TY - THES A1 - Ruff, Daniel T1 - Analyse der psychoakustischen Größen von UX Sounds N2 - UX-Sounds lösen neben ihrer informativen Funktion auch emotionale Reaktionen aus. Die Arbeit untersucht, welche psychoakustischen Größen mit bestehenden Valenz/Arousal-Bewertungen von 85 UX-Sounds zusammenhängen, die aus fünf studentischen Vorarbeiten zusammengeführt wurden. Für jeden Sound wurden sechs psychoakustische Größen in MATLAB berechnet (Lautheit, Schärfe, Rauigkeit, Schwankungsstärke, Tonalität, Spektralschwerpunkt) und mittels Pearson-Korrelationen mit den V/A-Bewertungen in Beziehung gesetzt. Als stabilster Befund zeigt sich: Ein höherer Spektralschwerpunkt geht mit höherem Arousal und niedrigerer Valenz einher – über alle Teilgruppen hinweg. Andere Zusammenhänge, etwa Tonalität–Arousal, erwiesen sich hingegen als stark quellenabhängig, was die Bedeutung der Datensatzzusammensetzung unterstreicht. T3 - UX Sound and Auditory Stimuli (UXSAS) Database - 107 KW - Psychoakustik Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-51215 PB - Technische Hochschule Nürnberg CY - Nürnberg ER - TY - CHAP A1 - Walther, Christoph ED - Amthor, Ralph-Christian ED - Goldberg, Brigitta ED - Hansbauer, Peter ED - Landes, Benjamin ED - Wintergerst, Theresia T1 - Personzentrierter Ansatz T2 - Wörterbuch Soziale Arbeit N2 - Stichwort im Wörterbuch der Sozialen Arbeit KW - Psychologie KW - Psychologie KW - Humanistische Psychologie KW - Personzentrierter Ansatz Y1 - 2026 SN - 978-3-7799-9000-0 VL - 2026 PB - Beltz Juventa CY - Weinheim ET - 10. Aufl. ER - TY - CHAP A1 - Walther, Christoph ED - Amthor, Ralph-Christian ED - Goldberg, Brigitta ED - Hansbauer, Peter ED - Landes, Benjamin ED - Wintergerst, Theresia T1 - Soziale Arbeit mit psychisch erkrankten Menschen T2 - Wörterbuch Soziale Arbeit N2 - Stichwort "Soziale Arbeit mit psychisch erkrankten Menschen" im Wörterbuch Soziale Arbeit KW - Psychiatrie KW - Soziale Arbeit in der Psychiatrie KW - Soziale Arbeit mit psychisch erkrankten Menschen Y1 - 2026 SN - 978-3-7799-9000-0 VL - 2026 PB - Beltz Juventa CY - Weinheim ET - 10. Aufl. ER - TY - GEN A1 - Walther, Christoph T1 - 50 Jahre Psychiatrie-Enquete 1971-1975. Hintergründe – Positionen - Reformvorschläge N2 - 50 Jahre Psychiatrie-Enquete 1975: Politische und fachliche Hintergründe zu der Entstehung. Erarbeitete Positionen, Forderungen zur Veränderung und konkrete Reformvorschläge in der psychiatrischen Versorgung der BRD in der Enquete. KW - Psychiatrie KW - Geschichte der Psychiatrie KW - Psychiatrie-Enquete Y1 - 2025 ER - TY - GEN A1 - Walther, Christoph T1 - Mentale Gesundheit N2 - Was versteht man unter mentaler Gesundheit? Woran kann man sie erkennen? Woran kann man erkennen, dass sie gefährdet ist? KW - Psychologie KW - Mentale Gesundheit KW - Psychologie KW - Mentale Gesundheit KW - Psychische Gesundheit Y1 - 2026 VL - 2026 ER - TY - CHAP A1 - Yöndem, Şakir Furkan A1 - Tavakoli Kolagari, Ramin A1 - Schlereth-Groh, Benedikt T1 - Bridging the Sim-to-Real Gap with Explainability for ML-based Object Detection on Sonar Data N2 - Saving drowning victims is time-critical, but detecting people underwater is highly challenging due to poor visibility and large distances. While side-scan sonar (SSS) is widely used for seafloor mapping and debris detection, human detection in sonar data remains largely unexplored. Training deep neural networks for this task requires a large dataset, but collecting real maritime data is difficult and expensive, making a synthetic data generation approach necessary. We introduce SimWave, a simulation environment designed to generate synthetic data for underwater human detection. We train deep learning models on real, synthetic, and hybrid datasets, evaluating their performance on real sonar images. The contribution of this paper lies in combining synthetic data generation with Explainable Artificial Intelligence (XAI) to systematically refine artificial datasets, addressing the gap between synthetic and natural data to enhance real-world performance—–an approach not previously explored in underwater sonar-based human detection. To gain insight into the model’s decision-making process, we apply XAI techniques to analyze how attention shifts between real and synthetic training data. This helps visualize the synthetic-real data mismatch, refine synthetic data, and enhance model performance in real-world conditions. Our experimental results show that models trained on hybrid datasets, supported by XAI-based analysis, achieve notable performance improvements and better generalization. XAI helps identify domain gaps between real and synthetic data, allowing for dataset refinement and improved model accuracy. These findings highlight the effectiveness of synthetic generated data in training deep learning models for underwater human detection and emphasize the critical role of XAI in optimizing training data for real-world conditions KW - Underwater Human Detection KW - Side-Scan Sonar KW - Synthetic Data Generation KW - Explainable AI (XAI) KW - Domain KW - Bridging KW - Domain Gap KW - Deep Learning Y1 - 2025 ER - TY - CHAP A1 - Yöndem, Şakir Furkan A1 - Schlereth-Groh, Benedikt A1 - Tavakoli Kolagari, Ramin T1 - MirrorDrive: A Sim-to-Real Bridge for Reproducible Automotive Software Development N2 - A central difficulty in automotive software engineering is to ensure that autonomous driving functions are verified and validated with equal rigor in simulation and on physical vehicles. The transition from virtual development to real-world deployment introduces domain differences in sensor noise, actuator behavior, latency, timing, and environmental conditions, which complicate reproducibility and challenge the reliability of sim-to-real transfer. To address these issues, we present MirrorDrive, an ROS 2-based platform designed to enforce a “zero code-change” principle. MirrorDrive uses a 1:1 Gazebo simulation model of a physical remote-controlled (RC)-Car platform, allowing the exact same application code to run without adaptation in both environments, enabled by the ROS 2 middleware. The core contribution lies in achieving full deployment consistency by packaging the entire environment and application code within Docker containers. This approach guarantees reproducibility across different systems, supporting Continuous Integration (CI). Our case studies suggest that MirrorDrive provides a scalable and portable framework for the rapid prototyping and reliable validation of autonomous software, making it a valuable testbed for sim-to-real research and education (https://github.com/furkanyondemm/MirrorDrive). KW - Automotive Software Engineering KW - Sim-to-Real Transfer KW - Autonomous Driving KW - ROS2 KW - Containerization Y1 - 2026 U6 - https://doi.org/10.18420/se2026-ws_02 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Yöndem, Sakir Furkan A1 - Schlereth-Groh, Benedikt A1 - Kolagari, Ramin Tavakoli T1 - AquaScan-1K: Paving the Way to Robust Object Detection in Side-Scan Sonar T2 - 2026 IEEE Canadian Atlantic Ocean Symposium (CAOS) N2 - Machine learning models for underwater perception face substantial challenges in turbid search-and-rescue scenarios, where optical sensing is ineffective and side-scan sonar (SSS) becomes the primary modality. Existing SSS datasets often lack variability in viewpoint and seabed structure, limiting robustness and generalization. We present AquaScan-1K, a curated dataset of approximately 1,000 SSS images acquired under multiple angles and diverse seabed conditions, with expert annotations provided by Red Cross personnel using ethical human surrogates. The dataset captures realistic operational variability relevant for small-object detection. Cross-dataset experiments show that models trained on AquaScan-1K generalize more reliably under domain shift than those trained on public baselines. Transformer architectures exhibit reduced performance degradation under domain shift, and saliency analyses confirm that they consistently attend to shadow–silhouette features that constitute the primary discriminative signal in side-scan sonar imagery. Y1 - 2026 U6 - https://doi.org/10.1109/CAOS69069.2026.11665924 SP - 1 EP - 6 PB - IEEE ER - TY - CHAP A1 - Yöndem, Sakir Furkan A1 - Schlereth-Groh, Benedikt A1 - Tavakoli Kolagari, Ramin T1 - WaveGuard: Real-Time On-Board Detection of Missing Persons in Side-Scan Sonar Imagery Y1 - 2026 ER - TY - CHAP A1 - Schlereth-Groh, Benedikt A1 - Yöndem, Şakir Furkan A1 - Kolagari, Ramin Tavakoli A1 - Schmid, Ute T1 - Explainable-AI-Based Training for Relevance-Based Robust Reinforcement Learning T2 - Lecture Notes in Computer Science Y1 - 2026 SN - 9783032323347 U6 - https://doi.org/10.1007/978-3-032-32335-4_28 SN - 0302-9743 SP - 309 EP - 316 PB - Springer Nature Switzerland CY - Cham ER -