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We consider a small helicopter structure that is maneuvered through the control of moving masses. It is referred to as a swash mass helicopter (SMH). This paper addresses the trajectory tracking control problem for the SMH, with a specific focus on the decoupling change of coordinates of both rotational and translational dynamics. We propose a control scheme in which position tracking is the primary objective, while the attitude tracking task is considered as a secondary objective. The intermediate control signals related to the attitude dynamics exploit the structural properties of the SMH and are enhanced with terms that grant a more accurate tracking of the target trajectory. The closed-loop system stability under the trajectory tracking objective is obtained following the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) approach. In addition, the presence of external disturbances can diminish the trajectory tracking performance. For this reason, a nonlinear outer loop controller is added to the IDA-PBC to compensate the disturbances. Finally, the results of several simulations are reported to evaluate the performance of the control strategy.
A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is incremented. Finally, the highest scored connections are combined to form a Bayesian Network. The novel Physarum Learner method is evaluated with different configurations and compared to the LAGD Hill Climber showing comparable performance with respect to quality of training results and increased time efficiency for large data sets.
NoiSLU: a Noisy speech corpus for Spoken Language Understanding in the Public Transport Domain
(2024)
Abstract
In the early stages, entrepreneurs encounter significant challenges and extreme uncertainty when founding a young innovative venture. In response to this, a spectrum of wide support mechanisms are available. Typically facilitated not in isolation, but rather through collaborative efforts involving multiple institutions within a startup ecosystem. An exemplary theoretical framework delineating auspicious collaborative efforts among multiple institutions to foster innovation, is the Triple Helix model. The primary objective of this thesis is therefore to analyze the positioning of European top incubator landscape within the Triple Helix Model and the support offers for startups resulting from the cooperation between incubator and a subsystem. Ultimately, implications for the local German incubator brigk should be described. In doing so, the conducted study employed a qualitative data analysis, leveraging 100 datasets within the data analysis software ATLAS.ti. By crawling the "About us" pages on incubators' websites, the methodology involved first-level paragraph coding based on the Triple Helix Model's subsystems. Subsequently, second-level codes were assigned following a consistent schema that highlighted the kind of collaboration. The final step involved the clustering of similar second-level codes, providing a nuanced understanding of the dimensional interaction between incubators and other institutions. The results show that cooperation’s between the analyzed incubators and the three subsystems of the Triple Helix Model do exist. Besides the pronounced frequency of cooperation with the Economic Subsystem, the role reversal revealed by the analysis is a particularly interesting finding in this thesis. It shows that incubators themselves often take on the role of a subsystem to drive innovation. Looking at the results of this work in a broader context, it could represent a benchmark for individual institutions within Europe, improving their cooperation’s with the players of the three subsystems. And in the long run, enhancing their offering and support to young innovative companies.
Feedback kann eine sehr wichtige Rolle für den Lernerfolg spielen. Beim Peer Review Verfahren beurteilen die Studierenden selbst gegenseitig ihre Arbeitsergebnisse, geben also einander Feedback. In diesem Beitrag werden das Peer Review Verfahren mit Gruppenarbeit verknüpft und dabei zwei mögliche Strukturierungsvarianten differenziert. Für die Umsetzung dieser beiden Varianten wird die Moodle-Aktivität „Gegenseitige Beurteilung“ herangezogen, die in der hier verwendeten Moodle-Version aber nicht vollumfänglich auf das Arbeiten mit Gruppen ausgelegt ist. Daher wird in diesem Beitrag aufgezeigt, welche Gestaltungen und manuellen Schritte seitens der Lehrperson zweckmäßig sind, um die Aktivität „Gegenseitige Beurteilung“ auch in Kombination mit Gruppenarbeit in geeigneter Weise einsetzen zu können.
Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.