Refine
Institute
- Fakultät Informatik (597)
- Fakultät Elektro- und Informationstechnik (463)
- THI Business School (412)
Version
- published (858)
Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.
The wireless communication technology has gained significant attention in the transportation industry over the recent years. Cellular Vehicle-to-Everything (V2X) communication facilitates the information exchange among road users (such as vehicles, pedestrians etc.) and the infrastructure with an intention to improve the overall road safety, driving comfort, traffic efficiency and save energy. Advanced use-cases aim towards enhancing key functionalities of vehicle automation by means of sensor data sharing and cooperative maneuver & trajectory planning. The introduction of the PC5 interface for sidelink (SL) communication within the mobile communication systems, supports direct exchange of messages between users, independent of the cellular network infrastructure.
Two types of radio resource allocation modes are supported in Cellular V2X: managed mode and the unmanaged mode. In the managed mode, a User Equipment (UE) remains connected to the cellular network and the process of resource allocation is coordinated by the base station. In the unmanaged mode, a UE selects its radio resources from a pre-configured resource pool without any assistance from the base station. Originally both these modes were developed by considering that the vehicles exchange periodic messages which are safety-critical in nature. The existing rule based radio resource allocation algorithms in both the modes are unable to adapt their selection parameters in the events of aperiodic data traffic patterns resulting from the diverse generation rules of different V2X messaging protocols.
We begin this PhD thesis by carrying out system level network simulations within the developed framework Artery-C, where we study the metrics and parameters that influence the performance of the rule-based radio resource allocation in the sidelink modes. In the first step, we derive the baseline conditions where each mode performs to its best efficiency. By varying the generation rules of the messaging protocols, we further analyze the behavior of the modes when V2X data traffic does not follow a specific pattern. Our studies have shown that both the modes suffer from frequent re-allocations because the messages are no longer periodic and the data sizes do not fit into the previously allocated radio resources. This results in poor utilization of the allocated resources. The unmanaged mode is particularly susceptible to radio resource collisions because the vehicles only have partial awareness about the resource selection decisions of other road traffic participants.
As a second contribution, we examine the criteria for sidelink mode selection and the possibilities for a mode switching operation within the sidelink modes and also between the sidelink and the cellular (Uu) modes. We have formulated the strategies for mode switching and calculate the latency in each phase of the mode switch procedure. Although the managed mode has shown advantages with regard to allocation and management of radio resources, it is to be noted that a vehicle cannot remain connected to a base station at all instants of time. Also, switching between different modes is not seamless considering the associated latencies in each phase. This leads us towards the goal of improving the efficiency of the allocation & scheduling of radio resources in the unmanaged mode.
After a careful review of the enhancements that can be implemented within the rule based algorithm in the unmanaged mode, it was found that there needs to be a mechanism where vehicles can continuously share their resource selection decisions, adapt their selection parameters and even re-evaluate them (if needed) within a grant period. Therefore, we investigated the Reinforcement Learning (RL) based Artificial Intelligence (AI) approaches that facilitate independent learning, adapting and decision making among spatially distributed vehicular agents. We have developed a fully decentralized multi agent networked Markovian Decision Process (MDP) model of the Cellular V2X communication network where each agent executes an AI based radio resource scheduler. By extending the actor-critic methodology of the RL, we have derived two variants - Independent Actor Critic (IAC) and Shared Experience Actor Critic (SEAC). The results of our evaluations have indicated that both these schedulers have a potential to achieve better radio resource utilization with a reduced risk of radio resource collisions among the agents. Subsequently, it brings about 15 − 20% improvement in the reliability of the communication link which we regard as a valuable contribution. To summarize, this PhD thesis investigates the performance of the rule based radio resource allocation algorithms in Cellular V2X and proposes the qualitative improvements that can be achieved by means of reinforcement learning.
Clustering and Anomaly Detection in Embedding Spaces for the Validation of Automotive Sensors
(2024)
This paper examines the relationship between corporate purpose and disaster response. To that aim, we use a unique dataset generated by Just Capital's COVID‐19 response tracker that characterizes the early responses of 928 companies included in the Russell 1000 Index generated by the COVID‐19 pandemic. We marry this data to the Business Roundtable Statement on the Purpose of a Corporation (BRT‐CPS) signatories list, which includes 210 firms that declared “a fundamental commitment to all stakeholders” as their corporate purpose. We estimate the differential impact of BRT‐CPS signees on the rest. After controlling our results with financial indicators and environmental, social, and governance (ESG) data, we convey that BRT‐CPS signees were more likely to develop strategies that benefited their surrounding communities and other stakeholders. Moreover, corporations with high ESG were less likely to have an early response that directly benefited stakeholders other than shareholders, except for layoffs and unpaid leave. Finally, we discuss the outcomes considering the symbolic management theory, corporate purpose literature, and its relation to stakeholder management.
A novel hybrid copper paste was developed for low temperature sintering of bare semiconductors. Cu(II) formate (Cu(for)) is complexed in amino-2-propanol (A2P) and added to a paste of etched brass micro flakes. A two-step sintering process is applied: The paste is printed and dried at 120 °C under formic acid (FA) enriched N2 atmosphere (FAN2) for 5 min. Afterwards, bare semiconductors are placed and sintered at 250 °C for 5 min applying a bonding pressure of 20 MPa/10 MPa. By the thermal decomposition of the Cu(for) atomic Cu is released and forms in-situ Cu-nanoparticles. An interconnect is realized with shear strength >100 MPa.
Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker.
The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.
ÖPNV für Kinder
(2024)
Diese Bachelorarbeit befasst sich mit der nutzerzentrierten Konzeption hochautomatisierter Shuttlebusse im öffentlichen Personennahverkehr (ÖPNV) mit einem besonderen Fokus auf die Bedürfnisse von Kindern und deren Eltern. Ziel der Arbeit war es, herauszufinden, wie Shuttlebusse gestaltet werden müssen, um sie für Kinder optimal nutzbar zu machen und gleichzeitig das Vertrauen der Eltern in die Technologie zu gewinnen. Um die Perspektiven der zwei Zielgruppen besser nachvollziehen zu können, wurden Interviews mit Schulkindern und eine Onlineumfrage mit den Eltern durchgeführt. Basierend auf den Erkenntnissen wurden dann Prototypen entwickelt und in Nutzertests evaluiert. Die Ergebnisse der Studie mit Kindern zeigen, dass diese positiv und offen gegenüber der neuen Technologie eingestellt sind. Zudem lässt sich erkennen, dass den Kindern vor allem ein großes Medienangebot während der Fahrt wichtig ist. Die Studienergebnisse der Eltern haben gezeigt, dass durch erhöhte Level an Echtzeitinformationen, während der Fahrt der Kinder, die Zufriedenheit gesteigert wird. Zusätzlich dazu, wird der Trend erkennbar, dass durch die Bereitstellung vermehrter Informationen auch das Vertrauen in das System ansteigen kann. Die Arbeit liefert einige Empfehlungen dazu, wie die Bedürfnisse beider Zielgruppen miteinander vereint werden können.
This master thesis compares the environmental impacts of 4th-generation district heating (4GDH) and 5th-generation district heating and cooling (5GDHC) systems using different heat sources in Germany. The primary objective is to evaluate and compare the environmental performance of a geothermal-based 5GDHC system with a gas-combined heat and power (CHP) based 4GDH system.
The study follows DIN ISO 14040/14044 standards and employs the SimaPro software with the ecoinvent 3.10 database to perform the LCA. The analysis considers two scenarios: one using the German electricity grid and the other using photovoltaic (PV) panels as the electricity source. Key impact categories assessed include climate change, acidification, freshwater and marine eutrophication, human toxicity and resource depletion.
Results indicate that the 5GDHC system, despite its advanced technology, has higher overall environmental impacts compared to the 4GDH system in most categories, primarily due to the intensive resource use and higher greenhouse gas emissions during the drilling process and operational phases. However, 5GDHC shows lower impacts on human toxicity (cancer-related) and land use efficiency, highlighting areas where it performs better than 4GDH.
This comparative study underscores the need for further optimization and integration of renewable energy sources in 5GDHC systems to enhance their sustainability and reduce their environmental impacts. The findings provide valuable insights for policymakers and stakeholders in the energy sector aiming to achieve carbon neutrality and improve the environmental performance of district heating systems.
Keywords: Comparative life cycle assessment (LCA); Life cycle inventory (LCI); District heating system (DHS); 5th generation district heating and cooling system (5GDHC); 4th generation district heating system (4GDH); Geothermal energy; Combined heat and power (CHP); SimaPro; Ecoinvent
The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).
Lithium-Ion Batteries (LIBs) usually present several degradation processes, which include their complex Solid-Electrolyte Interphase (SEI) formation process, which can result in mechanical, thermal, and chemical failures. The SEI layer is a protective layer that forms on the anode surface. The SEI layer allows the movement of lithium ions while blocking electrons, which is necessary to prevent short circuits in the battery and ensure safe operation. However, the SEI formation mechanisms reduce battery capacity and power as they consume electrolyte species, resulting in irreversible material loss. Furthermore, it is important to understand the degradation reactions of the LIBs used in Electric Vehicles (EVs), aiming to establish the battery lifespan, predict and minimise material losses, and establish an adequate time for replacement. Moreover, LIBs applied in EVs suffer from two main categories of degradation, which are, specifically, calendar degradation and cycling degradation. There are several studies about battery degradation available in the literature, including different degradation phenomena, but the degradation mechanisms of large-format LIBs have rarely been investigated. Therefore, this review aims to present a systematic review of the existing literature about LIB degradation, providing insight into the complex parameters that affect battery degradation mechanisms. Furthermore, this review has investigated the influence of time, C-rate, depth of discharge, working voltage window, thermal and mechanical stresses, and side reactions in the degradation of LIBs.
Background
Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.
Patients and methods
15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).
Results
Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.
Conclusions
We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.
Die Unternehmensberatung OSCAR GmbH und der Autor dieses Beitrags führen im Rahmen einer Kooperationsveranstaltung regelmäßig gemeinsam ein Case Study-Training für ein Assessment Center mit Studierenden der Betriebswirtschaft durch. Die Veranstaltung findet in einem rein digitalen Format live via Zoom statt und wird anschließend um ein Peer Review-Verfahren via Moodle ergänzt, bei dem die Studierenden ihre Gruppenlösungen zur Case Study gegenseitig beurteilen. Nach einem Überblick über das Gesamtkonzept skizziert der vorliegende Beitrag die Vorbereitung in Moodle, die Umsetzung in der Haupt-Session und den Breakout Sessions in Zoom und schließlich die Überführung der Gruppen mit ihren Arbeitsergebnissen von Zoom nach Moodle, um hier die gegenseitige Beurteilung zu ermöglichen. Der Beitrag wendet sich nicht nur an Interessierte im Themenfeld Assessment Center, sondern auch generell an Lehrende, die an einem kombinierten Einsatz von Zoom und Moodle im Kontext von Gruppenarbeit und Feedback interessiert sind.
Digitalisierung – Motor für innovative Geschäftsmodelle im Umfeld des hochautomatisierten Fahrens
(2024)
Digital Trust Management
(2024)
Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: “Stationary Vehicle Warning—Broken-Down Vehicle” and “Dangerous Situation—Electronic Emergency Brake Lights”. Volkswagen’s warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.
Digital Leadership
(2024)
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
(2024)
Digitale Transformation
(2024)
Digital Communication – Systematisches Social-Media-Marketing auf der Basis des Social-Media-Zyklus
(2024)
Digital Procurement
(2024)
The conversion from water-based to solvent-based paint systems, particularly Ultra High Solid (UHS) formulations, is a unique and significant undertaking for enhanced efficiency, sustainability, and environmental responsibility within industrial paint shop operations. This study outlines a comprehensive conversion concept explicitly tailored for the MAN Truck & Bus SE cab paint shop, emphasizing its unique energy-saving strategies, sustainability measures, and environmental considerations.
The development of the conversion concept actively integrates scientific principles, advanced technologies, and strategic methodologies to optimize energy utilization throughout the paint application and curing processes. Key elements of the concept actively include the implementation of energy-efficient equipment, such as infrared drying systems and regenerative thermal oxidizers, to minimize energy consumption and actively reduce greenhouse gas emissions associated with paint curing.
Furthermore, sustainability objectives are addressed through adopting eco-friendly practices, such as using low-VOC (Volatile Organic Compound) solvent formulations and integrating closed-loop solvent recovery systems to minimize waste and promote resource efficiency. Life cycle assessment methodologies are employed to evaluate the environmental impacts of the conversion process, enabling informed decision-making regarding material selection, process optimization, and waste management strategies.
The proposed conversion concept is underpinned by a holistic approach to energy management, sustainability, and environmental stewardship, aligning with MAN Truck & Bus SE's commitment to corporate social responsibility and ecological sustainability. By implementing energy-saving measures, adopting sustainable practices, and adhering to stringent environmental regulations, the conversion to a solvent-based (UHS) paint system not only enhances operational efficiency and product quality but also contributes to reducing the carbon footprint and environmental impact associated with automotive paint manufacturing.
Developing an integrated conversion concept from water to solvent-based paint (UHS) system in the MAN Truck & Bus SE cab paint shop signifies a significant step towards achieving energy efficiency, sustainability, and environmental responsibility in industrial paint shop operations. This conversion concept fosters sustainable growth and drives positive ecological change within the automotive manufacturing sector by prioritizing energy-saving initiatives, embracing sustainable practices, and mitigating environmental risks.
Gerade in Zeiten des Fachkräftemangels ist es wichtig, neue Strategien zur Gewinnung von Fachkräften zu implementieren. Die vorliegende Arbeit widmet sich daher dem Thema der Relevanz von Social Media Kanälen in der Gewinnung von Auszubildenden im speziellen im Bereich der Kindertageseinrichtungen. Hierbei werden unter anderem die Themen Ausbildungsmarketing, Social Media und Social Media Recruiting in Bezug auf aktueller Fachliteratur dargestellt, um einen theoretischen Überblick über das Thema zu gewährleisten. Im Rahmen einer quantitativen Datenerhebung wurde untersucht, welche Relevanz aktuelle Erzieherauszubildende in der Nutzung von Social Media Kanälen sehen. Sowohl die theoretische Darlegung des Themas als auch die Ergebnisse der erhobenen Daten sollen als Entscheidungs- und Implementierungshilfe für Kindertageseinrichtungen dienen.
Markov reward models are commonly used in the analysis of systems by integrating a reward rate to each system state. Typically, rewards are defined based on system states and reflect the system’s perspective. From a user’s point of view, it is important to consider the changing system conditions and dynamics while the user consumes a service. The key contributions of this paper are proper definitions for (i) system-centric reward and (ii) user-centric reward of the Erlang loss model M/M/n-0 and M/M(x)/n with state-dependent service rates, as well as (iii) the analysis of the relationships between those metrics. Our key result allows a simple computation of the user-centric rewards. The differences between the system-centric and the user-centric rewards are demonstrated for a real-world cloud gaming use case. To the best of our knowledge, this is the first analysis showing the relationship between user-centric rewards and system-centric rewards. This work gives relevant and important insights in how to integrate the user’s perspective in the analysis of Markov reward models and is a blueprint for the analysis of other services beyond cloud gaming while also considering user engagement.
Optimizing High Voltage Battery Manufacturing Supply Chains: A Comprehensive Analysis Of Product Carbon Footprint And Carbon Tax Of Three Different Manufacturer’s Batteries.
This thesis presents a comprehensive analysis of the carbon footprints associated with the manufacturing supply chains of three vehicle batteries. With data provided by A2mac1 Company and conducted for both industry and academic purposes at Technical Hochschule Ingolstadt, the study focuses on optimizing battery production processes and transportation to reduce environmental impact.
In addition to analyzing carbon footprints within the battery manufacturing supply chains, the research extends its scope to evaluate the most favorable locations in terms of carbon tax implications across USA regions, Chinese states, and European cities. By integrating predictions of future emissions and carbon tax scenarios, this study provides holistic insights into the environmental sustainability of battery manufacturing practices.
Through detailed assessments of production and transportation factors, including raw material sourcing, manufacturing processes, and distribution networks, this study identifies key drivers of carbon emissions and evaluates their impact on overall environmental sustainability. The findings underscore the importance of supply chain optimization and the adoption of greener practices to mitigate carbon emissions and promote sustainability within the automotive industry.
Keywords: Battery manufacturing, Supply chain optimization, Carbon footprint analysis, Production factors, Transportation, Environmental sustainability, Automotive industry, Carbon tax implications
Diese Masterarbeit befasst sich mit der Entwicklung eines vereinfachten Modells eines elektromagnetisch betätigten Relais unter Verwendung des Simulationsprogramms COMSOL Multiphysics. Sie bietet eine detaillierte Einleitung in die mathematischen und physikalischen Konzepte der verwendeten Bauelemente sowie in die Berechnungsverfahren elektromagnetischer Felder und der Finite-Elemente-Methode (FEM). Dies verschafft ein tiefgehendes Verständnis für die Berechnung der magnetischen Flussdichte, der elektromagnetischen Kraft und der Verluste. Zudem stellt die Arbeit Strategien vor, wie der Rechenaufwand durch gezielte Vereinfachungen effizient reduziert werden kann. Die erstellte Simulation modelliert einen zeitabhängigen Schaltvorgang im dreidimensionalen Raum und visualisiert die dynamische Natur des Prozesses.
In einer Ära zunehmender Arbeitsmobilität und eines verschärften Wettbewerbs
um hochqualifizierte Arbeitskräfte erlangen anteilsbasierte
Vergütungsprogramme zunehmend an Bedeutung. Unternehmen sehen sich
vermehrt mit der Herausforderung konfrontiert, die langfristige Bindung und
Motivation ihrer Mitarbeiter zu gewährleisten. Die Integration anteilsbasierter
Vergütungsprogramme ermöglicht es Unternehmen, ihre Mitarbeiter unmittelbar
am Erfolg des Unternehmens zu beteiligen, indem sie Aktienoptionen oder
andere Beteiligungsrechte gewähren. Hierbei wird nicht nur ein finanzieller Anreiz
geboten, sondern folglich eine Anerkennung für die Leistungen der Mitarbeiter
am Unternehmenserfolg signalisiert. Trotz der Potenziale, die anteilsbasierte
Vergütungssysteme bieten, gehen mit ihnen auch Herausforderungen einher. Für
eine effiziente Umsetzung ist es unerlässlich, neben der Konzeption und der
darauffolgenden Implementierung eine akribische Planung und Abstimmung mit
den Unternehmenszielen vorzunehmen. Hierbei stellt die Berücksichtigung
bilanzieller Aspekte einen fundamentalen Aspekt dar, um die potenziellen
Auswirkungen und Effekte auf die Vermögens-, Finanz- und Ertragslage
angemessen zu analysieren. Ein zentraler Fokus dieser Untersuchung liegt auf
den regulatorischen Anforderungen des IFRS 2, welcher Unternehmen dazu
verpflichtet, anteilsbasierte Vergütungstransaktionen transparent in ihren
Finanzberichten darzustellen und somit die finanziellen Auswirkungen
offenzulegen. Dabei werden neben den grundlegenden Modalitäten der
Ausgestaltungsmöglichkeiten, miteinhergehend auch die Bewertungs- und
Bilanzierungsrichtlinien ausführlich erläutert. Durch eine Analyse der
Implementierung und Auswirkungen dieser Programme die in den DAX gelisteten
Unternehmen wird ein umfassendes Verständnis für deren Bedeutung und
Funktionalität in der Unternehmenspraxis angestrebt. Diese Erkenntnisse sollen
dazu beitragen, gegenwärtig eingesetzte Praktiken zu identifizieren und
Potenziale zu erschließen, um die Effektivität und Transparenz der Programme
weiter zu verbessern.
The QUILT-1M dataset is the first openly available dataset containing images harvested from various online sources. While it provides a huge data variety, the image quality and composition is highly heterogeneous, impacting its utility for text-conditional image synthesis. We propose an automatic pipeline that provides predictions of the most common impurities within the images, e.g., visibility of narrators, desktop environment and pathology software, or text within the image. Additionally, we propose to use semantic alignment filtering of the image-text pairs. Our findings demonstrate that by rigorously filtering the dataset, there is a substantial enhancement of image fidelity in text-to-image tasks.
For the use of the CSS model, workflows are needed to describe, assign and derive capabilities. This contribution provides a Capability Determining method that encompasses the three underlying technology-independent methods to guide stakeholders in describing, assigning and deriving capabilities for manufacturing. The first underlying method, Describing Capabilities, describes a workflow for the formal semantic description of capabilities. The workflow for assigning capabilities to a production resource is explained using the second method Assigning Capability. In the last workflow, capabilities are derived from production specification or a process description using the method Deriving Capability.
We examine the impact of water (160 ± 41 ppm of reference) on the anode, cathode, separator and electrolyte in two aging scenarios: calendric aging (60 °C, 80 days, charged state), resulting in a triggered current interrupt device (CID), and cycling 1680 times (charge/discharge with 1C, 2.75–4.2 V, 20 ± 2 °C), resulting in 24.5% residual capacity. We applied computer tomography (CT), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS) to understand the capacity loss. The aged NMC/LCO–graphite cells were compared to a reference cell in pristine state. Both aging scenarios showed (a) thick depositions on the anode and cathode consisting mainly of oxygen, fluorine and phosphorous, (b) reduced separator pore sizes, (c) the deposition of Mn, Co and Ni on top of the anode and (d) the decomposition of the conductive salt LiPF6 accompanied by HF formation and a loss of active lithium. Calendric aging consumes the water content and additionally leads to (e) the decomposition of the organic solvent followed by CO2 gas formation. Cyclic aging increases the water content and additionally results in (f) the consumption of the additive fluoroethylene carbonate (FEC). These findings show how water affects aging phenomena and results in a capacity decrease in the cell.
Fused Deposition Modeling (FDM) is a well-established manufacturing method for producing both prototype and functional components. This study investigates the mechanical properties of FDM components by material and process-related influencing variables. Tensile tests were conducted on seven different materials in their raw filament form, two of which were fiber-reinforced, to analyze their material-related influence. To cover a wide range from standard to advanced materials relevant for load-carrying components as well as their respective variations, polylactic acid (PLA), 30% wood-fiber-reinforced PLA, acrylonitrile butadiene styrene (ABS), polycarbonate (PC), a blend of ABS and PC, Nylon, and 30% glass-fiber-reinforced Nylon were selected. The process-related influencing variables were studied using the following process parameters: layer thickness, nozzle diameter, build orientation, nozzle temperature, infill density and pattern, and raster angle. The first test series revealed that the addition of wood fibers significantly worsened the mechanical behavior of PLA due to the lack of fiber bonding to the matrix and significant pore formation. The polymer blend of ABS and PC only showed improvements in stiffness. Significant strength and stiffness improvements were found by embedding glass fibers in Nylon, despite partially poor fiber–matrix bonding. The materials with the best properties were selected for the process parameter analysis. When examining the impact of layer thickness on part strength, a clear correlation was evident. Smaller layer thicknesses resulted in higher strength, while stiffness did not appear to be affected. Conversely, larger nozzle diameters and lower nozzle temperatures only positively impacted stiffness, with little effect on strength. The part orientation did alter the fracture behavior of the test specimens. Although an on-edge orientation resulted in higher stiffness, it failed at lower stresses. Higher infill densities and infill patterns aligned with the load direction led to the best mechanical results. The raster angle had a significant impact on the behavior of the printed bodies. An alternating raster angle resulted in lower strengths and stiffness compared to a unidirectional raster angle. However, it also caused significant stretching due to the rotation of the beads.
Market availability of aluminum alloys for laser powder bed fusion (L-PBF) is still highly limited in comparison to conventional manufacturing processes. The demand for high-strength but inexpensive alloys specifically designed for L-PBF is high. This demand has led to research on a variety of adapted conventional alloys which are still limited to utilize the full potential of L-PBF. Scalmalloy® (Al–Mg–Sc–Zr) satisfies the demand for high-strength L-PBF-alloys but needs a high energy input and has troubles with evaporation of Mg. Scancromal® (Al–Cr–Sc–Zr) is a novel alloying system for L-PBF and was first introduced in 2019 with the possibility of higher build rates and comparable strengths to Scalmalloy®. In this paper, a more economic low Sc-containing version of Scancromal® is presented. A parameter study was performed for 100 μm layer thickness reaching high build rates of about 47 cm3 h−1 . Hardness tests for different parameters were carried out and showed a stable process window with a hardness comparable to AlSi10Mg. Additionally, two-dimensional multilayer process simulations showed a potential for increasing the layer thickness to 150 μm and therefore a significant increase in build rate of up to 70 cm3 h−1 highlighting the high productivity potential of Al–Cr alloys for L-PBF.
Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20% in traffic flow compared to the baseline – the corresponding pre-optimised real-world control.
Among the possible traffic members on a Vehicle-to-Everything network, the term Vulnerable Road User (VRU) is assigned e.g. to pedestrians and cyclists. The VRU Awareness Message (VAM) is used by VRUs to inform other users of their presence and ensure they are perceived in a traffic system. Since the number of VRUs in crowded areas might be very high, the over-the-air traffic might be overloaded. To reduce channel overload, VAMs offer a clustering feature in which VRUs with similar kinematics and positions can group themselves so that only one device transmits messages. The VRU Basic Service specification describes the cluster as a bounding box that must cover all its members using a geometric shape so that other vehicles in the vicinity can avoid colliding with the contained VRUs. This paper contributes to the standardization effort by introducing a data structure, the Cluster Map, for the clustering in the VRU Basic Service. Furthermore, this work is the first to suggest strategies for forming bounding box shapes. Simulation results show that each of the geometry types is useful in different situations, thus further research on the topic is advised.
We consider a small helicopter structure that is maneuvered through the control of moving masses. It is referred to as a swash mass helicopter (SMH). This paper addresses the trajectory tracking control problem for the SMH, with a specific focus on the decoupling change of coordinates of both rotational and translational dynamics. We propose a control scheme in which position tracking is the primary objective, while the attitude tracking task is considered as a secondary objective. The intermediate control signals related to the attitude dynamics exploit the structural properties of the SMH and are enhanced with terms that grant a more accurate tracking of the target trajectory. The closed-loop system stability under the trajectory tracking objective is obtained following the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) approach. In addition, the presence of external disturbances can diminish the trajectory tracking performance. For this reason, a nonlinear outer loop controller is added to the IDA-PBC to compensate the disturbances. Finally, the results of several simulations are reported to evaluate the performance of the control strategy.
A novel structure learning algorithm for Bayesian Networks based on a Physarum Learner is presented. The length of the connections within an initially fully connected Physarum-Maze is taken as the inverse Pearson correlation coefficient between the connected nodes. The Physarum Learner then estimates the shortest indirect paths between each pair of nodes. In each iteration, a score of the surviving edges is incremented. Finally, the highest scored connections are combined to form a Bayesian Network. The novel Physarum Learner method is evaluated with different configurations and compared to the LAGD Hill Climber showing comparable performance with respect to quality of training results and increased time efficiency for large data sets.
NoiSLU: a Noisy speech corpus for Spoken Language Understanding in the Public Transport Domain
(2024)
Abstract
In the early stages, entrepreneurs encounter significant challenges and extreme uncertainty when founding a young innovative venture. In response to this, a spectrum of wide support mechanisms are available. Typically facilitated not in isolation, but rather through collaborative efforts involving multiple institutions within a startup ecosystem. An exemplary theoretical framework delineating auspicious collaborative efforts among multiple institutions to foster innovation, is the Triple Helix model. The primary objective of this thesis is therefore to analyze the positioning of European top incubator landscape within the Triple Helix Model and the support offers for startups resulting from the cooperation between incubator and a subsystem. Ultimately, implications for the local German incubator brigk should be described. In doing so, the conducted study employed a qualitative data analysis, leveraging 100 datasets within the data analysis software ATLAS.ti. By crawling the "About us" pages on incubators' websites, the methodology involved first-level paragraph coding based on the Triple Helix Model's subsystems. Subsequently, second-level codes were assigned following a consistent schema that highlighted the kind of collaboration. The final step involved the clustering of similar second-level codes, providing a nuanced understanding of the dimensional interaction between incubators and other institutions. The results show that cooperation’s between the analyzed incubators and the three subsystems of the Triple Helix Model do exist. Besides the pronounced frequency of cooperation with the Economic Subsystem, the role reversal revealed by the analysis is a particularly interesting finding in this thesis. It shows that incubators themselves often take on the role of a subsystem to drive innovation. Looking at the results of this work in a broader context, it could represent a benchmark for individual institutions within Europe, improving their cooperation’s with the players of the three subsystems. And in the long run, enhancing their offering and support to young innovative companies.
Feedback kann eine sehr wichtige Rolle für den Lernerfolg spielen. Beim Peer Review Verfahren beurteilen die Studierenden selbst gegenseitig ihre Arbeitsergebnisse, geben also einander Feedback. In diesem Beitrag werden das Peer Review Verfahren mit Gruppenarbeit verknüpft und dabei zwei mögliche Strukturierungsvarianten differenziert. Für die Umsetzung dieser beiden Varianten wird die Moodle-Aktivität „Gegenseitige Beurteilung“ herangezogen, die in der hier verwendeten Moodle-Version aber nicht vollumfänglich auf das Arbeiten mit Gruppen ausgelegt ist. Daher wird in diesem Beitrag aufgezeigt, welche Gestaltungen und manuellen Schritte seitens der Lehrperson zweckmäßig sind, um die Aktivität „Gegenseitige Beurteilung“ auch in Kombination mit Gruppenarbeit in geeigneter Weise einsetzen zu können.
Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.
In dieser Bachelorarbeit steht das Thema Merchandising im deutschen Profifußball im Fokus. Die wissenschaftliche Untersuchung wurde auf die 1. und 2. Fußball-Bundesliga beschränkt. Mit dem großen Ziel, die Forschungsfrage „auf welchen aktuellen Trend die Vereine das größte Augenmerk legen müssen“ zu beantworten. Um einen weitreichenden Blick über diesen Wirtschaftszweig zu erhalten wurden ebenfalls auch weitere Kriterien, wie Ziele, Chancen und Risiken begutachtet. Als Grundlage diente die Literaturrecherche mittels Fachbücher, Zeitungsartikel sowie Studien. Bei den Zielen konnte ausfindig gemacht werden, dass es sich beim Merchandising um eine sehr wichtige Einnahmequelle handelt. Interessanterweise ließ sich feststellen, dass der Merchandisingkonsum vom Fanverhalten beeinflusst wird. Je stärker die Bindung von Vereinsanhänger zum Fußballclub ist, desto wichtiger ist dieser für den Merchandisingumsatz. Deswegen fokussieren sich die Fußballvereine bereits auf die jüngsten Mitglieder. In sogenannten Kids-Clubs. In der Hoffnung die Verbindung zum Verein zu kräftigen und lebenslang aufrecht zu erhalten. Des Weiteren wird mittels Merchandisingprodukten ein wichtiges Marketingziel verfolgt. Die mit dem Vereinswappen versehenen Artikel repräsentieren den Verein als Marke. Eine durchgeführte Studie des Unternehmens FanQ bekräftigt die Wichtigkeit der Fanartikel für die Anhänger. Auf welche in dieser Bachelorarbeit Bezug genommen wird. Zusätzlich wurden die Chancen mit dem Ergebnis, dass der ausländische Markt noch Potenzial aufweist und Risiken, dass ein Zusammenhang zwischen Merchandising-Artikel und Gewalttaten besteht, untersucht. Hervorzuheben ist die wissenschaftliche Auseinandersetzung mit dem Thema der Produkttrends. Hier sind mehrere Entwicklungen erkennbar. Bewahrheitet hat sich unter anderem der Erfolg mit den sogenannten „Sondertrikots“. Diesem Erfolgsfaktor bedient sich fast jeder Profifußballverein der ersten Bundesliga. Außerdem ist das Thema der „Nachhaltigkeit“ der stärkste Trend. Eine Auseinandersetzung mit dem ökologischen Fußabdruck der Artikelherstellung wird nicht nur seitens der Vereine durchgeführt, sondern auch vom Bundesministerium sowie von den Produktkäufern gefordert. Auf dieser Grundlage ist es empfehlenswert das sich die Fußballvereine in Zukunft vermehrt auf dieses Thema konzentrieren sollten.
It is well known that lithium-ion batteries pose a certain safety risk. The thermal runaway of a cell and the subsequent thermal propagation through the battery are considered particularly dangerous. Effective solutions for their mitigation are therefore of great interest. Previous studies have shown the significant influence
of a cell’s state of charge (SOC) on its behavior during thermal runaway. This relation may be exploitable in a battery pack to improve its safety. This study aims to assess the leverage of active SOC reduction in the imminent threat of thermal runaway. Implementing such a technique could become feasible with the emergence of reconfigurable battery systems. Four experiments were conducted, each with a module of three fresh 63 Ah high energy pouch cells in a spring-loaded bracing. The experiments studied different stationary SOC configurations, uniform (100% and 60%) and non-uniform (100%–60%–100% and 100%–20%–100%). The results indicate that thermal propagation is substantially delay (87 s) by discharging a cell in its path. The SOC reduction primarily decreases the maximum temperature of the respective cell. Further effects are a calmer thermal runaway and prolonged propagation time within the cell as well as to the next cell. In comparison, the SOC reduction has little impact on the cell’s own triggering time, as the triggering time is mainly determined by the thermal energy transferred from the preceding cell and hence by its SOC. Furthermore, the analysis of the experimental data (temperature, voltage, pressure, video) gives insights into the propagation of thermal runaway through the individual layers of a cell. With reference to the position of a cell relative to the origin of the thermal propagation, a decrease of its mass loss and an increase of its internal propagation time is observed. This effect is attributed to the decreasing module pressure due to progressive loss of material. The assessment shows that active SOC reduction techniques have great leverage for mitigating or even stopping thermal propagation in a battery pack.
In 2020 the new COVID-19-virus brought an external shock to the stock markets. In this situation effective leadership was essential to bring companies through the crisis.
Political observation showed that countries led by women managed the pandemic situation more effectively than those led by men. Therefore, the research objective of this article is to examine whether female leaders were also more successful to guide companies through the pandemic. Success in this study is defined as financial performance based on stock prices.
To find out whether female managers showed a better financial performance during this crisis, this study examined all companies listed on the German Stock Exchange, which have at least 50 % women in the board. To compare this female managed companies to firms managed mainly by male managers, a market value weighted index for the female managed companies was conducted and compared to the German Stock Index DAX by looking at several financial key figures.
The analysis of these key figures shows that female managed companies outperformed male managed companies in terms of return as well as risk in the year of the COVID-pandemic but were not able to keep their overperformance in the subsequent years. This may be an indicator that women are superior in leading a company through a crisis but suffer in terms of performance when times have calmed down.
Automatizované řízení
(2022)
Sustainable UX zielt darauf ab, ökologische und soziale Überlegungen in das Design digitaler Produkte zu integrieren. Die Implementierung von Sustainable UX stellt jedoch Designer, Entwickler sowie Unternehmen vor große Herausforderungen, da etablierte UX-Methoden oft nur bedingt kompatible Ansätze für Nachhaltigkeit bieten. Diese Arbeit beschäftigt sich mit der Frage, wie Sustainable UX in traditionelle UX-Designprinzipien integriert werden kann und welche Herausforderungen dabei entstehen. Ein Hauptproblem besteht im deutlichen Mangel an spezialisierten Werkzeugen und klaren Methodiken, was die effektive Implementierung von Sustainable UX erschwert.
Zur Unterstützung dieser Untersuchung wurde eine qualitative Nutzerstudie durchgeführt, die die Wirkung der Sensibilisierung für ökologische Themen auf die Akzeptanz nachhaltiger Optionen bei den Nutzern erfasst. Die Studie ergänzt die Diskussion um praktische Anwendungen und zeigt, wie durch gezieltes Design und transparente Kommunikation die Akzeptanz und Motivation für umweltfreundlichere Entscheidungen erhöht werden kann.
Die Untersuchungen zeigen, dass die Integration von nachhaltigen Praktiken in bestehende UX-Methoden auf umfangreiche Herausforderungen stoßen. Diese umfassen nicht nur die technische und praktische Umsetzung, sondern auch ethische Überlegungen und die Notwendigkeit, zwischen Nutzerbedürfnissen und ökologischer, sowie sozialer Verantwortung abzuwägen. Die Ergebnisse verdeutlichen die Notwendigkeit, einen ausgewogenen Ansatz zu finden, der die menschlichen Bedürfnisse berücksichtigt und gleichzeitig die ökologischen und sozialen Auswirkungen der Nutzererfahrung miteinbezieht.