Offenburg University of Applied Sciences
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Élément de préhension destiné à être utilisé en robotique et en cobotique
No full textThe invention relates to a gripper element for use in robotics and cobotics, the gripper element (5a, 5b) being at least partly in the form of a multi-bending beam (9, 19) so that an essentially parallel gripping and in particular direct measuring of the gripping force and a fiber reinforcement is made possible. The invention also relates to a method of producing such a gripper element.Die Erfindung betrifft ein Greifelement zum Einsatz in der Robotik und Kobotik, wobei das Greifelement (5a, 5b) wenigstens teilweise als Mehrfachbiegebalken (9, 19) ausgebildet ist, so dass ein im Wesentlichen paralleles Greifen und insbesondere eine direkte Messung der Greifkraft sowie eine Faserverstärkung ermöglicht wird. Weiterhin betrifft die Erfindung ein Verfahren zur Herstellung eines derartigen Greifelements.L'invention concerne un élément de préhension destiné à être utilisé en robotique et en cobotique, l'élément de préhension (5a, 5b) étant au moins partiellement sous la forme d'une poutre à courbures multiples (9, 19) de telle sorte qu'une préhension sensiblement parallèle et en particulier une mesure directe de la force de préhension et d'un renfort fibreux sont rendues possibles. L'invention concerne également un procédé de fabrication d'un tel élément de préhension
Hands-on optical fiber in live broadcasting
No full textIn this paper, the authors present the planning and realization of the live broadcast of the German Student Surfing Championships in Seignosse, France, using an optical network in a particularly harsh environment
Automotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning
Get PDFThe awareness of different road surface types is crucial for the safe operation of automated vehicles in on- and off-road modes. This paper focuses on the classification of different road surface types using automotive radar and Machine Learning (ML) Artificial Intelligence (AI) models. This analysis is based on the range spectrum of the backscattered radar signals from different road surfaces. The dataset for training and testing is formed by combining the range data from various road surface types. A Random Forest (RF) classifier is built for the identification and classification of four different road surface types. The proposed method is compared using range data obtained from two different mounting positions of the radar. Under dry conditions, a generalization error of 84.5% in the forward-looking position of the radar is achieved. The proposed classifier is also able to distinguish between wet and dry asphalt surfaces with a generalization error of 88.7%
Impact of an interaural place of stimulation mismatch on the binaural interaction component in single sided deaf and bilateral CI users
No full textA Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms
No full textDue to demographic change, health economics is increasingly focused on the quality of life in advanced age and the associated cost aspects. Dementia is one of the key issues in this area and its efficient treatment will become increasingly relevant in the near future. Our system aims to automatically create treatment plans for dementia patients in a digital platform. To this end, three algorithms were implemented: a rule-based approach, an approach based on artificial neural networks, and an approach using large language models. A comprehensive synthetic dataset with fictitious patients, medical problems, and treatments was created for training and evaluation of the algorithms
Extension of the ten golden rules towards aspects of holistic product development
No full textThe ‘Ten Golden Rules’ for promoting sustainability in product development offer a simple and practical method for integrating ecological principles into the development process. This method has proven particularly successful in small and medium-sized enterprises (SMEs) and small development teams, as it provides easy-to-understand and cost-effective guidelines that can be implemented without extensive resources. In essence, the ‘Ten Golden Rules’ aim to minimise material consumption, avoid environmentally harmful substances, promote product longevity and improve reusability and recyclability. In this contribution, an approach is developed to take up the perspective of the Ten Golden Rules and expand on them. In order to be able to use it as an up-to-date holistic project alignment tool. A central aspect of the further development of the method is the greater consideration of social sustainability. This extension ensures that not only ecological, but also ethical and social aspects of the value chain come into focus. This includes fair working conditions and transparency in the supply chain. The promotion of modularity and reparability is also increasingly seen as an important step forward in extending the service life of products and minimising waste
Experimental GPR Water Leakage Detection Using Modified Vivaldi Antennas
No full textThis paper presents an experimental study on subsurface water leakage detection using a Ground Penetrating Radar (GPR) system with modified Vivaldi antennas. The antennas are optimized by introducing extended circular edge slots to enhance gain and radiation characteristics at the low end of the bandwidth. Compared to a basic Vivaldi antenna of the same size, the optimized design achieves a 2.5 dBi gain improvement in the mid-band and a 56 MHz extension in low end of the bandwidth. Controlled experiments involving a buried metal rod and a plastic pipe alongside a leak source, simulates a real leakage scenario. Analysis of measurement results including B-scans reveal diffused scattering and distortion in the peak hyperbola signatures, corresponding to water presence. Thus, water leakage at a 15 cm depth is successfully detected with good spatial resolution. These results demonstrate the potential of the modified Vivaldi-based GPR system as a non-invasive, cost-effective tool for early water leakage detection in infrastructure and environmental monitoring applications
