpenMP constructs. In addition to revealing unexpected scalability bottlenecks, the results also show that it is a viable approach for systematic validation of performance expectations. As the second contribution, we show how the isoefficiency function of a task-based program can be determined empirically and used in practice to control the efficiency. Isoefficiency, a concept borrowed from theoretical algorithm analysis, binds efficiency, core count, and the input size in one analytical expression, thereby allowing the latter two to be adjusted according to given (realistic) efficiency objectives. Moreover, we analyze resource contention by modeling the efficiency of contention-free execution. This allows poor scaling to be attributed either to excessive resource contention overhead or structural conflicts related to task dependencies or scheduling. Our results, obtained with applications from two benchmark suites, demonstrate that our approach provides insights into fundamental scalability limitations or excessive resource overhead and can help answer critical co-design questions. Our contributions for better scalability engineering can be used not only in the traditional software development cycle, but also in other, related fields, such as algorithm engineering. It is a field that uses the software engineering cycle to produce algorithms that can be utilized in applications more easily. Using our contributions, algorithm engineers can make informed design decisions, get better insights, and save experimentation time

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Obsessed by a Dream

This Open Access biography chronicles the life and achievements of the Norwegian engineer and physicist Rolf Widerøe. Readers who meet him in the pages of this book will wonder why he isn't better known. The first of Widerøe's many pioneering contributions in the field of accelerator physics was the betatron, the second, the linear accelerator, both summarized in a 27 page PhD. The betatron revolutionized the fields of cancer treatment through radiation therapy and also nondestructive testing; hospitals worldwide installed Widerøe's machine and today’s modern radiation treatment equipment is based on his inventions. The most recent renaissance of the linac provides unprecedented x-ray intensities at Free Electron Laser (FEL) facilities in operation and construction worldwide. . Widerøe’s story also includes a fair share of drama, particularly during World War II when both Germans and the Allies vied for his collaboration. Widerøe held leading positions in multinational industry groups and was one of the consultants for building the world's largest nuclear laboratory, CERN, in Switzerland. He gained over 200 patents, received several honorary doctorates and a number of international awards. The author, a professional writer and maker of TV documentaries, has gained access to hitherto restricted archives in several countries, which provided a wealth of new material and insights, in particular in relation to the war years. She tells here a gripping and illuminating story