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Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt.
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.
Wie kann man das Thema Forschungsdatenmanagement (FDM) konkret und anwendbar für Forschende gestalten, die bisher noch wenig Kontakt damit hatten? Auf diese Frage gibt das Konzept „30 Minuten FDM für HAW. Ein Informationsformat für Forschende an HAW in NRW“ eine Antwort. Es entstand als Projektarbeit im Zertifikatskurs Forschungsdatenmanagement 2023/24
Um die Forschungsdatenmanagement-Plattform Coscine optimal für Forschungsprojekte nutzen zu können, ist es sinnvoll, einige Fragen im Vorhinein zu klären. So können aufwendige Änderungen der Datenverwaltung im Nachhinein vermieden werden. Hierzu bietet die Handreichung hilfreiche Leitfragen und Erläuterungen für Forschende und FDM-Service-Personal an HAW in NRW (DH.NRW-Hochschulen).
FDM-Service-Mitarbeitende können die Handreichung in ihrer Beratung zu Coscine einsetzen und mit der Eingabemaske in der Kopfzeile des Dokuments auf ihre Hochschule anpassen.
To successfully develop and introduce concrete artificial intelligence (AI) solutions in operational practice, a comprehensive process model is being tested in the WIRKsam joint project. It is based on a methodical approach that integrates human, technical and organisational aspects and involves employees in the process. The chapter focuses on the procedure for identifying requirements for a work system that is implementing AI in problem-driven projects and for selecting appropriate AI methods. This means that the use case has already been narrowed down at the beginning of the project and must be completely defined in the following. Initially, the existing preliminary work is presented. Based on this, an overview of all procedural steps and methods is given. All methods are presented in detail and good practice approaches are shown. Finally, a reflection of the developed procedure based on the application in nine companies is given.
Stand 01.01.2022 sind in Deutschland 618.460 elektrisch angetriebene KFZ zugelassen. Insgesamt sind derzeit 48.540.878 KFZ zugelassen, was einer Elektromobilitätsquote von ca. 1,2 % entspricht. Derzeit werden Elektromobile über Ladestationen oder Steckdosen mit dem Stromnetz verbunden und üblicherweise mit der vollen Ladekapazität des Anschlusses aufgeladen, bis das Batteriemanagementsystem des Fahrzeugs abhängig vom Ladezustand der Batterie die Ladeleistung reduziert.
Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.
Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.
Modern implementations of driver assistance systems are evolving from a pure driver assistance to a independently acting automation system. Still these systems are not covering the full vehicle usage range, also called operational design domain, which require the human driver as fall-back mechanism. Transition of control and potential minimum risk manoeuvres are currently research topics and will bridge the gap until full autonomous vehicles are available. The authors showed in a demonstration that the transition of control mechanisms can be further improved by usage of communication technology. Receiving the incident type and position information by usage of standardised vehicle to everything (V2X) messages can improve the driver safety and comfort level. The connected and automated vehicle’s software framework can take this information to plan areas where the driver should take back control by initiating a transition of control which can be followed by a minimum risk manoeuvre in case of an unresponsive driver. This transition of control has been implemented in a test vehicle and was presented to the public during the IEEE IV2022 (IEEE Intelligent Vehicle Symposium) in Aachen, Germany.