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Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µmax estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
Die Maschine ist in der Lage faserverstärkte thermoplastische Kunststoffrohre herzustellen. Entwickelt und konstruiert wurde die Maschine als Open Source Hardware Projekt. Das bedeutet die Baupläne und Zeichnungen werden frei zur Verfügung gestellt. Heimwerker und andere Interessierte sollen dadurch die Möglichkeit bekommen faserverstärkte Rohre eigenständig und günstig herzustellen. Die Entwicklung und Konstruktion der Wickelmaschine ist das Ergebnis einer Masterarbeit an der Hochschule Osnabrück.
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
Compliance of agricultural AI systems : app-based legal verification throughout the development
(2024)
Significant advances in artificial intelligence (AI) have been achieved; however, practical implementation in agriculture remains limited. Compliance with emerging regulations, such as the EU AI Act and GDPR, is now vital, even for non-critical AI systems. Developers need tools to assess legal compliance, which is complex, often requiring full legal advice. To address this issue, we are developing a support app that simplifies the legal aspects of AI system development, covering the entire lifecycle, from conception to distribution. The current app, which covers the key legal area of copyright and will soon include GDPR and the AI Act, aims to bridge the gap between AI research and agriculture. An evaluation of our app by experts from both the legal and the IT domains shows that the app assists the developers so that they make legally correct statements. Consequently, it promotes legal compliance and awareness among developers, contributing to the seamless integration of AI into agriculture. The need for compliant AI systems in various industries, including agriculture, will only increase as regulations evolve.
Innovationen sind die stärksten Gestaltungsfaktoren für eine neue vielversprechende Zukunft, da sie die wichtigsten Treiber für Wachstum und Ertrag in unserer Wirtschaft sind. Die aktuelle Zeitenwende zeigt uns sehr deutlich, dass wir ohne Innovationen bzw. Veränderungen und Anpassungen kaum noch wettbewerbsfähig bleiben, sowohl als Nation bzw. als Gesellschaft und insbesondere als Unternehmen.
Die hohe Dynamik und Komplexität der wirtschaftlichen und sozialen Prozesse setzt neue Maßstäbe an die Innovationsstrategien von Institutionen und Unternehmen.
Neue Technologien, neue Märkte, neues Kundenverhalten und der stetige Wandel sowohl in der Arbeitswelt als auch in unserem gesellschaftlichen Umfeld, wie z.B. die Digitalisierung, zeigen uns, dass allein eine Produktinnovation als solche heute nicht mehr ausreicht. Unter den genannten Randbedingungen müssen Innovationen auch in der Gestaltung von Geschäftsprozessen und Realisierung der "Work-Life-Balance" neu erdacht bzw. überprüft werden.
Der Vorsprung innovativer Produkte im viralen Wettbewerb ist oft nur kurz. Ein ganzheitliches Innovationsmanagement hat alle Bereiche des Unternehmens einzubeziehen und führt zu neuen Geschäftsmodellen, die etablierte Geschäftspraktiken verdrängen, ebenso tauchen durch neue Technologien in immer stärkerem Maße neue Anbieter auf, die die Spielregeln in den Märkten verändern.
Der 1. Deutsche Innovations-Kongress will Impulse setzen, Best-Practice-Modelle als Vorbilder anbieten und im Austausch zwischen den Referent*innen und den Teilnehmer*innen neue Wege bzw. Perspektiven eröffnen.
Wir freuen uns auf alle Teilnehmer*innen und den Erfahrungsaustausch, um aktuelle und nachhaltige Innovations-Impulse zu setzen und neue Wege erfolgversprechende Wege zu beschreiben, womit die bereits fruchtbaren Kooperationen zwischen Wirtschaft und Wissenschaft im Großraum Osnabrück noch weiter belebt werden soll.
Aktuell tragen auch 8 Studierendengruppen des Masterstudiengangs "Entwicklung und Produktion" der Hochschule Osnabrück in der Fakultät I u. I im Rahmen des Moduls "Innovationsmanagement" in Kooperation mit Unternehmen aus der Region durch die Entwicklung neuer innovativer Produkte zum Erfolg des Kongresses bei. Die Zwischenergebnisse dazu werden in einer Poster-Ausstellung präsentiert. Die Innovationsprojekte werden unter der Leitung von Prof. Dr. Jens Schäfer durchgeführt.
The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with physical entities in the real world. These entities are devices of different categories and sizes range from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires of them to work within the limited resources. This means to find a suitable implementation and configuration for a security algorithm, that performs properly on the device, which may become a challenging task. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are recorded in security policies, but they do not consider resource availability on the involved device and its power consumption while executing security algorithms. This paper presents an IoT security configuration tool that helps the designer of an IoT environment to experiment with the trade-off between maximizing security and extending the lifetime of a resource constrained IoT device. The tool is controlled with high-level description of security goals in the form of policies. It allows the designer to validate various (security) configurations for a single IoT device up to a large sensor network.