Refine
Institute
- Fakultät Informatik (597)
- Fakultät Elektro- und Informationstechnik (463)
- THI Business School (412)
Version
- published (858)
With neutron diffraction, the local stress and texture of metallic components can be analyzed non-destructively. For both, highly accurate positioning of the sample is essential, requiring the measurement at the same sample location from different directions. Current sample-positioning systems in neutron diffraction instruments combine XYZ tables and Eulerian cradles to enable the accurate six-degree-of-freedom (6DoF) handling of samples. However, these systems are not flexible enough. The choice of the rotation center and their range of motion are limited. Industrial six-axis robots have the necessary flexibility, but they lack the required absolute accuracy. This paper proposes a visual servoing system consisting of an industrial six-axis robot enhanced with a high-precision multi-camera tracking system. Its goal is to achieve an absolute positioning accuracy of better than 50μm. A digital twin integrates various data sources from the instrument and the sample in order to enable a fully automatic measurement procedure. This system is also highly relevant for other kinds of processes that require the accurate and flexible handling of objects and tools, e.g., robotic surgery or industrial printing on 3D surfaces.
In this thesis I investigate how start-ups from the Web 3.0 and blockchain field engage with the future. To gain a better understanding of the distinct aspects of interacting with the future, a literature research was first conducted, focused on future-making and strategic foresight. With the knowledge gained during this research, I then formulate hypotheses that are tested by conducting interviews with founders of blockchain and Web 3.0 start-ups. Resulting, is the discovery that even if their approach is unsystematic, their practices still coincide with these described in future-making practices.
Integrating renewable energies into district heating systems has a large potential to reduce CO2-emissions in the heating sector. As district heating systems offer the possibility of incorporating renewable energies into the heat supply, new systems have to be built and the existing networks must be maintained. This study investigates ways to optimize existing district heating systems in order to ensure economic sustainability in the long-term. Previous case studies have elaborated on a variety of optimization measures. However, to date, these measures have neither been collected nor consistently assessed for a wider application range. Therefore, in the study presented here a system for assessing the ecological and economic benefits of optimization measures was developed and applied. The assessment method utilized showed that optimization of district heating consumer substations and adding of a central buffer storage tank has a high optimization potential in comparison to intermittent operation strategy, which has a significantly lower optimization potential. From this information and the transferability data, a district heating operator can determine which optimization measure should be prioritized, which is shown at the end of the paper on an example case.
Lithium–ion batteries are well established as traction batteries for electric vehicles. This has led to a growing market for second-life batteries that can be used in applications like home energy storage systems. Moreover, the recyclability and safe handling of aged or damaged cells and packs has become more important. While there are several indicators, like state of health (SOH), state of power (SOP), or state of safety (SOS), which describe the state of a battery before its defined end of life (EOL), there is no consistent classification methodology by which to describe the usability of a cell or pack after its EOL is reached. The proposed state of usability (SOU) provides a new indicator that accounts for the usability for second life, recyclability, and possible required safety handling of a lithium–ion battery after its first intended life cycle. This work presents a decision tree method, which in turn leads to five discrete usability levels enabling a fast and rough determination of the SOU for practical use. Further, a calculation methodology for reasonable continuous regions of the SOU is proposed. Both methods are based on a literature-based rating of all of the relevant defect and aging mechanisms displayed in a risk matrix. Finally, some experimental methods that can be used for SOU determination are proposed. The developed methodology and the hands-on approach using a decision tree are well-suited for real world application in recycling companies and battery test laboratories.
Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.
Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction
(2024)
Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system’s performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system’s warning signal, the accident can be avoided or not by the driver when reacting to this signal.
This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.
Sowohl der Anteil und Umfang von Software, als auch die Komplexität und die Entwicklungsgeschwindigkeit bei neuen Produkten nimmt stetig zu. Um diesen Anforderungen gerecht zu werden, ist es notwendig, die Softwareentwicklung fortlaufend zu optimieren und zu verbessern. Durch die Vergrößerung des Softwareanteils eines Produktes nehmen auch die Wechselwirkungen zwischen den digitalen, als auch den übergeordneten, technischen Systemen, in welche die digitalen integriert sind, stetig zu. Hieraus ist der Begriff der Embedded Software entstanden: Ein digitales System, Welches in ein technisches System eingebettet ist und als universeller Systemintegrator zwischen Steuerung, Überwachung und/oder Regelung dient.
Zur Entwicklung von Embedded Software wird eine Toolkette verwendet. Diese besteht aus einer Vielzahl einzelner Programme, welche miteinander verzahnt sind. Eine solche Toolkette ist auf einzelne Entwicklungsprogramme und Softwarehersteller spezifiziert, weswegen entsprechende Entwicklungswerkzeuge Unikaten gleichen. Die Firma ESPRiT Engineering entwickelt derzeit selbst eine eigene Toolkette, um als Dienstleister herstellerübergreifend und -unabhängig Embedded Software für Kunden zu entwickeln. Um sicherheitskritische Software mit einer Toolkette zu entwickeln, muss diese entsprechend zertifiziert werden, wofür eine Klassifizierung und Qualifizierung notwendig ist.
Das Besondere an der folgenden Arbeit ist, dass sich die Toolkette derzeit noch im frühen Entwicklungsstadium befindet und bereits hier entwicklungsbegleitend gemäß ISO 26262 auf potentielle Toolfehler und deren Auswirkungen auf das endgültige Softwareprodukt untersucht wird. Deshalb wird in dieser Arbeit auch nach geeigneten Möglichkeiten gesucht, potenzielle Fehler sicher aufzudecken. Daran anknüpfend werden geeignete, prozessuale oder toolbasierte Testmaßnahmen erstellt, wodurch die Klassifizierung der Toolkette ermöglicht wird. Für Fehler, welche durch entsprechende Maßnahmen nicht sicher aufgedeckt werden können, werden die Möglichkeiten zur Qualifizierung untersucht und vorgestellt. Begleitend ist eine zugehörige Prozesslandschaft definiert worden.
Durch diese Neuerung im Bereich der Toolkettenentwicklung erhofft sich die Firma ESPRiT Engineering, die Toolkette schneller und effizienter fertigzustellen, sowie parallel ein geeigneteres Endprodukt zu erhalten.
Bislang waren SAP-Anwendungen eher für ihre zweckmäßigen, aber unübersichtlichen und aus heutiger Sicht wenig intuitiven Benutzeroberflächen bekannt. Die Einführung von SAP Fiori – einer neuen Benutzeroberfläche und eines neuen Designkonzepts – soll Abhilfe schaffen und die User Experience steigern. Dabei steht erstmals der Nutzer im Mittelpunkt und Prozesse, Ansichten und das Verhalten der Anwendungen werden nach den Anforderungen des Anwenders spezifiziert.
SAP Fiori bietet die Möglichkeit die Zufriedenheit des Endanwenders zu steigern, indem die Handhabung des Systems benutzerfreundlicher gestaltet werden kann. Diese Idee hat sich über die letzten Jahre immer weiter verbreitet und wird von SAP selbst als die Zukunft angesehen. Dadurch hat es sich auch seinen Weg in die Unternehmensführung bzw. IT-Abteilungen von Firmen gebahnt. Auch die verantwortlichen IT-Entscheider bei Schwan Cosmetics interessieren sich für die Vorteile von SAP Fiori. Die in dieser Arbeit vorgenommene Evaluation soll es er-möglichen, eine fundierte Entscheidung treffen zu können, ob die Umstellung auf Fiori weiterverfolgt und umgesetzt wird.
Das Ziel dieser Arbeit ist es, dem Leser einen Überblick über SAP Fiori zu bieten und die Unterschiede gegenüber der gewohnten Nutzeroberfläche hervorzuheben. Außerdem soll anhand analysiert werden, wie SAP Fiori bei den Endanwendern in den gewählten Geschäftsbereichen gesehen wird und ob eine unternehmensweite Einführung eine Steigerung der User Zufriedenheit erwarten lässt. Weiterhin kann für die betrachteten Test-Abteilungen eine klare Aussage bzgl. einer etwaigen Verbesserung der Nutzerfahrung getroffen werden.
Im Rahmen dieser Bachelorarbeit wird eine Vorher-Nachher-Betrachtung des prototypischen Tests in den gewählten Fachabteilungen durch zwei inhaltlich auf-einander aufbauende Fragebögen durchgeführt.
Vorab werden theoretische Grundlagen, wie User Experience, erörtert und eine Einführung in SAP Enterprise-Ressource-Planning (Kurz: ERP) und dessen Hintergründe gegeben. In diesem Zuge wird auch SAP Fiori eingegangen und dessen Vorteile und Unterschiede gegenüber dem SAP GUI erläutert. Dafür wird eine umfassende Literaturrecherche durchgeführt, um den aktuellen Stand der Forschung zu diesem Thema zu ermitteln.
Anschließend werden zwei Fragebögen erstellt: einer für die Vorher-Betrachtung und einer für die Nachher-Betrachtung. Die Fragen in beiden Fragebögen sind korrelierend, um eine aussagekräftige Analyse zu ermöglichen. Die Vorher-Befragung wird durchgeführt, bevor das Projekt startet.
Die Daten werden von den Benutzern gesammelt, die das SAP-System im Ist-Stand verwenden. Während und vor dem Projekt wird der Arbeitsablauf mitsamt den notwendigen Schritten zuerst in der SAP GUI dokumentiert. Nach der Ermittlung der notwendigen Fiori Applikationen, wird das System konfiguriert, um eine tägliche Anwendung im aktiven Betrieb zu ermöglichen. Im Anschluss daran führen die Projektbeteiligten ihre Arbeit vollständig in SAP Fiori durch, um sich eine Einschätzung und Meinung bilden zu können.
Parallel dazu wird ein Experte im Bereich User Experience befragt, um wichtige Erkenntnisse und eine professionelle Einschätzung zur User Experience im Allgemeinen und die von SAP Fiori zu erlangen.
Die Nachher-Befragung wird durchgeführt, nachdem das Projekt abgeschlossen wurde, um nach der Eingewöhnungs- und Nutzungszeit die tatsächliche Einschätzung der Tester zu erfragen. Anschließend werden die Ergebnisse der beiden Fragebögen analysiert, um festzustellen, ob sich die User Experience durch die Einführung von SAP Fiori verbessert hat.
Aus diesen Daten und der Analyse derer werden im Anschluss Erkenntnisse abgeleitet und Zusammenhänge zwischen den Antworten, dem Verhalten der Tester und den Hintergrundinformationen zu den Testpersonen interpretiert. Die Ergänzung dieses Wissens mit den Aussagen des Experten, ermöglicht eine Aussage zu den Forschungsfragen und außerdem die Erstellung einer Handlungsempfehlung im Hinblick auf das weitere Vorgehen mit Fiori.
Real-time rendering of real world terrain states a popular field of research of the recent decades. Real world terrain is represented by highly detailed data sets. Therefore, it is crucial to use methods and algorithms that create simplified terrain meshes out of this data. As a result, steadily new approaches and techniques are developed, to render terrain efficiently with a high degree of detail, while trying to use the most of the current hardware’s performance.
The goal of this thesis is to develop an off-screen terrain renderer using Vulkan as graphics API and real world data sets, as height maps and satellite images. Additionally, the developed terrain render is supposed to be executed on selected embedded devices, while still delivering high framerates.
In order to achieve this goal, necessary basic terminology and concepts of terrain rendering are dealt with. An important part of these concepts are performance optimizations, as level of detail and culling methods.
Furthermore, Vulkan as graphics API will be introduced and compared with its predecessor OpenGL. For a technical understanding of Vulkan it is crucial to look at its render pipeline and basic rendering concepts.
After several rendering methods are analyzed, based on them the design of this thesis’s renderer is examined, while the most important parts of its implementation are looked at closer by analyzing their code snippets.
The main part of this thesis is completed, by analyzing benchmarks of different scenarios to see how the developed terrain render behaves in different situations. These benchmarks include the frames per second, and statistics of the device’s hardware components, as CPU load, GPU load, memory usage and memory throughput.
The thesis ends, with a summary and general conclusion of the work. Possible future work includes optimizing the usage of embedded devices’ unified memory, testing of texture compression methods for an optimized memory usage or a comparison of an OpenGL version of the developed renderer by comparing benchmark results of the same scenarios.
The dairy farms form a vital part of German agriculture and consume significant energy for their operation. Germany is working towards energy transition to transform the existing fossil fuel-nuclear system to a sustainable energy system that relies on renewable sources due to various societal and political demands. As the energy demand is increasing due to the rise in population, there is also a growing push in Germany to adopt energy efficient technologies and measures to reduce energy waste. There is a scope for improving the energy efficiency in dairy farms. Using energy optimization techniques can help reduce the load on energy suppliers as well as increase cost savings for farms.
German farmers are adopting newer digital technologies to achieve optimization of energy in their farms. One of the key tools in realizing energy optimization is the Energy Management System (EMS). The use of EMS in the case of dairy farms has been reviewed and presented in this work. The objective of the study is to investigate the potential of energy optimization in dairy farms. Efficiency measures like replacing inefficient farm equipment are necessary to achieve energy efficiency. These measures are discussed in detail and their potential to save energy in dairy farms is presented. A comparison of all these measures is studied and how much energy savings can be obtained is reviewed.
The use case scenario of EMS in different applications is presented as an indication of possibilities for energy savings using EMS. Furthermore, how the AI technology in combination with EMS can be used in farms and its benefits in dairy farming is reviewed. The types of farms on which the energy optimization measures can be implemented in an ideal case scenario are reviewed. The result of the study shows that there is vast potential for energy optimization in German dairy farms. Thus, the presented work can be further studied to identify further optimization possibilities with the advancement of AI and newer technologies.
In der vorliegenden Abschlussarbeit wird ein Versuchsträger aufgebaut und mit Hilfe der Dimensionalanalysemethode nach dem Buckingham Pi Theorem skaliert. Ziel ist es dabei ein Fahrzeug aufzubauen, mit welchem zum einen autonome und zum anderen teleoperierte Fahrfunktionen, für Flottenversuche im Bereich des autonomen Fahrens, umgesetzt werden können. Dabei wird zunächst die Theorie der Skalierung mit dem Buckingham Pi Theorem anhand eines Praxisbeispiels erklärt. Anschließend werden die für die Skalierung verwendeten Fahrzeugmodelle erläutert und anhand derer die Skalierungsvorschriften für das Versuchsfahrzeug abgeleitet.
Mit der erläuterten Theorie wird dann ein Prototyp des Versuchsträgers entwickelt und aufgebaut. Dabei wird auf Basis eines kommerziell erhältlichen Grundfahrzeug ein Versuchsträger mit zusätzlichen Sensoren und Kontrollmöglichkeiten entwickelt. Es wird ein Überblick über dieses Grundfahrzeug und die verwendeten Zusatzkomponenten gegeben. Anschließend werden die vorgenommenen Modifikationen an dem Grundfahrzeug, wie auch die praktische Anwendung des Buckingham Pi Theorems aufgezeigt. Mit den daraus gewonnen Vorgaben wird dann ein Prototyp des Versuchsträgers aufgebaut.
Der aufgebaute Prototyp wird schließlich anhand von Messungen am realen Versuchsträger als auch durch das Resultat von Simulationen validiert. Damit wird sichergestellt, dass das Versuchsfahrzeug auch den Erwartungen entsprechend agiert, wenn es in späteren Projekten eingesetzt wird.
This thesis examines the ethical and societal implications of generative AI models, focusing on their associated risks, but also exploring the potential benefits. It synthesizes existing research to outline the primary concerns and opportunities that generative AI presents, with an emphasis on the need for its responsible development and deployment. The analysis includes an exploration of the increasing difficulty in distinguishing between AI-generated and human-created content, highlighting advancements in AI that challenge current notions of authenticity and trust.
The work further discusses a variety of broader societal changes and risks that may emerge with generative AI, advocating for the necessity of regulatory interventions to mitigate potential negative outcomes. It assesses and critically investigates the current regulatory landscape and potential regulation strategies, noting the lack of comprehensive and mandatory frameworks to address the implications of generative AI adequately.
The critical role of collaborative efforts among stakeholders in navigating the ethical landscape of generative AI is highlighted. It concludes that addressing the examined challenges requires a multidisciplinary approach, integrating insights from an array of fields, including technology, ethics, and policy to ensure that generative AI advances in a way that aligns with societal values and norms.
This master's thesis examines how digital procurement techniques have revolutionized TATA Motors' supply chain management. Using a case study methodology, the study investigates how digital technologies are implemented in procurement procedures and how this affects supply chain performance in general as well as efficiency and cost-effectiveness. The results enhance comprehension of how digitalization might transform conventional procurement techniques by providing an analysis of the obstacles and possibilities faced by TATA Motors in maneuvering through this dynamic terrain.
Steigende Kundenanforderungen und im Verlauf der Jahre immer komplexer werdende Fahrzeugsysteme, haben in der Automobilindustrie zu höheren Fehlerauftretenswahrscheinlichkeiten geführt. Im Test- und Absicherungsprozess liegt die Herausforderung eine aus Kundensicht hervorragende Qualität bei gleichzeitiger Reduktion des Aufwandes und der Kosten zu liefern. Um diesen Umständen gerecht zu werden, halten zunehmend simulationsgestützte Testmethoden Einzug im Test- und Absicherungsprozess.
Die vorliegende Masterarbeit befasst sich mit der Entwicklung einer Simulationsmethodik, die für den automatisierten Schnittstellentest im Bereich Audio- und Videoübertragung im Fahrzeug eingesetzt werden kann. Die Erarbeitung und Entwicklung der Methodik erfolgt an einer Funktion, die an der Schnittstelle zwischen dem Park- und Infotainmentsteuergerät angesiedelt ist und als Kommunikationsmedium Automotive Ethernet verwendet. Dabei reicht sie von der Generierung des Videostreams der nahen Fahrzeugumgebung durch Umfeldkameras bis zur Wiedergabe an der Anzeigeeinheit im Fahrzeuginnenraum. Definiertes Ziel bei der Entwicklung der Simulationsmethodik ist es, dass Entwickler die Befähigung erhalten Freigabeentscheidungen des auf dem Infotainmentsteuergerät verorteten Funktionsteils zu treffen. Herzstück der Arbeit ist die Modellbildung des in ein Audio Video Transport Protocol (AVTP) und im H.264 Kompressionsformat
eingebetteten Videostream vorzunehmen. Zweiter Bestandteil ist die Simulation der über SOME/IP-Paketen (Pakete, welche die Scalable service-Oriented MiddlewarE over Internet Protocol als Transport nutzen) übertragenen Steuersequenzen zur Anforderung des Videostreams bzw. Veränderung von Anzeigeoptionen im Erscheinungsbild. Nach der Integration beider Bausteine in ein Gesamtsimulationsmodell wird dieses in einem manuell durchgeführten Testfall an einer Hardware-in-the-Loop-Prüfumgebung verifiziert. Die umgesetzte Methodik bietet als Hauptbestandteil erstmals, innerhalb des gesamten Entwicklungsprozesses, die Möglichkeit Teile der Schnittstellenfunktion auf der Zielhardware des Infotainmentsteuergeräts zu verifizieren und abzusichern.
Diese Arbeit konzentriert sich auf die Entwicklung eines Prototypensystems bestehend aus einem Wasserreservoir, einer Pumpe und einer Turbine, wobei besonderes Augenmerk auf die Auslegung und Konstruktion der Turbine gelegt wird. Die Auswahl der Komponenten basiert auf früheren Arbeiten zu diesem System. Durch Versuchsdurchführungen wurde der Betriebspunkt der Anlage genauer bestimmt und eine entsprechend ausgelegt Pelton-Turbine konstruiert. Die Festigkeitsanalyse zeigt, dass die Turbinenschaufeln den auftretenden Belastungen standhalten. Der Prototyp des Systems erreicht eine Drehzahl von 431 1/min.
In the current era, characterized by the rapid advancement of Artificial Intelligence, which is being developed at a rapid pace, delivering promising and exciting progress in different sectors such as healthcare, transportation, education, and entertainment, it is unclear how this evolution will impact society, businesses, and individuals in the future. While AI is being one of the most hyped technologies currently, also cautionary, and critical voices get louder that see significant risks to society if AI lacks appropriate safeguards or is misused. Yet, many companies want to be part of the AI revolution and want to benefit from an implementation of the technology. Therefore, the exploration of the impact of AI tools on daily work in businesses is necessary to determine whether the implementation of such a rapidly developing technology is beneficial.
How AI could impact companies, especially in consulting, is not a deeply examined topic by now and lacks research and knowledge. Therefore, the impact that AI could have on daily work processes in a consulting company needs to be further studied. In this thesis, different use cases within the daily workflows of consultants at 3DSE Management Consultants GmbH are selected, different available AI-supported business tools evaluated, and the impact of AI determined.
In this thesis, it is shown that the tasks that one must do stay the same but can now be supported by different AI tools. Especially in text processing and research tasks can be done faster, while for interviews and meetings an analysis can be done automatically. Additionally, image generation enables the creation of tailored images to a slide´s content and improves presentation quality. Three different phases are identified where AI can be used as support and it is shown that the impact of AI can be improved by building functional experience with the use of AI tools and domain expertise to evaluate the correctness of the results and reach higher complexity and levels of detail. Two models are introduced that visualize the need for experience and expertise.
The results prove that the implementation of AI can improve productivity of daily work in a consulting company. Even more so if certain circumstances are true. Improving experience of working with AI and expertise in the domain the tasks are located are success factors that need to be looked at by 3DSE. If done right, the implementation of AI can be enhanced to not solely personal work, but also consulting processes and ultimately integrated in the consulting service.
Nachhaltigkeitsberichterstattung (ESRS) und ihre Prüfung – eine Darstellung im europäischen Kontext
(2024)
A Novel Approach for Researching Crossing Behavior and Risk Acceptance: The Pedestrian Simulator
(2016)
Cycle management of manufacturing resources: identification and prioritization of investment needs
(2017)
Cybersecurity as a Service
(2023)
Towards to Battery Digital Passport: Reviewing Regulations and Standards for Second-Life Batteries
(2024)
Greenhouse gas emissions from transportation harm the environment. In response to these environmental concerns, numerous countries encourage the adoption of electric vehicles (EVs) as a more environmentally friendly option than traditional gasoline-powered vehicles. Advances in battery technology have made batteries an alternative solution for energy storage in stationary applications and for electric mobility. Reduced lithium-ion batteries (LIBs) production costs due to economies of scale, electrode material and cell design developments, and manufacturing process improvements have driven this success. This trend is expected to increase the number of LIBs on the market that may be discarded in the environment at the end of their useful life if more sustainable alternatives are not technologically mature. This coming environmental concern can be mitigated by collecting wasted EV batteries, reconfiguring them, and reusing them for applications with less stringent weight, performance, and size requirements. This method would extend battery life and reduce environmental effects. The present work investigates the main regulatory structures of the second-life battery industry that require rules, technical standards, and laws. To achieve this objective, a systematic review was carried out following a strict protocol that includes identifying relevant studies, extracting data and information, evaluating, and summarizing information. This paper explains the primary rules and technical standards governing the second-life battery business. The findings highlight the need for universities, research institutions, and government agencies to evaluate the second-life battery industry objectively. This would enable the creation of new technological regulations and laws for this burgeoning industry.
Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion-recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. While 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
Die Lunge des Menschen ist als eines der Hauptorgane für die Sauerstoffversorgung, die Sauerstoffaufnahme und die anschließende Weitergabe an das Blut im menschlichen Körper zuständig. Eine Erkrankung dieses Organs kann erhebliche gesundheitliche Folgen haben. In der Pneumologie ist man daher bestrebt, solche Krankheiten frühzeitig zu erkennen und effektiv zu behandeln. Das Ziel der vorliegenden Arbeit ist es, anhand von verschiedenen Werten des Blutes, mit Fokus auf Immunoglobulin G und Daten über bisherige Erkrankungen die Verläufe von Lungenkrankheiten vorhersagen zu können und die Behandlung dementsprechend anzupassen.
With the rise of autonomous driving technology, concerns over the safety of AI-based systems have increased, particularly regarding their effect on human life and infrastructure. The complexity and unpredictable nature of AI-enabled software functions have disrupted traditional safety evaluation methods. In such a scenario, the quality control methods used at the manufacturing environments of sensors used for deploying AI-based perception functions need to be coupled with the downstream functions. This study focuses on investigating the impact of a specific production-related defect called blemish, which is caused by debris on image sensors of camera modules. The research aims to answer two fundamental questions: 1) How can blemish artifacts be mathematically modeled, and what is the process for developing a suitable dataset that incorporates this defect? 2) How can the influence of blemish artifacts on performance of object detection models be traced back to define quality requirements of the camera modules? This work takes a novel perspective by shifting the focus from improving model resilience to improving hardware quality camera modules by studying the effects of manufacturing defects on downstream functions. This research seeks to establish a framework to derive quality targets for front camera module manufacturing which can be extended to other sensors for similar applications.
Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4× on federated data sets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, and FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.
Automated Valet Parking Systems (AVPS) relieve the driver of the entire parking process. Many of the systems known today rely on a combination of automotive sensors with sensors of the infrastructure. For this purpose, parking facilities are equipped with comprehensive sensor technology to support the vehicles in environment sensing and route planning. This approach is comparatively expensive which is why many parking operators don’t provide that technology to their customers. This paper proposes a lean AVPS system architecture that requires minimal effort to adapt the infrastructure. At the same time, state-of-the-art vehicle technology is used to make AVPS more profitable overall. At the beginning, an overview will be given describing the state of the art of AVPS. Subsequently, requirements for the AVPS will be elaborated, whereby the system can be designed and implemented in the following. Finally, the presentation of simulation results shows that one doesn’t have to extend the infrastructure with sensors to develop a safe and reliable AVPS.
If we, as user experience designers, want to create truly user-centered experiences, we need to understand not only what people want, but what they need and how they behave. Human behavior, however, also results from the choices people make every day. The same applies to the development and design of automated transportation. To achieve and maintain these goals, we need to accept that human decisions are not only made on a rational level, but rather on the basis of mental shortcuts, which may be efficient, but can also lead to cognitive biases and thus to sub-optimal results.
This master’s thesis specifically sheds light on four cognitive biases - truthiness, illusion of control, action bias and automation bias -, their existence in automated driving, as well as their relationship to each other and possible influencing factors. As part of a preliminary study (n=117), which was implemented as an online questionnaire, the existence of the cognitive biases in focus of this work is examined in more detail and initial findings of visualizations and explanations as an influencing factor are collected. In the further course, a user study (n=34) was completed to build on the knowledge gained and examine the accuracy of the mental model of the occupants of an automated ride and their individual personality traits as further influential parameters. This work should serve as a cornerstone to include cognitive biases and their influencing forces in the development of automated vehicles in the future and to use the knowledge gathered to integrate psychological aspects that affect every human being into user-centered humanmachine interactions.
The fatigue behavior of thin electrical steel sheets under cyclic loading is investigated in dependence on the edge surface. Therefore, four different edge conditions are compared, whereas the edge is either laser cut, shear cut, wire cut, or polished. Strain- and stress-controlled fatigue tests are performed to determine S-N curves in the low cycle regime as well as in the high cycle regime. Microstructural data is collected by non-contacting (optical) Profilometry, Nanoindentation, X-Ray Diffraction, and Electron Backscatter Diffraction to understand the differences in fatigue life by considering surface roughness, residual stresses, hardness, and microstructure. Shear cut specimens achieve the lowest fatigue life, while the other edge conditions reach relatively similar values in the LCF regime. Crack initiation is mainly intergranular in the case of defect-free edges. This tendency has a considerable influence on the observed fatigue behavior.
The impact of technology on employment and consequently on skills is becoming increasingly apparent, particularly in the use of artificial intelligence.
The aim of this bachelor thesis can therefore be divided into two research questions: On the one hand, the aim is to analyze what impact the use of artificial intelligence will have on the labor market. The second question is concerned with the impact artificial intelligence will have on the labor market in terms of the skills required.
In order to provide a profound answer to the research questions, a wide range of literature was used, including books, research studies and use cases. Based on the research questions, the thesis addresses the worldwide AI driven labor market, identifying both the displacement of human workers and the simultaneous opportunities for new occupations and economic growth. Moreover, a variety of necessary skills are highlighted that are essential for overcoming the dynamic challenges on the labor market posed by AI. Finally, the subsequent analysis of the financial sector and public administration serves to illustrate and clarify the relevance of these skills. Therefore, both knowledge of artificial intelligence and existing human skills are important. Depending on the occupation and task area, the focus of skills is either on the direct application of artificial intelligence or on developing an awareness and understanding of it. It also shows that a wide range of actors, including companies and educational institutions, have a key role to play in the process of adapting workers' skills.
Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence,
it is performed manually to a large extent. Even though full automation of such a process is difficult, semiautomation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75% of the
labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.
Crash-Prone Fault Combination Identification for Over-Actuated Vehicles During Evasive Maneuvers
(2024)
Throughout a vehicle’s lifecycle, systems may fail during operation, requiring effective fault management by the vehicle controller. Various system faults affect vehicle handling differently. Additionally, vehicle velocity and road friction directly impact handling and stability. Thus, it is essential to investigate relevant factors, such as actuator faults, vehicle velocity, road friction, and their combinations, before developing a fault-tolerant controller to mitigate potential critical situations. Our work thus focuses on identifying faults and fault combinations that might lead to crashes for over-actuated vehicles during evasive maneuvers and those impacting comfort parameters. We employ a state-of-the-art vehicle controller optimized for evasive lane changes for over-actuated vehicles. The driving scenario encompasses critical conditions defined in ISO 26262 with ASIL-D, including velocities up to 130 km/h and requiring steering away from obstacles. Failure Mode and Effects Analysis, Design of Experiments, and statistical tools are used to determine fault combinations most likely to lead to crashes during evasive maneuvers. Our results indicate that the vehicle controller successfully handled the maneuver in over 53% of investigated cases, reaching up to 75.1% on dry surfaces. Road friction emerges as the most critical parameter for collision avoidance and comfort. Brake faults exhibit a higher influence on vehicle handling than other actuator faults, while single motor faults do not significantly impact vehicle parameters. Regarding two-factor interactions, brake actuators dominate, followed by steering and motor. These findings provide valuable insights for developing fault-tolerant controllers for over-actuated vehicles, guiding decisions on addressing specific faults to enhance safety and comfort parameters.
Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code will be published on GitHub.
Compression of end-to-end non-autoregressive image-to-speech system for low-resourced devices
(2023)
Dogs-of-the-DAX
(2024)
Diese Arbeit überträgt die populäre, US-amerikanische „Dogs-of-the-Dow“-Anlagestrategie auf den deutschen Markt. Sie beantwortet die Frage, ob ein Aktienportfolio basierend auf DAX-Dividendenstrategien von 2013 bis 2023 eine bessere Performance im Vergleich zum DAX hätte erzielen können. Für eine umfassende Bewertung wird zusätzlich eine Performanceanalyse für krisenhafte Zeiträume anhand der COVID-19-Pandemie und des Ukraine-Kriegs durchgeführt.
Repräsentativ für die DAX-Dividendenstrategien stehen dabei das „Top-10“-, „Low-5“-, und „Low-1“-Portfolio sowie der Dividendenindex DivDAX. Das „Top-10“-Portfolio setzt sich aus den zehn dividendenstärksten Aktien des deutschen Aktienindex DAX zusammen. Daneben sind im „Low-5“- bzw. „Low-1“-Portfolio die fünf bzw. eine Aktie der „Top-10“ mit dem niedrigsten Aktienkurs allokiert. Alle Aktien werden gleichgewichtet.
Mithilfe der jährlichen Bestimmung der diskreten Rendite, annualisierten Volatilität und Sharpe Ratio wird die Performance der DAX-Dividendenstrategien im Vergleich zum DAX sowie DAX Equal Weight als Benchmarks analysiert.
Obwohl die „Top-10“ in fünf der elf Jahre eine höhere diskrete Rendite als der DAX erreichten, lag ihre durchschnittlichen Sharpe Ratio mit 0,57 um 0,02 unter der des DAX (0,59). Auch die „Low-5“ erzielten in sechs der elf Jahre eine Überrendite, waren jedoch hinsichtlich ihrer Gesamtperformance um 0,03 unterhalb des DAX positioniert.
Demgegenüber erwies sich das sehr risikoreiche „Low-1“-Portfolio mit einer durchschnittlichen Sharpe Ratio von 0,75 als überlegen gegenüber dem DAX.
Überraschenderweise war das Rendite-Risiko-Profil des DivDAX erfolgreicher als das der „Top-10“ und „Low-5“.
Sämtliche DAX-Dividendenstrategien waren dem DAX Equal Weight risikoadjustiert überlegen, was die Wertpapiergewichtung als Erfolgsfaktor des DAX vermuten lässt.
Customer Perceived Value is at the heart of marketing and business. There are many approaches, but inconsistencies exist in understanding the nature, characteristics and conceptualization of this phenomenon. Therefore, a coherent concept is required. This thesis is an endeavor to go beyond the established terminology to enrich the discipline. It provides an interdisciplinary investigation that combines philosophical, psychological, physical and neuroscience perspectives to understand the nature of Customer Perceived Value and structure knowledge using a new approach. Based on a synthesis of theories, a new metaphorical concept was developed that considers the nature of customer perceived value from the perspective of world-forming concepts of information and energy and their interactions. The uniqueness of the concept lies in its ability to integrate and explain all qualities of Customer Perceived Value and amalgamate all previous conceptions in this field. This new concept provides a practical tool for managing value from a strategic marketing perspective. The new approach also represents the basis for a new Customer Value typology. Not the primary but equally significant goal of this thesis is the popularization of the following theories: Simonov’s Need-Informational theory of emotions, Ostwald's Energetics, Rupert Sheldrake’s Theory of Morphic Fields and Kurt Lewin's Field Theory. This concept, developed based on the insights provided by these theories, contributes to marketing science.
A simulation study to analyse the impact of V2X communication on the emergency vehicle response time
(2024)
Focused on the crucial intersection of urban mobility and public safety, the thesis investigates the practical implications of integrating V2X technology for the streets of Ingolstadt. This study employs simulation techniques to mimic real-world situations, evaluating the tangible benefits of V2X-enabled vehicles in speeding up emergency arrival time.
By connecting theory with simulations, this work aims to guide policymaking and technology development dept. for improved emergency response systems, for the roads of Ingolstadt.
The increasing demand for electric vehicles EV in recent years has led to a growing need for advanced BMS that can accurately estimate the state of health SOH of batteries. The SOH is a critical parameter that determines the performance and lifespan of batteries, and accurate estimation of these parameters is essential for optimizing battery utilization and improving the overall efficiency and reliability of EV. Accurately estimating the SOH of batteries in real driving conditions is a challenging task due to the dynamic nature of driving cycles, which can cause significant variations in battery behavior. Moreover, the accuracy of existing estimation techniques is often affected by factors such as battery degradation, temperature variations, and non-linearities in battery behavior.
To address these challenges, researchers and engineers have developed a wide range of techniques and algorithms for estimating the SOH of batteries in real driving conditions. These techniques include model-based approaches, data-driven methods, and hybrid techniques that combine both model-based and data-driven approaches. The objective of this master thesis is to critically review the existing literature on estimation techniques for SOH in real driving cycles, identify the strengths and limitations of different approaches, and propose a novel estimation technique that can overcome the limitations of existing approaches. The proposed technique will be evaluated using real-world data obtained from a test vehicle.
It is vital to do a precise assessment of the condition of these batteries in order to guarantee that they can be used safely and to prevent explosions that may possibly be catastrophic. The challenges that were discussed before could be solved with the assistance of prediction models. The purpose of this research is to evaluate the accuracy of predictions made by a variety of machine learning algorithms on the state of the battery. In order to achieve this result, time series forecasting techniques are used to data metrics. It was shown that Long Short-Term Memory LSTM models perform very well when it comes to the creation of forecasts that can be relied upon. An accurate forecast made with the aid of machine learning models may assist in increasing sales of electric vehicles and ensuring that these batteries are used in a secure manner.
In Zeiten der Energiewende und der Abhängigkeit von fossilen Energieträgern beschäftigt sich diese Arbeit mit elektrischen Wärmepumpen sowie den Technologien zur Optimierung ihres Betriebs, wie beispielsweise der Leistungsregelung. Im Fokus der Untersuchung steht die Kennfeldmessung einer Inverter-gesteuerten Hybridwärmepumpe unter verschiedenen Betriebsbedingungen wie der Frequenz, Quell und Senkentemperatur. Es werden 60 Messszenarien festgelegt und für jedes Szenario nach Aufbereitung und Bereinigung der Messdaten der jeweilige COP ermittelt. Die Analyse des COPs zeigt, dass Wärmepumpen bei niedrigen Senketemperaturen optimale Betriebspunkte bei niedriger Inverter-Frequenz verzeichnen. Hingegen arbeiten die Anlagen bei hohen Senkentemperaturen am effizientesten im hohen Frequenzbereich. Aus den Messdaten wird mithilfe einer Kurvenanpassung eine polynomische Funktion abgeleitet. Das daraus entwickelte Modell bildet bei einer Quelltemperatur von 5 °C und Senkentemperatur von 30 °C den optimalen Betriebspunkt bei 25 % Inverter-Frequenz mit einem COP von 5,5 ab. Hierbei liegt unter diesen Bedingung bei 100 % der niedrigste COP von 4,5 vor. Als direktes Gegenbeispiel dazu, liegt bei 5 °C Sole, 50 °C Senke und 100 % Inverter-Frequenz die höchste Effizienz mit einem COP von 3 vor, während bei 25 % der niedrigste COP von etwa 2,3 abgebildet wird. Trotz methodischen Abweichungen an der orientierten Norm DIN EN 14511-2, erweist das mathematische Modell mit einem R2-Score von 0,97 eine gute Übereinstimmung mit den Messdaten auf.
Das Ziel der Masterarbeit war es, eine mobile Prüfbox zu konzipieren und konstruieren, mit deren Hilfe sich die Vorteile automatisierter Qualitätskontrolle durch industrielle Bildverarbeitung anhand kleiner additiv gefertigter Bauteile auf Messen und bei Unternehmen demonstrieren lassen. Dazu werden zunächst die theoretischen Grundlagen der additiven Fertigung sowie der industriellen Bildverarbeitung erläutert. Anschließend wird ein Prüfbox-Konzept methodisch erarbeitet und auskonstruiert. Zudem wird ein Konzeptschaltplan der elektrischen Komponenten vorgestellt und sowie eine Aufstellung der Materialkosten durchgeführt.
This paper delves into the integration of Artificial Intelligence (AI) within the infotainment industry and its impact on privacy concerns. The rise of personalized experiences in infotainment has transformed user engagement by offering tailored recommendations across various content categories such as news, music, videos, and social media updates. However, the continuous gathering of personalized data by AI raises privacy issues. Despite these concerns, AI algorithms analyze user preferences, behaviors, and demographics to provide personalized content, improving user satisfaction and interaction. While some regions have implemented regulations to protect user privacy, finding a balance between the benefits of AI, user expectations, and privacy protection presents a challenge. Enhancing transparency, user control, and consent procedures are essential to ensure the ethical use of AI systems and safeguard user information. Recognizing the potential risks associated with AI-driven infotainment allows stakeholders to collaborate on a comprehensive framework that maximizes the advantages of AI while preserving privacy.
Design und Evaluation einer App für automatisierte Fahrzeuge mit besonderem Fokus auf Inklusion
(2024)
Die Integration automatisierter Fahrzeuge (AVs) bietet bedeutende Vorteile für Menschen mit Sehbehinderungen. Diese Forschungsarbeit fokussiert sich darauf, wie eine umfassende Unterstützung für sehbehinderte Personen während sämtlicher Phasen einer Reise in vollautomatisierten Fahrzeugen gewährleistet werden kann. Im Zuge dieser Untersuchung erfolgte eine eingehende Analyse der wahrgenommenen User Experience und des Vertrauens in das Unterstützungskonzept. Die Bedürfnisse der sehbehinderten Zielgruppe wurden unter anderem durch Interviews präzise ermittelt. Eine vielversprechende Lösung könnte die Entwicklung einer dedizierten App sein.
Durch ein Wizard-of-Oz-Experiment wurden sowohl die App, als auch ein entfernter Reisebegleiter von Nutzern getestet, um eine umfassende Antwort auf die Forschungsfrage zu erhalten. Dieser Ansatz soll sicherstellen, dass die Unterstützung nahtlos und effektiv in den Alltag sehbehinderter Menschen integriert werden kann. Während das Vertrauen in den entfernten Reisebegleiter signifikant höher war als in die App, konnte in der wahrgenommenen User Experience kein signifikanter Unterschied zwischen den beiden Konzepten festgestellt werden.
Da die Erfüllung bestimmter Modelleigenschaften und formeller Anforderungen entscheidend für den Erfolg von Softwareprojekten ist, führt dies zu einer klaren Motivation für die Entwicklung eines Werkzeuges, welche die Prüfung von Modelleigenschaften rationalisiert und beschleunigt. Diese Arbeit widmet sich dem Design und der Implementierung eines Plugins für Visual Paradigm, das die automatisierte formale Prüfung von der Erreichbarkeit der Zustände eines UML-Zustandsdiagramms ermöglicht. Das Plugin setzt sich aus dem Auslesen der Diagrammelemente, deren Übersetzung in das SMTLIB-Format und schlussendlich der Prüfung mithilfe des Z3-Solvers zusammen. Für die Evaluation des Plugins wurde eine Interviewstudie durchgeführt. Diese ergab, dass die korrekte Nutzung von Werkzeugen zur automatisierten Prüfung, zu einer signifikanten Reduzierung der benötigten Zeit und zur Verringerung der menschlichen Fehlerquote bei Erreichbarkeitsanalysen führt. Dies zeigt, dass die korrekte Nutzung des Plugins wahrscheinlich ebenso dieselben Ergebnisse vorweisen könnte.
Die vorliegende Bachelorarbeit konzentriert sich auf die Entwicklung und Implementierung eines innovativen Konzepts unter Nutzung von Graphdatenbanken und ihrer Analysemöglichkeiten in der Prozessautomatisierung. Die Motivation hierfür erwächst aus der zunehmenden Bedeutung von Datenanalyse und -verarbeitung in der heutigen Geschäftswelt, gepaart mit dem Bedarf an effizienten,
flexiblen und skalierbaren Lösungen im Datenmanagement und der Prozessoptimierung.
In einer Zeit, in der der Wert von Daten stetig steigt, wird die effiziente Sammlung, Speicherung und Analyse großer Datenmengen immer entscheidender für den Geschäftserfolg. Herkömmliche Datenbankmodelle kommen hierbei oft an ihre Grenzen, besonders bei der Handhabung komplexer Beziehungen und dynamischer Datenstrukturen. Graphdatenbanken stellen in diesem Kontext eine fortschrittliche Alternative dar, die eine effiziente Darstellung und Verarbeitung von Datenbeziehungen ermöglichen.
Es mangelt jedoch an umfassenden Konzepten, die eine Integration von Graphdatenbanken in die Prozessautomatisierung verschiedener Unternehmensgrößen erlauben. Diese Arbeit strebt danach, diese Forschungslücke zu schließen, indem sie ein praxisnahes Konzept für die Implementierung von Prozessinstanzdaten in eine Graphdatenbank entwickelt. Dies leistet nicht nur einen Beitrag zur wissenschaftlichen Forschung, sondern hat auch das Potenzial, Effizienz und Effektivität von Geschäftsprozessen wesentlich zu verbessern.
Das Hauptziel dieser Arbeit ist die Entwicklung eines Konzepts, das die Stärken von Graphdatenbanken nutzt, um Prozessinstanzdaten und verarbeitete Entitätsdaten in Unternehmen optimal zu analysieren. Dies umfasst die Erstellung eines Prototyps, der die Möglichkeiten von Graphdatenbanken zur Verbesserung von Entscheidungsprozessen durch präzisere Datenanalysen und -interpretationen demonstriert.