TY - JOUR A1 - Kuhn, Philipp A1 - Huber, Matthias A1 - Dorfner, Johannes A1 - Hamacher, Thomas T1 - Challenges and opportunities of power systems from smart homes to super-grids JF - Ambio N2 - The world’s power systems are facing a structural change including liberalization of markets and integration of renewable energy sources. This paper describes the challenges that lie ahead in this process and points out avenues for overcoming different problems at different scopes, ranging from individual homes to international super-grids. We apply energy system models at those different scopes and find a trade-off between technical and social complexity. Small-scale systems would require technological breakthroughs, especially for storage, but individual agents can and do already start to build and operate such systems. In contrast, large-scale systems could potentially be more efficient from a techno-economic point of view. However, new political frameworks are required that enable long-term cooperation among sovereign entities through mutual trust. Which scope first achieves its breakthrough is not clear yet. UR - https://doi.org/10.1007/s13280-015-0733-x Y1 - 2015 UR - https://doi.org/10.1007/s13280-015-0733-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62566 SN - 1654-7209 SN - 0044-7447 VL - 45 IS - Suppl 1 SP - 50 EP - 62 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Tutunaru, Robin A1 - Bosch, Stephan A1 - Greßhake, Lukas A1 - Holzhammer, Uwe T1 - Ein Konzept für eine GIS-gestützte Visualisierung der spezifischen Anforderungen von regionalen Akteuren der Energiewende an die Landnutzung JF - AGIT: Journal für Angewandte Geoinformatik N2 - Für eine nachhaltige Energiewende werden Konzepte benötigt, welche die techno-ökonomische und sozial-ökologische Komplexität regionaler Standortentscheidungen für erneuerbare Energien (EE) visualisieren können. Dabei möchten wir die Ergebnisse mittels Geographischer Infor-mationssysteme (GIS) so visualisieren, dass sie von jedem Akteur nachvollzogen werden können und einen konstruktiven Austausch zur Wertigkeit bestimmter Flächen zwischen allen Akteuren erlauben. T2 - A Concept for a GIS-based Visualisation of the Specific Demands on Land Use at the Background of Regional Energy Transitions UR - https://doi.org/10.14627/537742005 Y1 - 2023 UR - https://doi.org/10.14627/537742005 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62815 SN - 2509-713X SN - 2364-9283 VL - 2023 IS - 9 SP - 44 EP - 49 PB - Wichmann Verlag CY - Berlin ER - TY - JOUR A1 - Huber, Matthias A1 - Dimkova, Desislava A1 - Hamacher, Thomas T1 - Integration of wind and solar power in Europe: Assessment of flexibility requirements JF - Energy N2 - Flexibility is the ability of a power system to respond to changes in power demand and generation. Integrating large shares of variable renewable energy sources, in particular wind and solar, can lead to a strong increase of flexibility requirements for the complementary system, traditionally hydrothermal, which has to balance the fluctuations of variable generation. We quantify these flexibility requirements at the operational timescale of 1–12 hours and different spatial scales across Europe. Our results indicate that three major factors determine the ramping flexibility needed in future power systems: the penetration of variable renewables, their mix and the geographic system size. Compared to the variability of load, flexibility requirements increase strongly in systems with combined wind and PV (photovoltaics) contribution of more than 30% of total energy and a share of PV in the renewables mix above 20–30%. In terms of extreme ramps, the flexibility requirements of a geographically large, transnational power system are significantly lower than of smaller regional systems, especially at high wind penetration. UR - https://doi.org/10.1016/j.energy.2014.02.109 Y1 - 2014 UR - https://doi.org/10.1016/j.energy.2014.02.109 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-54702 SN - 0360-5442 SN - 1873-6785 VL - 2014 IS - 69 SP - 236 EP - 246 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Mieruch, Sebastian A1 - Kreps, Gastón A1 - Chouai, Mohamed A1 - Reimers, Felix A1 - Vredenborg, Myriel A1 - Rabe, Benjamin A1 - Tippenhauer, Sandra A1 - Behrendt, Axel T1 - SalaciaML-2-Arctic - a deep learning quality control algorithm for Arctic Ocean temperature and salinity data JF - Frontiers in Marine Science N2 - We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic , a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successfully reproduces human expertise by correcting misclassifications made by the classical algorithm, reducing False Negatives (samples incorrectly classified as “bad”) by 96% for temperature and 99% for salinity. When used in combination with a visual post-QC by human experts, it achieves a workload reduction of approximately 60% for temperature and 85% for salinity. All code and data required to reproduce the analysis or apply the method to other datasets are openly available via PANGAEA and GitHub. Moreover, SalaciaML-2-Arctic is accessible as a browser-based application at https://mvre.autoqc.cloud.awi.de, enabling its use without software installation or programming knowledge. UR - https://doi.org/10.3389/fmars.2025.1661208 Y1 - 2025 UR - https://doi.org/10.3389/fmars.2025.1661208 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62935 SN - 2296-7745 VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - CHAP A1 - Schlosser, Alexander A1 - Schuderer, Peter A1 - Franke, Jörg ED - Kohl, Holger ED - Seliger, Günther ED - Dietrich, Franz ED - Vien, Ha Thuc T1 - Sustainability Assessment and Optimization in Construction Site: A Simulation-Based Approach T2 - Decarbonizing Value Chains: Proceedings of the 20th Global Conference on Sustainable Manufacturing (GCSM 2024), October 9–11, 2024, Ho Chi Minh City, Vietnam N2 - The construction industry is currently facing significant challenges. In order to address these challenges, the REMUS simulation model library for the construction industry is being developed. To this end, the physical modules are divided into stationary and mobile modules, as well as information objects. To create the simulation model and conduct the simulation experiment, a requirements cluster with the most important parameters of construction sites is created. The elements of sustainability—environmental, economy, and social aspects—are employed to assess the simulation results and to optimize the model. To this end, corresponding KPIs, methods, and procedures are delineated, which are documented during the various simulation experiments and evaluated subsequently. The equipment and environment exert an influence on the “economy”. This is reflected in the costs associated with the model components and their operation. The area of “environmental” is represented by the consumption of input materials. Alternative consumption and recovery concepts are implemented and compared here. The “social” aspect is represented by the human-machine collaboration. As part of the simulation experiments, the recorded variables are continuously adapted and refined. This process enables the simulation to improve the sustainability of the construction site environment. UR - https://doi.org/10.1007/978-3-031-93891-7_10 Y1 - 2025 UR - https://doi.org/10.1007/978-3-031-93891-7_10 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62843 SN - 978-3-031-93891-7 SP - 82 EP - 90 PB - Springer CY - Cham ER - TY - JOUR A1 - Paarmann, Sabine A1 - Schreiber, Markus A1 - Chahbaz, Ahmed A1 - Hildenbrand, Felix A1 - Stahl, Gereon A1 - Rogge, Marcel A1 - Dechent, Philipp A1 - Queisser, Oliver A1 - Frankl, Sebastian Dominic A1 - Morales Torricos, Pablo A1 - Lu, Yao A1 - Nikolov, Nikolay I. A1 - Kateri, Maria A1 - Sauer, Dirk Uwe A1 - Danzer, Michael A. A1 - Wetzel, Thomas A1 - Endisch, Christian A1 - Lienkamp, Markus A1 - Jossen, Andreas A1 - Lewerenz, Meinert T1 - Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries JF - Batteries & Supercaps N2 - AbstractFor the battery industry, quick determination of the ageing behaviour of lithium‐ion batteries is important both for the evaluation of existing designs as well as for R&D on future technologies. However, the target battery lifetime is 8–10 years, which implies low ageing rates that lead to an unacceptably long ageing test duration under real operation conditions. Therefore, ageing characterisation tests need to be accelerated to obtain ageing patterns in a period ranging from a few weeks to a few months. Known strategies, such as increasing the severity of stress factors, for example, temperature, current, and taking measurements with particularly high precision, need care in application to achieve meaningful results. We observe that this challenge does not receive enough attention in typical ageing studies. Therefore, this review introduces the definition and challenge of accelerated ageing along existing methods to accelerate the characterisation of battery ageing and lifetime modelling. We systematically discuss approaches along the existing literature. In this context, several test conditions and feasible acceleration strategies are highlighted, and the underlying modelling and statistical perspective is provided. This makes the review valuable for all who set up ageing tests, interpret ageing data, or rely on ageing data to predict battery lifetime. UR - https://doi.org/10.1002/batt.202300594 Y1 - 2024 UR - https://doi.org/10.1002/batt.202300594 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51546 SN - 2566-6223 VL - 7 IS - 11 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Kiran, Iqra A1 - Azzam, Mohamed A1 - Endisch, Christian A1 - Butt, Nauman Zafar A1 - Lewerenz, Meinert T1 - Evaluation of Calendar Aging in Cells with Graphite: Silicon Anode Using Float Current Analysis Under the Influence of Voltage Hysteresis JF - Journal of The Electrochemical Society N2 - This study investigates the calendar aging of lithium-ion batteries with graphite-silicon anodes using float current analysis. While float current analysis is already a proven method for assessing aging in cells with graphite-based anodes, the presence of silicon introduces additional complexities due to its voltage hysteresis. We address this by comparing the results for the scaling factor separately for charge and discharge. The scaling factor is initially derived from charge and discharge GITT measurements on fresh cells, including an aging-induced shift among both electrode curves. This approach enables quantification of SEI growth ISEI growth, and cathode lithiation current ICL bridging measured results for float currents with capacity loss rate. As a result, the scaling factor during charge delivered the most meaningful results regarding fitted aging currents. By extending the estimation method based on the Arrhenius equation across temperatures from 5 °C to 50 °C, our model is validated against measured float currents, improving the predictive accuracy of long-term aging trends in silicon-containing anodes. Electrochemical impedance spectroscopy provided further insights into degradation mechanisms, revealing a strong correlation between cathode lithiation by salt decomposition and resistance increase at high voltages (⩾4.15 V), confirmed by pulse tests at 100% SOC showing a sharp resistance increase at elevated voltages. UR - https://doi.org/10.1149/1945-7111/ae0fe8 Y1 - 2025 UR - https://doi.org/10.1149/1945-7111/ae0fe8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63053 SN - 1945-7111 VL - 172 IS - 10 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Spinner, Thilo A1 - Schlegel, Udo A1 - Schall, Martin A1 - Sperrle, Fabian A1 - Sevastjanova, Rita A1 - Gobbo, Beatrice A1 - Rauscher, Julius A1 - El-Assady, Mennatallah A1 - Keim, Daniel A. T1 - Speculative Execution of Similarity Queries: Real-Time Parameter Optimization through Visual Exploration T2 - Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference N2 - The parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this issue, supporting the user in understanding the connection between abstract model parameters and model results. We present a visual analytics system enabling result understanding and model refinement on a ranking-based similarity search algorithm. Our system (1) visualizes the results in a projection view, mapping their pair-wise similarity to screen distance, (2) indicates the influence of model parameters on the results, and (3) implements speculative execution to enable real-time iterative refinement on the time-intensive offline similarity search algorithm. Y1 - 2021 UR - https://ceur-ws.org/Vol-2841/ UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63027 PB - RWTH Aachen CY - Aachen ER - TY - JOUR A1 - Kohler, Markus A1 - Mitsios, Dionysios A1 - Endisch, Christian T1 - Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity JF - Journal of Manufacturing Systems N2 - Manufacturing wound rotor synchronous machines (WRSMs) for electric vehicle traction systems necessitates rigorous quality inspection to ensure optimal product performance and efficiency. This paper presents a novel visual anomaly detection method for monitoring the needle winding process of WRSMs, utilizing unsupervised learning with convolutional autoencoders (CAEs) and the structural similarity index measure (SSIM). The method identifies deviations from the desired orthocyclic winding pattern during each stage of the winding process, enabling early detection of winding errors and preventing resource wastage and potential damage to the product or winding machinery. Trajectory-synchronized frame extraction aligns the visual inspection system with the winding trajectory, ensuring precise monitoring traceable to a specific point in the winding process. We present the comprehensive Winding Anomaly Dataset (WAD), which comprises images of WRSM rotor prototypes with and without winding faults recorded in different lighting conditions. The proposed reconstruction-based anomaly detection technique is trained on fault-free data only and utilizes the introduced masked mean structural dissimilarity index measure (MMSDIM) to focus on the relevant sections of the winding during inference. Comprehensive comparative analysis reveals that the CAE with unregularized latent space and the maximum mean discrepancy Wasserstein autoencoder (MMD-WAE) outperform the beta variational autoencoder (beta-VAE) in terms of anomaly detection performance, with the CAE and WAE delivering comparable results. Extensive testing confirms the approach’s effectiveness, achieving 95.6 % recall at 100 % precision, an AUROC of 99.9 %, and an average precision of 99.1 % on the challenging WAD, considerably outperforming state-of-the-art visual anomaly detection models. This work thus offers a robust solution for WRSM production quality monitoring and promotes the incorporation of visual inspection in electric drive manufacturing systems. UR - https://doi.org/10.1016/j.jmsy.2024.12.005 Y1 - 2024 UR - https://doi.org/10.1016/j.jmsy.2024.12.005 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-55371 SN - 1878-6642 VL - 2025 IS - 78 SP - 410 EP - 432 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Hartl, Dominik A1 - de Luca, Valeria A1 - Kostikova, Anna A1 - Laramie, Jason A1 - Kennedy, Scott A1 - Ferrero, Enrico A1 - Siegel, Richard A1 - Fink, Martin A1 - Ahmed, Sohail A1 - Millholland, John A1 - Schuhmacher, Alexander A1 - Hinder, Markus A1 - Piali, Luca A1 - Roth, Adrian T1 - Translational precision medicine: an industry perspective JF - Journal of Translational Medicine N2 - In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective. UR - https://doi.org/10.1186/s12967-021-02910-6 KW - Translational medicine KW - Precision medicine KW - Drug development KW - Biomarkers KW - Multi-omics KW - Modeling KW - Artificial intelligence KW - Pharmaceutical industry KW - Digital biomarkers KW - Companion diagnostics Y1 - 2021 UR - https://doi.org/10.1186/s12967-021-02910-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39873 SN - 1479-5876 VL - 19 PB - BioMed Central CY - London ER - TY - JOUR A1 - Zepf, Andreas A1 - Gelner, Alexander A1 - Härtl, Martin A1 - Jaensch, Malte T1 - 3D-CFD-Based optimization of piston Geometry, injector nozzle Design, and injection strategy for the alternative diesel fuel Oxymethylene ether (OME) JF - Fuel N2 - Oxymethylene ethers (OMEs) represent a promising alternative to conventional diesel fuels, offering carbon–neutral mobility and soot-free combustion due to the absence of carbon–carbon bonds. This prevents the soot-NOx trade-off. Moreover, an OME-adapted and optimized combustion process offers the opportunity to improve engine efficiency while simultaneously reducing NOx emissions, thus addressing the existing efficiency-NOx trade-off. Realizing this potential necessitates tailoring the mixture preparation and combustion process to the unique characteristics of OME. This study explores the optimization of piston geometry, injector nozzle design, and injection strategies to leverage OME’s unique properties for improved engine performance. Using 3D-CFD simulations with the CONVERGE software, key parameters such as nozzle diameter, spray angle, and piston bowl shape are analyzed for their impact on efficiency and emissions. The results highlight that wider piston bowl geometries enhance indicated efficiency, while larger nozzle diameters improve combustion efficiency due to reduced combustion durations. Smaller spray angles effectively lower NOx emissions but introduce challenges such as piston wetting and localized temperature peaks. The study also confirms that pilot injection does not benefit OME operation, simplifying injection system requirements. These findings underline the need for tailored engine designs to fully exploit OME’s potential as a sustainable fuel. The proposed configurations pave the way for further experimental validation and practical implementation in internal combustion engines, contributing to the global transition toward low-carbon transport systems. UR - https://doi.org/10.1016/j.fuel.2025.136995 Y1 - 2025 UR - https://doi.org/10.1016/j.fuel.2025.136995 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63407 SN - 1873-7153 VL - 406 IS - Part B PB - Elsevier CY - New York ER - TY - JOUR A1 - Kolff, Maurice A1 - Himmels, Chantal A1 - Venrooij, Joost A1 - Parduzi, Arben A1 - Pool, Daan M. A1 - Riener, Andreas A1 - Mulder, Max T1 - Effect of motion mismatches on ratings of motion incongruence and simulator sickness in urban driving simulations JF - Transportation Research Part F: Traffic Psychology and Behaviour N2 - This paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results. UR - https://doi.org/10.1016/j.trf.2025.103370 Y1 - 2025 UR - https://doi.org/10.1016/j.trf.2025.103370 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63433 SN - 1873-5517 VL - 2025 IS - 115 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Lodes, Lukas A1 - Hass, Erik A1 - Dachtler, Kristina A1 - Schiendorfer, Alexander T1 - SmartManPy – Open Source Synthetic Manufacturing Data JF - Procedia Computer Science N2 - Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub. UR - https://doi.org/10.1016/j.procs.2025.01.245 Y1 - 2025 UR - https://doi.org/10.1016/j.procs.2025.01.245 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57650 SN - 1877-0509 VL - 2025 IS - 253 SP - 1830 EP - 1839 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Doepfert, Markus A1 - Chu, Jiahe A1 - Hamacher, Thomas T1 - HAMLET: A modular agent-based Python framework for energy markets and systems JF - SoftwareX N2 - HAMLET is an agent-based Python framework designed to model the intricate interactions between energy markets, systems, and stakeholders. Its modular architecture provides a flexible and adaptable platform for analyzing complex energy systems. With numerous pre-built templates for energy system components, HAMLET enables researchers to focus on their area of expertise without the need to develop the surrounding ecosystem from scratch. By integrating market and system dynamics, HAMLET supports diverse applications, including market design optimization, grid management, policy refinement, and energy management system development. HAMLET delivers detailed insights into the technical, economic, and social impacts of new system designs, addressing a critical gap in energy system modeling. UR - https://doi.org/10.1016/j.softx.2025.102346 Y1 - 2025 UR - https://doi.org/10.1016/j.softx.2025.102346 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63428 SN - 2352-7110 VL - 2025 IS - 32 PB - Elsevier CY - Amsterdam ER - TY - THES A1 - Bauder, Maximilian T1 - Kooperative intelligente Verkehrssysteme für die forensische Unfallanalyse BT - Chancen und Herausforderungen durch die Car-to-X-Kommunikation N2 - Die vorliegende Dissertation befasst sich mit den Chancen und Herausforderungen für die forensische Unfallanalyse, die sich durch den Einsatz kooperativer intelligenter Verkehrssysteme (C-ITSs) ergeben. Im Zentrum der Untersuchung steht die kooperative Car-to-X-Direktkommunikation (C2X-Kommunikation) im 5,9 GHz Frequenzbereich, eine Technologie, die seit 2019 mit der Markteinführung des Volkswagen Golf 8 erstmals für den europäischen Massenmarkt verfügbar ist. Die übergeordnete Forschungsfrage der Arbeit besteht darin, wie die Chancen kooperativer intelligenter Verkehrssysteme für die forensische Unfallanalyse genutzt und gleichzeitig die entstehenden Herausforderungen adressiert werden können. Durch die erstmalige Verknüpfung dieser beiden Forschungsfelder sowie einer systematischen Analyse entlang der Schichten des Protokollstapels konnten Chancen und Herausforderungen der C2X-Kommunikation für die Unfallanalyse identifiziert werden. Darauf aufbauend sind vier Forschungsfragen formuliert worden, die in dieser Dissertation untersucht und beantwortet wurden. Die Chancen für die Unfallanalyse liegen vor allem im Erhalt zusätzlicher digitaler Spuren durch die Fahrzeugzu-Allem-Kommunikation (C2X)-Kommunikationsdaten. Um diese Chance zu nutzen, ist es erforderlich, den Mehrwert der Daten für die Unfallanalyse nachzuweisen. Im Rahmen dieser Arbeit wurden Experimente und Analysen durchgeführt, um den Mehrwert von C2X-Kommunikationsdaten hinsichtlich Verfügbarkeit, Genauigkeit und Anwendbarkeit für die forensische Unfallanalyse zu bewerten. Besondere Aufmerksamkeit wurde der Cooperative Awareness Message (CAM) gewidmet, die wesentliche Informationen wie Fahrzeugposition, Geschwindigkeit und Fahrtrichtung enthält. Fahrversuche wurden durchgeführt, um die Genauigkeit und zeitliche Verfügbarkeit der CAM-Daten zu erforschen und deren gerichtssichere Verwendbarkeit in der Unfallanalyse sicherzustellen. Die Ergebnisse verdeutlichen, dass CAM-Daten eine vergleichbare oder höhere Genauigkeit und höhere zeitliche Auflösung im Unfallhergang aufweisen können als die Daten des Event Data Recorders (EDR). Darüber hinaus konnte durch die Entwicklung und Anwendung eines Kalman-Filters die mediane Positionsgenauigkeit der CAM-Daten erhöht werden. Ergänzend wurde ein Rekonstruktionsprogramm entwickelt, das ausschließlich auf CAM-Daten basiert und die direkte Anwendbarkeit der CAM-Daten für die Unfallanalyse plausibilisiert. Zuletzt wurde das Konzept einer forensischen Roadside-Unit (F-RSU) vorgestellt, die eine unabhängige Speicherung und Bereitstellung von C2X-Kommunikationsdaten ermöglicht. Neben den Chancen wurden zentrale Herausforderungen identifiziert. Dazu zählen die Berücksichtigung von C2X-Warnungen auf das Fahrerverhalten, die Integration der Daten in bestehende Rekonstruktionssoftware sowie datenschutzrechtliche Aspekte. Zur Adressierung dieser sollten primär bestehende Rekonstruktionsprogramme um C2X-Funktionen erweitert und neue forensische Grunddaten ermittelt werden. Für Letzteres wurde in dieser Arbeit eine Probandenstudie in einem Fahrsimulator durchgeführt. Die Ergebnisse zeigen, dass Fahrer auf C2X-Warnungen durch Gaswegnahme oder Bremsen angemessen reagieren, auch wenn der Warnungsgrund nicht unmittelbar erkennbar ist. Negative Auswirkungen auf die Reaktionsdauer der Probanden in Verbindung mit C2X-Warnungen konnten ebenfalls nicht nachgewiesen werden. Eine falsch-positive C2X-Warnung führte in der Studie lediglich bei 2 von 32 Probanden zu verkehrsgefährdenden Reaktionen, was darauf hindeutet, dass ihr Einfluss auf die Unfallmitursächlichkeit als gering einzustufen ist. Die Ergebnisse der Dissertation verdeutlichen, dass C2X-Kommunikationsdaten eine entscheidende Erweiterung der digitalen Datenbasis für die Unfallanalyse darstellen können. Sie liefern nicht nur bedeutende Informationen im Unfallhergang über die Unfallbeteiligten, sondern ermöglichen dazu die Analyse des Einflusses unbeteiligter Verkehrsteilnehmer und der Verkehrsinfrastruktur. Der wesentliche Beitrag dieser Dissertation liegt in der Schaffung einer Grundlage für die zukünftige, gerichtssichere Nutzung von C2X-Kommunikationsdaten in der Unfallanalyse. Die vollständige Ausschöpfung des Potenzials der C2X-Technologie erfordert jedoch weitere Forschungsarbeiten sowie rechtliche Klärungen hinsichtlich der Speicher- und Verarbeitungsmöglichkeiten zu forensischen Zwecken. N2 - This dissertation explores the opportunities and challenges for forensic accident analysis arising from the deployment of Cooperative Intelligent Transport Systems (C-ITSs). The investigation focuses on cooperative Vehicle-to-X short-range communication (hereafter referred to as V2X communication) in the 5.9 GHz band, a technology that has been available on the European mass market for the first time since 2019 with the introduction of the Volkswagen Golf 8. The overarching research question of this work is how the opportunities provided by C-ITSs can be utilized for forensic accident analysis while simultaneously addressing the associated challenges. By linking these two research fields for the first time and conducting a systematic analysis across the protocol stack layers, this study identifies both the opportunities and challenges of V2X communication for accident analysis. Based on these findings, four research questions were formulated, examined, and answered in this dissertation. The opportunities for accident analysis arise primarily from the availability of additional digital traces provided by V2X communication data. To fully leverage this opportunity, it is essential to demonstrate the benefits of these data for accident analysis. In this thesis, experiments and analyses were conducted to evaluate the added value of V2X communication data in terms of availability, accuracy, and applicability for forensic accident analysis. Special emphasis was placed on the Cooperative Awareness Message (CAM), which contains essential information such as vehicle position, speed and direction of travel. Driving tests were conducted to investigate CAM data’s accuracy and temporal availability and ensure its forensic usability in accident analysis. The results indicate that Cooperative Awareness Message (CAM) data provide a comparable or higher level of accuracy and temporal resolution for accident reconstruction than the data from the Event Data Recorder (EDR). Furthermore, the development and application of a Kalman filter enhanced the median positional accuracy of CAM data. A reconstruction program that exclusively utilizes CAM data was also developed, validating its direct applicability for accident analysis. Moreover, the concept of a forensic roadside unit (F-RSU) was introduced, enabling the independent storage and provision of V2X communication data. In addition to the opportunities, key challenges were identified. These include the influence of V2X warnings on driver behavior, the integration of V2X communication data into existing reconstruction software, and data protection concerns. To address these challenges, existing reconstruction programs should primarily be extended to include V2X functionality and new fundamental forensic data should be determined. For the latter, a driving simulator study was conducted with 32 participants. The results indicate that drivers respond appropriately to V2X warnings by either releasing the accelerator or braking, even when the reason for the warning is not immediately apparent. Moreover, no negative effects could be observed on participants’ reaction times in response to V2X warnings. A false-positive V2X warning led to traffic-endangering reactions in only 2 out of 32 participants, indicating a minimal impact on accident causation. The findings of this dissertation demonstrate that V2X communication data can serve as a significant extension of the digital database for accident analysis. They provide valuable information about the accident sequence involving the directly affected parties while also enabling the evaluation of the impact of uninvolved road users and surrounding traffic infrastructure. This dissertation’s main contribution is creating a basis for the future, court-proof utilization of V2X communication data in accident analysis. However, fully realizing the potential of V2X technology requires further research and legal clarification regarding storage and processing options for forensic purposes. Y1 - 2025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61671 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER - TY - THES A1 - Koval, Leonid T1 - Methodology for Evaluating and Optimizing Machine Learning Applications in Industrial Production N2 - This dissertation examines the challenges of integrating machine learning into manufacturing environments and introduces a structured optimization methodology, termedUnderstanding and Transformation, Classification and Estimation, Optimization, Results and Evaluation (UT-CORE), to align technical solutions with strategic production objectives. The initial chapters establish the historical context and current state of data-driven production, emphasizing the complexities introduced by imbalanced datasets, constrained budgets, and evolving AI maturity within the industry. Cost emerges as a central determinant for success, prompting in-depth analyses of model-centric and data-centric pipeline design approaches. Building on these foundations, UT-CORE is presented as a four-phase process that employs a morphological box to isolate critical pipeline components and to quantify them through cost, time, availability, and complexity. By transforming high-level goals into systematic ranking and selection mechanisms using multi-criteria decision-making tools, UTCORE pinpoints the most impactful aspects of an ML pipeline, such as labeling, deployment, or model tuning, and visualizes their break-even points for more transparent managerial oversight. This process supports incremental improvements and comprehensive overhauls and can be adapted to industrial use cases. An extensive real-world application in a small to medium-sized enterprise validates the method’s utility. The findings underscore the importance of optimizing meta-characteristics selectively rather than attempting to address an entire pipeline simultaneously. Concluding discussions highlight UT-CORE’s adaptability to emerging technologies such as automated machine learning, and robotic process automation, as well as the potential for integrated information modeling to enhance future iterations of the method. Ultimately, this dissertation contributes a robust, modular framework to facilitate cost-effective, data-driven quality assurance across diverse production contexts. Y1 - 2025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63465 PB - Technische Hochschule Ingolstadt CY - Ingolstadt ER - TY - JOUR A1 - Armbruster, Felina A1 - Gelner, Alexander A1 - Zepf, Andreas A1 - Prager, Maximilian A1 - Härtl, Martin A1 - Jaensch, Malte T1 - Investigations on particle emissions of large-bore engines powered by natural gas and hydrogen JF - Environmental Science: Advances N2 - TIn an effort to mitigate the impact of climate change, e.g., by reducing the emission of greenhouse gases, hydrogen is becoming an increasingly attractive alternative energy source, replacing conventional long-chain hydrocarbon fuels in the energy and transport sector. While there is a shift in individual transport towards battery-electric applications, the maritime and energy production sectors rely on a high energy density and time- and location-independent availability of the energy carrier. Therefore, large-bore engines powered by renewable fuels have the potential to shift the industry towards a climate-neutral operation. Besides the emission of greenhouse gases, internal combustion engines are known for emitting pollutant emissions, harming human health and the environment. Research on particle emissions of natural gas and hydrogen engines has mainly focused on automotive and heavy-duty applications. Hence, this study investigates particle emissions of a large-bore single-cylinder research engine powered by hydrogen, compared to natural gas, for the first time. Investigations on particles with a diameter as low as 10 nm showed particle numbers of 104 to 105 # cm−3, unexpectedly achieving slightly higher particle numbers in hydrogen than in natural gas operations. This is due to particles from lubricant oil and a stronger fuel interaction with the liner oil film in hydrogen operation, demonstrated within a 3D-CFD simulation. The concentrations are still lower by several orders of magnitude than in long-chain hydrocarbon fuel operations of identical engines. An extended emissions analysis based on the gaseous components THC, CO, and CO2 shows the negligible carbonaceous emissions induced by these oil-based particles. UR - https://doi.org/10.1039/D4VA00200H Y1 - 2024 UR - https://doi.org/10.1039/D4VA00200H UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50189 SN - 2754-7000 VL - 3 IS - 11 SP - 1524 EP - 1536 PB - RSC CY - London ER - TY - JOUR A1 - Hennighausen, Christine A1 - Yarza Navarro-Schär, Vanessa Gabriela A1 - Eller, Eric T1 - AI-Mediated Communication in E-Commerce: Implications for Customer Trust JF - International Journal of Consumer Studies N2 - Generative artificial intelligence (AI) technologies offer new potential for marketing and customer operations, such as automation and personalization of customer service. However, more must be understood about how AI‐mediated communication (AI‐MC) affects customer trust. We conducted an online experiment to investigate the impact of AI‐MC on customer trust in an online retail context. We presented N = 294 participants with two email scenarios describing a product return context, labeled as written by either (a) a service employee, (b) a service employee assisted by AI, or (c) AI on behalf of the service employee. We further varied levels of service criticality to consider customers' perception of vulnerability. Our findings revealed higher customer trust ratings in the online retailer when the email communications were written by the service employee, compared to those written by the service employee assisted by AI. When analyzing the different components of trust, it was found that communications written by the service employee assisted by AI reduced perceptions of both the online retailer's benevolence and integrity, while communications written by AI on behalf of the employee led to lower perceived integrity of the online retailer. Surprisingly, service criticality did not affect trust ratings. We discuss the managerial implications of integrating generative AI into customer service in the context of the EU AI Act, which came into force on 1 August 2024. UR - https://doi.org/10.1111/ijcs.70111 Y1 - 2025 UR - https://doi.org/10.1111/ijcs.70111 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62426 SN - 1470-6431 VL - 49 IS - 5 PB - Wiley CY - Oxford ER - TY - JOUR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection JF - Scientific Reports N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.1038/s41598-025-23019-6 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-23019-6 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63458 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Bhanderi, Savankumar A1 - Agrawal, Shiva A1 - Elger, Gordon T1 - Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection T2 - Research Square N2 - Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin). UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-7222130/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61996 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Schmid, Michael A1 - Endisch, Christian T1 - Online diagnosis of soft internal short circuits in series-connected battery packs using modified kernel principal component analysis JF - Journal of Energy Storage N2 - Safe operation of large battery storage systems requires advanced fault diagnosis that is able to detect faults and provide an early warning in the event of a fault. Since Internal Short Circuits (ISC) are the most common abuse condition leading to thermal runaway, this study addresses the early detection of incipient soft ISCs at the stage when the fault is still uncritical and does not lead to significant heat generation. The differences in cell voltages as measured by conventional battery management systems prove to be indicative features for ISC diagnosis. However, due to poor balancing and parameter variations, the cell voltage differences exhibit nonlinear variations. This work addresses this challenge with a nonlinear data model based on Kernel Principal Component Analysis (KPCA). To enable an online application in a vehicle, the present work reduces the computational complexity of the method by an optimal choice of training data. An analysis of the contribution of each cell to the fault statistics enables identification of the faulty cell. Since early-stage ISCs can exhibit a wide range of short-circuit resistances, experimental validation is performed with resistances from 10Ω to 10kΩ, which are correctly detected and isolated by the optimized cross-cell monitoring in all cases. UR - https://doi.org/10.1016/j.est.2022.104815 KW - lithium-ion battery KW - internal short circuit KW - fault diagnosis KW - fault isolation KW - kernel principal component analysis KW - battery safety Y1 - 2022 UR - https://doi.org/10.1016/j.est.2022.104815 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-25282 SN - 2352-1538 VL - 2022 IS - 53 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Nebl, Christoph A1 - Kotzur, Frank-Oliver A1 - Koch, Daniel A1 - Schweiger, Hans-Georg T1 - Prediction of constant power delivery of lithium-ion cells at high loads JF - Journal of Energy Storage N2 - Available energy and available capacity are key factors for dimensioning batteries. Discharge duration of a battery and its dependency on discharge current amplitude are well described by Peukert’s law from low to medium current ranges. Other equations describe this dependency up to very high current rates, including the currents occurring during a short circuit. It is not current and capacity, but energy and power which are the key parameters for dimensioning battery systems. Thus, the available power of battery cells, vs. the discharge duration was investigated from low to high constant power discharge loads. Based on the results of these experiments, a correlation of the maximum discharge duration for low to medium discharge power pulses, similar to the Peukert’s equation was found. Additionally, a new equation is proposed, describing the power range from low to very high discharge power rates. The result of this work simplifies the design of battery systems, its electromechanical components, as well as improves the prediction of available boost power cost-effective way e.g. for hybrid electric vehicles. UR - https://doi.org/10.1016/j.est.2020.101552 KW - Constant power discharge KW - Lithium-ion batteries KW - Peukert equation KW - High load amplitudes KW - Constant power prediction KW - Discharge power Y1 - 2020 UR - https://doi.org/10.1016/j.est.2020.101552 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9239 SN - 2352-1538 VL - 2020 IS - 30 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Knollmeyer, Simon A1 - Caymazer, Oğuz A1 - Großmann, Daniel T1 - Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain JF - Electronics N2 - Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks. UR - https://doi.org/10.3390/electronics14112102 Y1 - 2025 UR - https://doi.org/10.3390/electronics14112102 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59524 SN - 2079-9292 VL - 14 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Tangirala, Sri Girish A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - CNN-Based Classification of Optically Critical Cutting Tools with Complex Geometry: New Insights for CNN-Based Classification Tasks JF - Sensors N2 - Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features. UR - https://doi.org/10.3390/s25051575 Y1 - 2025 UR - https://doi.org/10.3390/s25051575 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58058 SN - 1424-8220 VL - 25 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Benchmarking CNN Architectures for Tool Classification: Evaluating CNN Performance on a Unique Dataset Generated by Novel Image Acquisition System JF - IEEE Access N2 - In this study, we introduce the ToolSurface-144 dataset, which is presented here for the first time. It comprises four subsets – Full R, Full S, Top R, and Top S – each containing 144 tool classes captured under varying illumination conditions and fields of view. A newly developed, patented imaging approach was employed to acquire the data. It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. In this study, we evaluate six state-of-the-art convolutional neural networks—AlexNet, DenseNet161, EfficientNet-B0, ResNet152, ResNet50, and VGG16—using three training strategies: fine-tuning, freezing of pre-trained layers, and training from scratch. The results show that EfficientNet-B0 consistently achieved the highest classification accuracy in nearly all experiments and data sets. Especially using the fine-tuning training strategy, the model achieved 99% accuracy in tool classification. ResNet50 benefited greatly from fine-tuning and freezing, achieving a significant increase in performance compared to training from scratch. In contrast, ResNet152, AlexNet, and VGG16 consistently showed poor classification performance, indicating difficulties regarding learning and generalisation. The results show that diffuse illumination and complete tool views provide the best classification conditions, while restricted image sections with homogeneous illumination negatively affect model performance. Among the evaluated training strategies, fine-tuning proved the most efficient training method for developing CNN models for tool classification. UR - https://doi.org/10.1109/ACCESS.2025.3574785 Y1 - 2025 UR - https://doi.org/10.1109/ACCESS.2025.3574785 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59669 SN - 2169-3536 VL - 13 SP - 96400 EP - 96422 PB - IEEE CY - New York ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - Automatized End Mill Wear Inspection Using a Novel Illumination Unit and Convolutional Neural Network JF - IEEE Access N2 - Ensuring cutting tools are in optimal condition is essential for achieving peak machining performance, given their direct impact on both workpiece quality and process efficiency. However, accurately assessing wear on end mills, especially those with complex geometries, pose a significant challenge due to their reflective surfaces and varied wear patterns. Presented here is a novel method that addresses this challenge by employing a customized illumination unit in conjunction with a convolutional neural network (CNN) for end mill wear analysis. This innovative approach involves utilizing the specially designed illumination unit to capture high-quality images, enabling precise examination of material wear on helically shaped end mills. Notably, this method is tailored to illuminate reflective surfaces and represents a pioneering application in the realm of wear testing.We validate the viability of this approach by employing CNN-based models to segment wear on complex-shaped end mills coated with titanium carbonitride (TiCN) and titanium nitride (TiN). We achieved remarkable mean Intersection over Union (mIoU) results in wear detection on a test dataset: 0.99 for tool segmentation, 0.78 for abnormal wear, and 0.71 for normal wear segmentation. UR - https://doi.org/10.1109/ACCESS.2024.3454692 Y1 - 2024 UR - https://doi.org/10.1109/ACCESS.2024.3454692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58160 SN - 2169-3536 VL - 12 SP - 124282 EP - 124297 PB - IEEE CY - New York ER - TY - CHAP A1 - Mathias, Selvine George A1 - Asif, Saara A1 - Akmal, Muhammad Uzair A1 - Knollmeyer, Simon A1 - Koval, Leonid A1 - Großmann, Daniel ED - Rocha, Ana Paula ED - Steels, Luc ED - van den Herik, Jaap T1 - Industrial Image Grouping Through Pre-Trained CNN Encoder-Based Feature Extraction and Sub-Clustering T2 - Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 2 N2 - A common challenge faced by many industries today is the classification of unlabeled image data from production processes into meaningful groups or patterns for better documentation and analysis. This paper presents a sequential approach for leveraging industrial image data to identify patterns in products or processes for plant floor operators. The dataset used is sourced from steel production, and the model architecture integrates feature reduction through convolutional neural networks (CNNs) like VGG, EfficientNet, and ResNet, followed by clustering algorithms to assign appropriate labels to the observed data. The model’s selection criteria combine clustering metrics, including entropy minimization and silhouette score maximization. Once primary clusters are identified, sub-clustering is performed using near-labels, which are pre-assigned to images with initial distinctions. A novel metric, C-Score, is introduced to assess cluster convergence and grouping accuracy. Experimental re sults demonstrate that this method can address challenges in detecting variations across images, improving pattern recognition and classification. UR - https://doi.org/10.5220/0013189000003890 Y1 - 2025 UR - https://doi.org/10.5220/0013189000003890 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58004 SN - 978-989-758-737-5 SP - 496 EP - 506 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Mathias, Selvine George A1 - Akmal, Muhammad Uzair A1 - Asif, Saara A1 - Koval, Leonid A1 - Knollmeyer, Simon A1 - Großmann, Daniel T1 - Pattern Identifications in Transformed Acoustic Signals Using Classification Models JF - Procedia CIRP N2 - Pattern identifications in signals is necessary to discern variations from approved normal values in different scenarios. With machine learning algorithms, it is possible to use hybrid methods of pattern identifications such as feature extractions followed by classifications and/or clustering. This paper presents a pattern identification approach of acoustic signals using their transformations as inputs to classification algorithms. The analysis is carried out on two transformed versions of acoustic emission (AE) hits such as log transformations of peak hits and binary sequencing based on threshold crossing. A comparative analysis using custom data loss metrics is made to determine which inputs provide the best information in predictive methods for identifying commonly occurring patterns while acknowledging significant data loss. The methodology is conducted on transformed versions of a public dataset and the results show that patterns can be discerned to above 90% accuracy with the transformed datasets. The experimental results yield that actual source signals need not be utilized depending on which transformations suit the practical application. UR - https://doi.org/10.1016/j.procir.2024.10.061 Y1 - 2024 UR - https://doi.org/10.1016/j.procir.2024.10.061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56233 SN - 2212-8271 VL - 2024 IS - 130 SP - 93 EP - 99 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Haimerl, Mathias A1 - Radetzky, Laura A1 - Brenner, Evi Stefanie A1 - Strapak, Viktoria A1 - Riener, Andreas T1 - Leveling the Playing Field BT - Neurodiversity in Research using the Potential of Gamified Questionnaires T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Participating in studies with lengthy questionnaires can be exhausting, especially for individuals with disabilities or neurodivergent individuals. Gamification can potentially enhance user experience (UX) and, thus, create a more inclusive engagement with studies. This study investigates gamified studies’ UX for neurodivergent persons. A gamified version of the Quality of Life Enjoyment and Satisfaction Questionnaire – Short Form (Q-LES-Q-SF) was created using motivational theory and the gamification frameworks Marczewski’s Periodic Table of Gamification Elements and Gamification User Types Hexad Scale. First results of a preliminary comparative survey with N = 72 (gamified n = 31) suggest that gamification increases the UX for neurodivergent participants. UR - https://doi.org/10.1145/3743049.3748578 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748578 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61794 SN - 979-8-4007-1582-2 SP - 659 EP - 667 PB - ACM CY - New York ER - TY - CHAP A1 - Neufeld, Lisa A1 - Heim, Elena A1 - Klinger, Madita Joy A1 - Himmels, Chantal A1 - Böck, Henrike A1 - Riener, Andreas T1 - Beats vs. Talks: Alleviating Virtual Reality Sickness with Music and Podcasts T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - As virtual reality (VR) technology advances, its potential for widespread application increases. However, VR sickness (VRS) remains a major obstacle to broader adoption. This study investigates the effect of auditory stimuli on VRS during VR experiences. It contrasts the effects of music and podcasts, with a no-sound environment serving as reference. Twenty-three participants experienced three different auditory conditions during a VR roller coaster ride in a randomized, balanced order. VRS was quantified using the Simulator Sickness Questionnaire (SSQ) and physiological data. The SSQ results indicate that music significantly reduced oculomotor disturbances and disorientation, while podcasts had no positive effects. The physiological data demonstrated no significant effects. The majority of participants preferred the music scenario, describing it as relaxing and pleasant. This highlights the potential of music, especially when self-selected and perceived as pleasant, to improve VR experience by significantly reducing VRS. This effect appears to be independent of physiological data. UR - https://doi.org/10.1145/3743049.3748539 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748539 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61776 SN - 979-8-4007-1582-2 SP - 488 EP - 493 PB - ACM CY - New York ER - TY - CHAP A1 - Appel, Patricia B. A1 - Böck, Henrike A1 - Hompf, Filippa Viktoria Franziska A1 - Plümer, Kolja Sam A1 - Russ, Sarah A1 - Riener, Andreas T1 - Understanding Bystander Preferences for Medical Emergency Support Measures in VR-Simulated eVTOL Flights T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - With the emergence of Urban Air Mobility (UAM), eVTOLs are set to revolutionize the way of traveling. However, there will be situations where things do not go as planned, such as medical emergencies during a flight. Due to the lack of cabin crew, tailored support measures must be identified to ensure passenger safety and well-being. To explore how potential passengers perceive medical emergencies on board as bystanders and what kind of assistance they expect, we conducted a VR study. The results show significant increases in negative emotions during a medical in-flight emergency. Moreover, support measures involving human interaction were rated significantly higher than those relying solely on information displayed on onboard screens. Particularly well-received were pilot announcements. For UAM to enter the market successfully, it is essential to address passenger needs in critical situations. Our findings provide valuable insights into shaping effective and user-centered emergency support strategies for eVTOL operations. UR - https://doi.org/10.1145/3743049.3748549 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3748549 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61761 SN - 979-8-4007-1582-2 SP - 755 EP - 760 PB - ACM CY - New York ER - TY - JOUR A1 - Klages, Anna-Lisa T1 - Andreas Schadauer: Wissen in Zahlen? Zur Herstellung quantitativen Wissens in der Sozialwissenschaft. Bielefeld: transcript 2022, 254 S., ISBN 978-3-8376-6398-3, 45,00 € JF - ZQF – Zeitschrift für Qualitative Forschung N2 - Mit der auf seiner Dissertation aufbauenden Monografie Wissen in Zahlen? Zur Herstellung quantitativen Wissens in der Sozialwissenschaft, zeigt Andreas Schadauer anhand von zwei empirischen Fallstudien auf, wie Umfragedaten zunächst zu Zahlen und Statistiken werden, denen im weiteren Verlauf der Rezeption ein nahezu faktischer Status der Objektivität zugeschriebenwird. Dafür zeichnet er in der ersten Fallstudie den Weg der Daten nach, die im Rahmen der Household Finance and Consumption Survey (HFCS) der Österreichischen Nationalbank (OeNB) zwischen 2010–2011 generiert wurden. Als zweite Fallstudie wählt er die österreichische Immobilienvermögenserhebung von 2008, die zum Zeitpunkt der Feldforschung bereits abgeschlossen war. Beide Datensätze wurden mit dem Anspruch an Repräsentativität generiert und stellen in der österreichischen Debatte um die nationale Vermögensverteilung wichtige Referenzen dar. Mit den „multi-sited“ (S. 19, 59 ff., Hervorh. i. Orig.) Fallstudien verfolgt Schadauer zwei Ziele: Zum einen hinterfragt er ein in vielen Gesellschaftsteilen vorherrschendes normativ-positivistisches Wissenschaftsverständnis, welches er als elementar für die unkritische Rezeption von Statistiken als die Abbildung von Realität im Singular sieht. Zum anderen stellt er sich „gegen die Vorstellung, Wissenschaft werde von der Gesellschaft determiniert und Erfolg hänge dann davon ab, was gesellschaftlich vorgegeben und akzeptiert wird (vgl. z. B. Bloor, 1991)“ (S. 16). Um sich diesen Zielen anzunähern, geht er der Frage nach, wie Zahlen und Statistiken so wichtig werden, dass sie mediale wie politische Diskurse formen, gar für Gesellschaftsgruppen beziehungsweise eine ganze Nation sprechen können. UR - https://doi.org/10.3224/zqf.v26i1.09 Y1 - 2025 UR - https://doi.org/10.3224/zqf.v26i1.09 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61689 SN - 2196-2138 VL - 26 IS - 1-2025 SP - 116 EP - 120 PB - Verlag Barbara Budrich CY - Leverkusen ER - TY - JOUR A1 - Gaedicke, Sonja A1 - Haubold, Rebekka A1 - Klages, Anna-Lisa A1 - Rhein, Christian A1 - Schwertel, Tamara A1 - Tietje, Olaf T1 - Conference Report: Mapping Situational Analysis—An International Conference JF - Forum Qualitative Sozialforschung / Forum: Qualitative Social Research (FQS) N2 - In this conference report we provide insights into some of the key topics presented at the first international conference on situational analysis in Germany, held in November 2024 in Magdeburg. Central issues discussed include collaborations and feminist perspectives in practice for power-sensitive knowledge production, nonhumans and how they can be included in situational analysis through all types of mappings as well as methodical and methodological questions regarding mapping processes, interdisciplinary mapping, and the conceptualization of space in and through situational analysis. Adele CLARKE and her work were honored and celebrated at the conference. UR - https://doi.org/10.17169/fqs-26.2.4424 Y1 - 2025 UR - https://doi.org/10.17169/fqs-26.2.4424 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59992 SN - 1438-5627 VL - 26 IS - 2 PB - Freie Universität Berlin CY - Berlin ER - TY - CHAP A1 - Peintner, Jakob A1 - Malve, Bhavana A1 - Sadeghian, Shadan A1 - Riener, Andreas T1 - Driving Together: An Analysis of Passengers' Needs and Desire for Cooperative Control in Automated Vehicles T2 - MuC´25: Proceedings of the 2025 Conference on Mensch und Computer N2 - Driving automation aims to enhance comfort, safety, and traffic flow by removing the human driver from the control loop. However, the human experience of commuting involves more than just reaching a destination or assuming the role of a driver. Factors like personal driving style and courtesy towards fellow road users are integral to the driving experience but often overlooked in the development of driving algorithms for automated vehicles. In this study, we explored the needs of passengers in highly automated vehicles. A qualitative use case analysis was conducted (N=16). In a second study, N=15 participants experienced the resulting use cases in an automated vehicle. In these scenarios, they were able to interact with the automation through a cooperation HMI. Results indicate that most participants expressed a desire for cooperative driving, albeit varying with the driving situation. Moreover, allowing cooperation improves passengers’ overall experience by satisfying psychological needs for autonomy, security, competence, and relatedness. UR - https://doi.org/10.1145/3743049.3743061 Y1 - 2025 UR - https://doi.org/10.1145/3743049.3743061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-62112 SN - 979-8-4007-1582-2 SP - 333 EP - 344 PB - ACM CY - New York ER - TY - CHAP A1 - Maksimovski, Daniel A1 - Festag, Andreas A1 - Facchi, Christian T1 - A Survey on Decentralized Cooperative Maneuver Coordination for Connected and Automated Vehicles T2 - Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - V2X communications can be applied for maneuver coordination of automated vehicles, where the vehicles exchange messages to inform each other of their driving intentions and to negotiate for joint maneuvers. For motion and maneuver planning of automated vehicles, the cooperative maneuver coordination extends the perception range of the sensors, enhances the planning horizon and allows complex interactions among the vehicles. For specific scenarios, various schemes for maneuver coordination of connected automated vehicles exist. Recently, several proposals for maneuver coordination have been made that address generic instead of specific scenarios and apply different schemes for the message exchange of driving intentions and maneuver negotiation. This paper presents use cases for maneuver coordination and classifies existing generic approaches for decentralized maneuver coordination considering implicit and explicit trajectory broadcast, cost values and space-time reservation. We systematically describe the approaches, compare them and derive future research topics. UR - https://doi.org/10.5220/0010442501000111 KW - V2X Communications KW - Cooperative Driving KW - Maneuver Coordination KW - Automated Vehicle Y1 - 2021 UR - https://doi.org/10.5220/0010442501000111 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14806 SN - 978-989-758-513-5 SN - 2184-495X SP - 100 EP - 111 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Hegde, Anupama A1 - Stahl, Ringo A1 - Lobo, Silas A1 - Festag, Andreas T1 - Modeling Cellular Network Infrastructure in SUMO T2 - SUMO Conference Proceedings N2 - Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today’s 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance. Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have. In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode. UR - https://doi.org/10.52825/scp.v2i.97 Y1 - 2022 UR - https://doi.org/10.52825/scp.v2i.97 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-14785 SN - 2750-4425 VL - 2 SP - 99 EP - 113 PB - TIB Open Publishing CY - Hannover ER - TY - JOUR A1 - Manger, Carina A1 - Vogl, Annalena A1 - Riener, Andreas T1 - Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models JF - Applied Sciences N2 - To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings. UR - https://doi.org/10.3390/app152011030 Y1 - 2025 UR - https://doi.org/10.3390/app152011030 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63831 SN - 2076-3417 VL - 15 IS - 20 PB - MDPI CY - Basel ER - TY - JOUR A1 - Axmann, Bernhard A1 - Harmoko, Harmoko T1 - The Assessment Model of Robotic Process Automation (RPA) Project Using Benefit Study and Balanced Scorecard (BSC) Approach JF - Tehnički glasnik N2 - Assessing Robotic Process Automation (RPA) projects is challenging, especially for small and medium-sized enterprises (SMEs) with limited resources. The most common approach, cost-benefit analysis, measures the financial success of a project, but it does not capture all RPA benefits, which can lead to erroneous decisions about starting, continuing, delaying, or stopping an RPA project. This research presents a novel RPA project assessment model that combines a benefits approach with the balanced scorecard (BSC) framework. The model identifies RPA benefits from the scientific literature and classifies them into four BSC indicators: financial, business process improvement, customer satisfaction, and learning and development. SMEs can easily use the model by comparing expected and actual benefits to make quick and holistic decisions. The model's simplicity, ease of use, and ability to reveal both monetary and non-monetary RPA benefits make it a valuable tool for SMEs. UR - https://doi.org/10.31803/tg-20240416111054 Y1 - 2024 UR - https://doi.org/10.31803/tg-20240416111054 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64252 SN - 1848-5588 VL - 18 IS - 2 SP - 246 EP - 253 PB - University North CY - Koprivnica ER - TY - JOUR A1 - Agostinelli, Simone A1 - Asatiani, Aleksandre A1 - Axmann, Bernhard A1 - Hannibal, Glenda A1 - Modlinski, Artur ED - del-Río-Ortega, Adela ED - Marrella, Andrea ED - Reijers, Hajo A. ED - Wilde, Adriana T1 - Trust Calibration for RPA JF - Dagstuhl Reports N2 - This working group focused on foundational issues of trust in RPA: How we can conceptualize trust between users and software robots and specify it in a way that, eventually, we can measure that trust empirically. UR - https://doi.org/10.4230/DagRep.14.7.52 Y1 - 2025 UR - https://doi.org/10.4230/DagRep.14.7.52 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64681 SN - 2192-5283 VL - 14 IS - 7 SP - 61 EP - 64 PB - Schloss Dagstuhl CY - Dagstuhl ER - TY - CHAP A1 - Michl, Marco A1 - Hof, Hans-Joachim A1 - Katzenbeisser, Stefan T1 - Classification, Impact, and Mitigation Strategies of Attacks in Automotive Trust Management Systems T2 - Proceedings of the 2024 on Cyber Security in CarS Workshop (CSCS ’24) N2 - With the advent of connected vehicles, which provide many new comfort functions, the attack surface increased as well. Widespread "hard security" mechanisms are used to secure the vehicle and its infrastructure, but they cannot mitigate all threats, especially if inside attackers are considered. "Soft security" mechanisms, like trust and reputation systems, can help to close the attack surface further. However, such systems add complexity and can also lead to further vulnerabilities. To use such mechanisms securely, this work analyzes attacks targeting trust management systems in the automotive domain. For this purpose, a literature review was conducted, and the extracted attacks were characterized and divided into several clusters, introducing a new nomenclature of attacks against trust management for vehicles. We argue that four main categories of attacks exist. They are distinguished by their target component: communication channel, trust management layer, or application. As a fourth category, we see attacks that are enabled by the use of trust management systems. The outcome of this analysis was used to create an adversary model based on detected attack vectors and to define abstract reference attackers. These can be used to evaluate the resilience of automotive trust management systems against the analyzed attacks. Mitigation strategies against the adversary's capabilities are further presented to avoid vulnerabilities in the design of automotive trust management systems. UR - https://doi.org/10.1145/3689936.3694691 Y1 - 2024 UR - https://doi.org/10.1145/3689936.3694691 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53694 SN - 979-8-4007-1232-6 SP - 61 EP - 75 PB - ACM CY - New York ER - TY - CHAP A1 - Ullrich, Henning A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Application of Quantum Key Distribution in Intelligent Transport Systems T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Quantum Key Distribution (QKD) enables two parties to generate a secret key, based on quantum physical properties. The secrecy of the exchanged key is guaranteed by these quantum physical properties, even against an attacker using a quantum computer. QKD therefore offers an Quantum Information Theoretically Secure (Q-ITS) alternative to Post Quantum Cryptography (PQC) schemes. This paper discusses advantages and challenges of deploying a QKD system in an Intelligent Transport System (ITS), by examining two exemplary use cases. Approaches to effectively implement QKD in these use cases are proposed, with a short discussion of advantages over the use of classical and PQC schemes. UR - https://doi.org/10.1145/3736130.3762692 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3762692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63186 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - CHAP A1 - Michl, Marco A1 - Hof, Hans-Joachim A1 - Katzenbeisser, Stefan T1 - From Access to Trust: Policy-Based Automotive Trust Management With XACML T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Trust is a fundamental requirement in vehicular systems, particularly in the context of autonomous and cooperative driving, where decisions often depend on data received from other vehicles or infrastructure. This paper presents a novel approach to integrating trust-based reasoning—specifically, decision-making based on subjective logic—into Attribute-Based Access Control (ABAC) systems, using the XACML policy language. We draw a parallel between trust management and access control, showing that trust assessments can be modeled as access decisions and expressed through standard access control policies. This integration allows for the reuse of mature ABAC infrastructure to represent and enforce trust-based decisions. Furthermore, we demonstrate how trust discounting mechanisms, which combine node-based and data-based trust, can be implemented within XACML policies. To evaluate this approach, we introduce a Rust-based XACML engine capable of interpreting trust-enhanced policies. Its integration in both inter- and intra-vehicle scenarios is discussed, assuming a modern zone-based architecture. Our results suggest that policy-based trust management can be efficiently implemented, enabling flexible and standardized trust reasoning in automotive environments. UR - https://doi.org/10.1145/3736130.3764511 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3764511 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64731 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - CHAP A1 - Laves, Claudius A1 - Hutzelmann, Thomas A1 - Mayer, Kevin A1 - Hof, Hans-Joachim T1 - Driving Down Premiums: A Security and Forensic Analysis of the DriveWell Insurance System T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - Modern car insurance companies can dynamically adjust premiums with mobile sensor units that constantly record, analyze, and grade driving behavior. However, attackers can tamper with these systems to cheat the insurance company and reduce their premiums. We developed an attacker model for this threat in conjunction with a risk assessment using the ISO/SAE 21434 Risk Rating Methodology, a widely accepted standard for evaluating and managing risks in automotive systems. To support and showcase our model, we examined the static IT security of the mobile sensor unit DriveWell and its companion Android application HUK Mein Auto. In particular, we utilized digital forensic tools to analyze the stored data in the sensor’s internal storage and the app’s smartphone storage, with a focus on encryption mechanisms for locally stored data and internet communication. During this analysis, we spotted a critical vulnerability that enables attackers to manipulate their insurance scores and potentially reduce premiums by up to 30%. Our model and real-world analysis provide a solid foundation for reducing the risk of such manipulation of insurance premiums. UR - https://doi.org/10.1145/3736130.3762688 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3762688 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64728 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER - TY - JOUR A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Hanß, Alexander A1 - Böckhorst, Stephan A1 - Elger, Gordon T1 - Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part II - Reliability JF - IEEE Transactions on Device and Materials Reliability UR - https://doi.org/10.1109/TDMR.2023.3300355 KW - LED KW - non-destructive testing KW - reliability KW - solder KW - scanning acoustic microscopy (SAM) KW - thermal impedance (Zth) KW - thermal resistant (Rth) KW - X-ray KW - transient thermal analysis (TTA) Y1 - 2023 UR - https://doi.org/10.1109/TDMR.2023.3300355 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39651 SN - 1558-2574 SN - 1530-4388 VL - 23 IS - 3 SP - 419 EP - 429 PB - IEEE CY - New York ER - TY - JOUR A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Böckhorst, Stephan A1 - Elger, Gordon T1 - Investigations on High-Power LEDs and Solder Interconnects in Automotive Application: Part I - Initial Characterization JF - IEEE Transactions on Device and Materials Reliability N2 - Thermo-mechanical reliability is one major issue in solid-state lighting. Mismatches in the coefficients of thermal expansion (CTE) between high-power LED packages and substrates paired with temperature changes induce mechanical stress. This leads to a thermal degradation of LED modules by crack formation in the solder interconnect and/or delamination in the substrate, which in turn increases junction temperature and thus decreases light output and reduces lifetime. To investigate degradation and understand influence of LED package design and solder material, a reliability study with a total of 1800 samples − segmented in nine LED types and five solder pastes − is performed. First of all, in this paper a state-of-the-art review of high-power LED packages is performed by analyzing and categorizing the packaging technologies. Second, the quality inspection after assembly is realized by transient thermal analysis (TTA), scanning acoustic microscopy (SAM) and X-ray. For TTA, a new method is introduced to separate the thermal resistance of the LED package from solder interconnect and substrate by applying the transient dual interface method (TDI) on samples with different solder interconnect void ratios. Further measurement effort is not required. The datasheet values for thermal resistance are verified and the different LED package types are benchmarked. The void ratio of the solder interconnects is determined by X-ray inspection combined with an algorithm to suppress disruptive internal LED package structures. TTA and TDI revealed that initial thermal performance is independent of solder paste type and that voiding is more critical to smaller LED packages. In addition, lower silver proportion in the paste is found to increase voiding. SAM is less sensitive for initial void detection than X-ray, but it’s applied to monitor crack propagation while aging in combination with TTA. The results of the reliability study, i.e., the crack growth under temperature shock test for the different SAC solders, will be presented in a second independent paper. UR - https://doi.org/10.1109/TDMR.2022.3152590 KW - LED KW - non-destructive testing KW - reliability KW - solder KW - scanning acoustic microscopy (SAM) KW - thermal impedance (Zth) KW - thermal resistant (Rth) KW - transient thermal analysis (TTA) KW - X-ray Y1 - 2022 UR - https://doi.org/10.1109/TDMR.2022.3152590 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28799 SN - 1530-4388 SN - 1558-2574 VL - 22 IS - 2 SP - 175 EP - 186 PB - IEEE CY - New York ER - TY - JOUR A1 - Rouegui, Meryem A1 - Bellabair, Hind A1 - El Asli, Abdelghani A1 - Amar, Amine A1 - Zörner, Wilfried A1 - Rachidi, Fouad A1 - Lghoul, Rachid T1 - Bio-Methanization of Sheep Manure and Beet Waste in the Meknes–Fès Region, Morocco: Effects of Pretreatment and Machine Learning Applications for Biochemical Methane Potential Prediction JF - Recycling N2 - Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical–thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH4/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16%, 25%, and 60% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH4/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste. UR - https://doi.org/10.3390/recycling10060213 Y1 - 2025 UR - https://doi.org/10.3390/recycling10060213 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64862 SN - 2313-4321 VL - 10 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Zippelius, Andreas A1 - Elger, Gordon T1 - Solder joint lifetime model using AI framework operating on FEA data JF - Engineering Failure Analysis N2 - The thermo-mechanical reliability of electronic systems is often limited by the crack growth within the solder joints. Addressing this issue requires careful consideration of the design of the package and solder pads. Finite Element Analysis (FEA) is widely used to predict crack growth and to model their lifetime. Traditionally, FEA post-processing methods rely on human expertise to select appropriate regions for evaluating plastic and creep strain at critical locations and correlating these values with experimental data using the Coffin-Manson equation, which predicts fatigue lifetime based on cyclic plastic strain. This study introduces a novel method for FEA post-processing of surface-mounted devices (SMD) on printed circuit boards (PCB) using artificial intelligence. The method transforms the FEA data into a 2D grid map of creep strain values and employs a Convolutional Neural Network (CNN) for automatic feature extraction. Afterwards, a fully connected layer correlates the extracted features with the experimental measured solder joint lifetime, effectively capturing nonlinear relationships. The study focuses on the development of the concept of crack formation in the solder interconnects of ceramic based high-power LED packages used in the automotive industry for headlights. The validated FEA model is based on an extensive data set of 1800 LED packages including seven different ceramic-based LED packages and five different solders. The design of the ceramic LED package covers two-pad and three-pad footprint for soldering and thin film and thick film metallized ceramic carriers. Results show a strong agreement (R2 Score is 99.867 %) between simulations and experimental data for ceramic LED packages. This automatic feature extraction from FEA data sets a new benchmark for improving solder reliability predictions, and it has proved to be better than established methods for lifetime prediction of solder joints. UR - https://doi.org/10.1016/j.engfailanal.2024.109032 Y1 - 2024 UR - https://doi.org/10.1016/j.engfailanal.2024.109032 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53803 SN - 1350-6307 VL - 2025 IS - 167, Part B PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data JF - Scientific Reports N2 - Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method. UR - https://doi.org/10.1038/s41598-025-06902-0 Y1 - 2025 UR - https://doi.org/10.1038/s41598-025-06902-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-60961 SN - 2045-2322 VL - 15 PB - Springer Nature CY - London ER - TY - INPR A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - AI-Driven Point Cloud Framework for Predicting Solder Joint Reliability using 3D FEA Data T2 - Research Square N2 - Crack propagation in solder joints remains a critical challenge affecting the thermo-mechanical reliability of electronic devices, emphasizing the need for optimized package and solder pad designs. Traditional Finite Element Analysis (FEA) methods for predicting solder joint lifespan rely heavily on manual post-processing, where high-risk regions for plastic strain accumulation are identified. However, these approaches often overlook intricate failure mechanisms, as they primarily average creep strain and correlate it with experimental lifetime data using the Coffin-Manson equation, limiting their predictive accuracy. To overcome these limitations, this study introduces a novel AI-driven framework that automates 3D FEA post-processing for surface-mounted devices (SMDs) connected to printed circuit boards (PCBs). Unlike traditional methods, this framework leverages deep learning architectures—specifically, 3D Convolutional Neural Networks (CNNs) and PointNet—to extract complex spatial features directly from 3D FEA data, eliminating the need for manual interpretation. These learned features are then mapped to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture nonlinear failure behaviours that conventional methods fail to recognize. The research focuses on crack propagation in ceramic-based high-power LED packages used in automotive lighting systems, incorporating variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates with validated FEA models. Comparative analysis shows that PointNet significantly outperforms 3D CNNs, achieving an exceptionally high correlation with experimental data (R² = 99.99%). This AI-driven automated feature extraction and lifetime prediction approach marks a major advancement over traditional FEA-based methods, offering superior accuracy, reliability, and scalability for predicting solder joint reliability in microelectronics. UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 Y1 - 2025 UR - https://doi.org/10.21203/rs.3.rs-6173485/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58308 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Zippelius, Andreas A1 - Mohd, Zubair Akhtar A1 - Schmid, Maximilian A1 - Elger, Gordon T1 - Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks JF - IEEE Transactions on Device and Materials Reliability N2 - Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures. UR - https://doi.org/10.1109/TDMR.2025.3633876 Y1 - 2025 UR - https://doi.org/10.1109/TDMR.2025.3633876 SN - 1558-2574 PB - IEEE CY - New York ER - TY - INPR A1 - Aubreville, Marc A1 - Stathonikos, Nikolas A1 - Donovan, Taryn A1 - Klopfleisch, Robert A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Wilm, Frauke A1 - Veta, Mitko A1 - Jabari, Samir A1 - Eckstein, Markus A1 - Annuscheit, Jonas A1 - Krumnow, Christian A1 - Bozaba, Engin A1 - Cayir, Sercan A1 - Gu, Hongyan A1 - Chen, Xiang A1 - Jahanifar, Mostafa A1 - Shephard, Adam A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Kotte, Sujatha A1 - Saipradeep, Vangala A1 - Lafarge, Maxime W. A1 - Koelzer, Viktor H. A1 - Wang, Ziyue A1 - Zhang, Yongbing A1 - Yang, Sen A1 - Wang, Xiyue A1 - Breininger, Katharina A1 - Bertram, Christof T1 - Domain generalization across tumor types, laboratories, and species – Insights from the 2022 edition of the Mitosis Domain Generalization Challenge N2 - Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert consensus and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, but with only minor changes in the order of participants in the ranking. UR - https://doi.org/10.48550/arXiv.2309.15589 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2309.15589 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41514 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Haghofer, Andreas A1 - Parlak, Eda A1 - Bartel, Alexander A1 - Donovan, Taryn A1 - Assenmacher, Charles-Antoine A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Fuchs-Baumgartinger, Andrea A1 - Klang, Andrea A1 - Jäger, Kathrin A1 - Klopfleisch, Robert A1 - Merz, Sophie A1 - Richter, Barbara A1 - Schulman, F. Yvonne A1 - Ganz, Jonathan A1 - Scharinger, Josef A1 - Aubreville, Marc A1 - Winkler, Stephan M. A1 - Kiupel, Matti A1 - Bertram, Christof T1 - Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors N2 - Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area ≥9.0μm2) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area ≥10.9μm2) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair (κ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements. UR - https://doi.org/10.48550/arXiv.2309.15031 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2309.15031 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41401 PB - arXiv CY - Ithaca ER -