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Institute
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (46)
- Fakultät Maschinenbau und Verfahrenstechnik (M+V) (34)
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- ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik (13)
- IBMS - Institute for Advanced Biomechanics and Motion Studies (ab 16.11.2022) (12)
- INES - Institut für nachhaltige Energiesysteme (12)
- Fakultät Medien (M) (ab 22.04.2021) (10)
- IUAS - Institute for Unmanned Aerial Systems (3)
- POIM - Peter Osypka Institute of Medical Engineering (2)
With recent developments in the Ukrainian-Russian conflict, many are discussing about Germany’s dependency on fossil fuel imports in its energy system, and how can the country proceed with reducing that dependency. With its wide-ranging consumption sectors, the electricity sector comes as the perfect choice to start with. Recent reports showed that the German federal government is already intending to have a fully renewable electricity by 2035 while exploiting all possible clean power options. This was published in the federal government’s climate emergency program (Easter Package) in early 2022. The aim of this package is to initiate a rapid transition and decarbonization of the electricity sector. The Easter Package expects an enormous growth of renewable energies to a completely new level, with already at least 80% renewable gross energy consumption, with extensive and broad deployment of different generation technologies on various scales. This paper will discuss this ambitious plan and outline some insights into this huge and rapidly increasing step, and show how much will Germany need in order to achieve this huge milestone towards a fully green supply of the electricity sector. Different scenarios and shares of renewables will be investigated in order to elaborate on preponed climate-neutral goal of the electricity sector by 2035. The results pointed out some promising aspects in achieving a 100% renewable power, with massive investments in both generation and storage technologies.
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for image classification also have better robustness properties, we investigate whether the improved adversarial robustness of ViTs extends to image restoration. We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer. We use Projected Gradient Descent (PGD) and CosPGD for our robustness evaluation. Our experiments are performed on real-world images from the GoPro dataset for image deblurring. Our analysis indicates that contrary to as advocated by ViTs in image classification works, these models are highly susceptible to adversarial attacks. We attempt to find an easy fix and improve their robustness through adversarial training. While this yields a significant increase in robustness for Restormer, results on other networks are less promising. Interestingly, we find that the design choices in NAFNet and Baselines, which were based on iid performance, and not on robust generalization, seem to be at odds with the model robustness.
Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.
Investigation on Bowtie Antennas Operating at Very Low Frequencies for Ground Penetrating Radar
(2023)
The efficiency of Ground Penetrating Radar (GPR) systems significantly depends on the antenna performance as the signal has to propagate through lossy and inhomogeneous media. GPR antennas should have a low operating frequency for greater penetration depth, high gain and efficiency to increase the receiving power and should be compact and lightweight for ease of GPR surveying. In this paper, two different designs of Bowtie antennas operating at very low frequencies are proposed and analyzed.
This paper presents a system that uses a multi-stage AI analysis method for determining the condition and status of bicycle paths using machine learning methods. The approach for analyzing bicycle paths includes three stages of analysis: detection of the road surface, investigation of the condition of the bicycle paths, and identification of substrate characteristics. In this study, we focus on the first stage of the analysis. This approach employs a low-threshold data collection method using smartphone-generated video data for image recognition, in order to automatically capture and classify surface condition and status.
For the analysis convolutional neural networks (CNN) are employed. CNNs have proven to be effective in image recognition tasks and are particularly well-suited for analyzing the surface condition of bicycle paths, as they can identify patterns and features in images. By training the CNN on a large dataset of images with known surface conditions, the network can learn to identify common features and patterns and reliably classify them.
The results of the analysis are then displayed on digital maps and can be utilized in areas such as bicycle logistics, route planning, and maintenance. This can improve safety and comfort for cyclists while promoting cycling as a mode of transportation. It can also assist authorities in maintaining and optimizing bicycle paths, leading to more sustainable and efficient transportation system.
Team description papers of magmaOffenburg are incremental in the sense that each year we address a different topic of our team and the tools around our team. In this year’s team description paper we focus on the architecture of the software. It is a main factor for being able to keep the code maintainable even after 15 years of development. We also describe how we make sure that the code follows this architecture.
Landing heel first has been associated with elevated external knee abduction moments (KAM), thereby potentially increasing the risk of sustaining a non-contact ACL injury. Apart from the foot strike angle, knee valgus angle (VAL) and vertical center of mass velocity at initial ground contact (IC) have been associated with increased KAM in females across different sidestep cuts. While real-time biofeedback training has been proven effective for gait retraining [4], the highly dynamic, non-cyclical nature of cutting maneuvers makes real-time feedback unsuitable and alternative approaches necessary. This study aimed at assessing the efficacy of immediate software-aided feedback on cutting technique in reducing KAM during handball-specific cutting maneuvers.
In 2015, Google engineer Alexander Mordvintsev presented DeepDream as technique to visualise the feature analysis capabilities of deep neural networks that have been trained on image classification tasks. For a brief moment, this technique enjoyed some popularity among scientists, artists, and the general public because of its capability to create seemingly hallucinatory synthetic images. But soon after, research moved on to generative models capable of producing more diverse and more realistic synthetic images. At the same time, the means of interaction with these models have shifted away from a direct manipulation of algorithmic properties towards a predominance of high level controls that obscure the model's internal working. In this paper, we present research that returns to DeepDream to assess its suit-ability as method for sound synthesis. We consider this research to be necessary for two reasons: it tackles a perceived lack of research on musical applications of DeepDream, and it addresses DeepDream's potential to combine data driven and algorithmic approaches. Our research includes a study of how the model architecture, choice of audio data-sets, and method of audio processing influence the acoustic characteristics of the synthesised sounds. We also look into the potential application of DeepDream in a live-performance setting. For this reason, the study limits itself to models consisting of small neural networks that process time-domain representations of audio. These models are resource-friendly enough to operate in real time. We hope that the results obtained so far highlight the attractiveness of Deep-Dream for musical approaches that combine algorithmic investigation with curiosity driven and open ended exploration.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.
Kundendaten im E-Commerce – Optimierungspotenzial im Checkout-Prozess des deutschen Online-Handels
(2023)
Die Gestaltung eines benutzungsfreundlichen Checkout-Prozesses ist für den Erfolg des E-Commerce von großer Bedeutung. Die Abfrage der Kundendaten bildet einen wichtigen Teil der Customer Journey. Auf der einen Seite wollen die Handelsunternehmen so viel wie möglich über ihre Kundschaft erfahren, um möglichst zielgenaue Angebote und Marketingmaßnahmen ausspielen und das perfekte Einkaufserlebnis generieren zu können. Auf der anderen Seite möchten sich die Kundinnen und Kunden beim Online-Shopping auf den Kauf konzentrieren und erwarten einen reibungslosen Ablauf. Der Checkout-Prozess ist in diesem Zusammenhang ein kritischer Punkt. Dies spiegelt sich auch in den hohen Warenkorbabbruchraten wider. Um Online-Shoppende nachhaltig zu begeistern, gibt es noch viel Raum für Verbesserungen. Mit dem Ziel, den Status quo im deutschen Online-Handel besser zu verstehen und Usability und User Experience für eine höhere Konvertierungsrate zu optimieren, untersuchte die hier vorgestellte Forschungsarbeit den Anmelde- und Checkout-Prozess der 100 umsatzstärksten Online-Shops in Deutschland. Es werden die Ergebnisse der Studie präsentiert und aufgezeigt, an welchen Stellen Optimierungspotenzial besteht – bspw. bei zu komplizierten Formularen, unnötigen Datenabfragen oder erzwungenen Registrierungen – sowie Vorschläge für die Praxis des Online-Handels diskutiert.
The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.
The paper will focus on the activities of the International Year of Light and Optical Technologies 2015 (IYL) with their impact in life, science, art, culture, education and outreach as well as the importance in promoting the objectives for sustainable development. It describes our activities carried out in the run-up to or during the IYL, as well as reports on the generic projects that led to the success of the IYL. The success of the IYL is illustrated by examples and statistics. Relating to the potential and success of the IYL, the impact and the genesis of the International Day of Light (IDL) is presented. Impressions from the opening ceremony of the IYL in Paris at UNESCO headquarters and the Inaugural Ceremony of the IDL will then be covered. A second focus is placed on the interdisciplinary media projects realized by the students of our university dedicated to these events. Finally, an analysis of the impact and legacy of IYL and IDL will be presented.
In this paper we report on further success of our work to develop a multi-method energy optimization which works with a digital twin concept. The twin concept serves to replicate production processes of different kinds of production companies, including complex energy systems and test market interactions to then use them for model predictive optimizing. The presented work finally reports about the performed flexibility assessment leading to a flexibility audit with a list of measures and the impact of energy optimizations made related to interactions with the local power grid i.e., the exchange node of the low voltage distribution grid. The analysis and continuous exploration of flexibilities as well as the exchange with energy markets require a “guide” leading to continuous optimization with a further tool like the Flexibility Survey and Control Panel helping decision-making processes on the day-ahead horizon for real production plants or the investment planning to improve machinery, staff schedules and production
infrastructure.
The present work describes an extension of current slope estimation for parameter estimation of permanent magnet synchronous machines operated at inverters. The area of operation for current slope estimation in the individual switching states of the inverter is limited due to measurement noise, bandwidth limitation of the current sensors and the commutation processes of the inverter's switching operations. Therefore, a minimum duration of each switching state is necessary, limiting the final area of operation of a robust current slope estimation. This paper presents an extension of existing current slope estimation algorithms resulting in a greater area of operation and a more robust estimation result.
Voice User Experience
(2023)
Sprachassistenten wie Alexa, Google Assistant, Siri, Cortana, Magenta und Bixby erfreuen sich dank ihrer intuitiven, schnellen und bequemen Interaktionsmöglichkeiten zunehmender Beliebtheit und bieten deshalb spannende Möglichkeiten für die Weiterentwicklung des digitalen Kundendialogs. Doch ob die Technologie wirklich breite Akzeptanz finden wird, hängt nicht nur mit ihrer technischen Qualität oder Usability zusammen. Auch die User Experience, die neben den Reaktionen der Nutzer*innen während der Anwendung auch ihre Erwartungen und Wahrnehmungen vor und nach der Anwendung umfasst, spielt eine zentrale Rolle. Die Messung der Qualität der Voice User Experience (Voice UX) ist daher von großem Interesse für die Bewertung und Optimierung von Sprachapplikationen. Die Frage, wie die Voice UX von sprachgesteuerten Systemen gemessen werden kann, ist jedoch noch offen. Aktuelle Methoden stützen sich häufig auf UX-Forschung zu grafischen Benutzeroberflächen, obwohl die sprachbasierten Interaktionsformen in der Regel weder visuell noch haptisch greifbar sind. In unserem Beitrag möchten wir den aktuellen Status quo der deutschen Voice User Experience untersuchen. Folgende Fragen stehen dabei im Mittelpunkt: Wie können Sprachanwendungen zu einem erfolgreichen Kundendialog beitragen? Welche Nutzerirritationen treten aktuell bei der Anwendung von Sprachassistenten auf? Mit welchen Methoden lässt sich die Voice User Experience messen?
In order to attract new students, German universities must provide quick and easy access to relevant information. A chatbot can help increase the efficiency in academic advising for prospective students. In this study we evaluate the acceptance and effects of chatbots in German student-university communication. We conducted a qualitative UX-Study with the chatbot prototype of Offenburg University of Applied Sciences (HSO), in order to determine which features are particularly relevant and which requirements are made by the users. The results show that acceptance increases if the chatbot offers quick and adequate assistance, furthermore, our participants preferred an informal communication style and valued friendly and helpful personality traits for chatbots.
Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In this work, we employ a generative solution, since it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for multiple removal. To that end, we run experiments on synthetic and on real data, and we compare the deep diffusion performance with standard algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.