In the top journal IEEE Transactions on Software Engineering (TSE) a paper on a new verification approach is in print (DOI: 10.1109/TSE.2021.3110191), which is authored by us. This approach is characterized by the following:
Modeling of objects in the physical model through Object Life Cycles
Connecting Object Life Cycles with Activity diagrams based on Semantic Action Specifications
Automated transfer of these models into the code of the nuXmv tool for model checking with Model-driven Transformation
General formal criteria for Consistency in Time Logic
The ICT would like to congratulate Dipl.-Ing. Matthias Bittner for receiving the faculty award of the Faculty of Electrical Engineering and Information Technology for the presentation of his master thesis
Pattern Recognition in a heterogeneous Smart Grid environment
The ongoing increase of decentralized mostly renewable energy producers as well as the growth of electromobility pose big challenges for our future power grid. The Energy&IT group of ICT is actively developing new concepts and solutions and is therefore particularly proud of the successful Smart Grid research cooperation with Siemens AG Austria, which made this master thesis possible.
Matthias will also continue to work with us in the future as part of his PhD in the ICT’s CD Lab for Embedded Machine Learning, and we look forward to further exciting projects and a further strengthening of the collaboration with Siemens.
Abstract of the thesis: Considering a Smart Grid and just observing the sampled grid measurements is an old-fashioned and outdated way of looking at this highly dynamical and heterogeneous system. There is a strong need of involving the environmental (e.g., weather, seasonal behaviour) and heterogeneous (e.g., diverse energy sources and consumers) influences into their analysis and optimization. This thesis is therefore starting at a very abstract viewpoint of such a Smart Grid and proposes: a pipeline for extracting patterns and a design cycle for developing Machine Learning concepts. The pattern extraction pipeline provides methods and concepts for extracting patterns related to the environmental and heterogeneous influences. This step of revealing and extracting patterns is achieved by applying this pipeline on historical data of an existing testbed in Aspern Vienna, Austria. The second part of this thesis is then focused on proposing a Machine Learning design cycle, which provides a general methodology for developing Machine Learning concepts based on the extracted patterns. This results in concepts for power consumption forecasting based on environmental data, system state clustering and rare event detection. The overall aim of all these concepts is to optimize the functionality, reliability and efficiency of the modern Smart Grids.
Supervisors: Dipl.-Ing. Daniel Hauer, Ao.Univ.Prof. Dr. Thilo Sauter
The 2021 edition of the International Symposium on Communications for Energy Systems (ComForEn) in Vienna has been performed in a hybrid setting. It was attended by stakeholders and participants to thematically related R&D projects. For two days, the 70 participants listened to 21 presentations and workshops on “Design”, “How to Energy Community?”, “ICT Solutions for Energy Communities across Europe”, “Reduction of the complexity of ICT systems” and a Workshop on “BIFROST – A narrative simulation tool for Smart Energy scenarios”. The Institute of Computer Technology at TU Wien was represented with presentations, challenge participations, and a workshop.
At the Institute of Computer Technology, in the research area Embedded Machine Learning, there are vacant positions for Project Assistants (Post-Doc and PhD student), from 01.01.2022, with the following scope of responsibility.
Research and project work in the area of Machine Learning and Embedded Systems
Supervision of students in the area of Machine Learning, Embedded Systems, Data Science, and Computer Vision
Collaboration in organizational and administrative tasks in a Christian Doppler Laboratory
Der Chipmangel in vielen Teilen der Welt hat auch vor dem ICT nicht Halt gemacht. Das für die Lehrveranstaltungen “Mikrocomputer VU” und “Mikrocomputer für Informatiker_innen VU” verwendete Entwicklungsboard NUCLEO-F334R8 ist bei unseren Distributoren nicht mehr erhältlich. Durch seine großzügige Unterstützung hat der Hersteller selbst mitgeholfen, dass auch in diesem Wintersemester die LVAs wie geplant durchgeführt werden können – inklusive Verwendung des Boards! Danke an STMicroelectronics!
Das Daumendrücken hat sich ausgezahlt: Das Institut für Computertechnik wurde bei den diesjährigen Best Teaching Awards gleich mit zwei Preisen ausgezeichnet! Wir gratulieren Dr. Friedrich Bauer und Dipl.-Ing. Daniel Hauer zum Best Teacher und zur Best Distance Lecture (für die LVA “Mikrocomputer Labor”). Danke an alle Studierenden für Ihre Stimme! https://www.tuwien.at/tu-wien/aktuelles/news/das-waren-die-best-teaching-awards-2021
The Institute of Computer Engineering is again strongly represented at the 5th edition of the Best Teaching Awards. A total of three courses (Microcomputer Lab, Digital Systems VO, Digital Systems UE) have been nominated for the Best Distance Lecture Award and two lecturers for the Best Teacher Award! We are looking forward to the price-giving on October 7 and keep our fingers crossed for our nominees FriedrichBauer and DanielHauer!
Das halbjährliche Institutstreffen am 23. März 2021 hat auch diesmal wieder unter Pandemiebedingungen als GotoMeeting stattgefunden. Das Hauptthema war die Vorstellung neuer und laufender Projekte. Insgesamt sind zur Zeit 32 drittmittelfinanzierte Projekte mit einem Gesamtvolumen von 940.000 Euro aktiv.