Technische Hochschule Würzburg-Schweinfurt Publikationsserver OPUS
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Navigating the Future: an approach of autonomous indoor vehicles
Get PDFIn this project, we explored the ability of Reinforcement learning (RL) in driving an indoor car autonomously. RL has proven its good performance in solving challenging decision-making problems. Therefore, RL can be a promising solution for autonomous car to deal with complex driving scenarios. As hardware a model car eqipped with sensors and powerful computational unit has been used. We also utilized SLAM for environment mapping and a combination of lidar data and Wi-Fi technology for localization. The experiment showed that the model can perform very well in simulation. Although the model lacks the ability to drive the car as smoothly along a route, the car is still able to avoid obstacles and walls in an unknown real-world environment
High-Frequency Transformer and Reactor Models for Network Studies, Part E: Measurements and transformer design details
No full textHigh-Frequency Transformer and Reactor Models for Network Studies, Part D: Model interfacing and specifications
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Consideration of Non-Standard Overvoltages Compared with Standard Overvoltages in Power Transformers
No full textOvervoltages that exist in the power system differ from the standard overvoltages (OVs) waveshape, that are used to test power equipment. In the paper typical OV waveshapes that occurs in the power system are analyzed, using the methods available in the literature: frequency domain severity factor (FDSF), time domain severity factor (TDSF) and the method from IEC 60071-2: 2018, Annex I. General remarks about applicability of these methods are given in the paper. It can be concluded that without the knowledge of transformer design and dielectric strength of an insulation system elements, the reliable estimation of non-standard overvoltages severity cannot be made. The results of the paper are in line with the conclusion of CIGRE WG A2.63: Transformer Impulse Testing
Between Reality and Fiction: Self-Representation as an Avatar and Its Effects on Self-Presence
No full textA self-confident appearance is a basic prerequisite for success in the world of work 4.0. Within a few seconds, people convey a first impression that usually lasts. Artificial intelligence is making it increasingly important how our virtual selves appear and communicate (nonverbally) in digital worlds such as the metaverse. In addition to the modified creation of an avatar, the field of photogrammetry is developing fast, creating exact likenesses of ourselves in virtual environments. Given the importance of self-representation in virtual space for future collaborations, it is important to investigate the impact of phenotype in virtual worlds and how an avatar type can profitably be used situationally. We analyzed the effect of self-similar versus desirable self-presentation as an avatar on one's self-awareness, considering various theoretical constructs in the area of self-awareness and stress stimuli. The avatars were arbitrarily created on the one hand and scanned on the other hand with the help of a lidar sensor, the state-of-the-art photogrammetry method. All subjects were exposed to the established Trier Social Stress Test. The results showed that especially insecure people prefer to create rather than be scanned when confronted with a stressful work situation. If they are in a casual work environment and a relaxed situation, they prefer a 3D photorealistic avatar that reflects them in detail. Confident people will give their avatar their true appearance in any situation, while insecure people would only do so for honesty and authenticity. The choice of avatar type has considerable impact on self-confidence in different situations
