Universität Stuttgart

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    Performance measurements for personalizable route planning for uncorrelated edge costs
    (2021) Bühler, Felix
    Nowadays, ordinary route planners compute paths by choosing the shortest or fastest route. However, there exist additional metrics from which users with varying preferences could benefit. Personalized route planning offers the possibility to combine different metrics with personal preferences. Nevertheless, personalized route planning has mainly been tested with correlated metrics. But when including uncorrelated metrics, the computing time increases significantly. Previous work found that the speedup technique “Customizable Route Planning” can lead to feasible speedups for single metric calculations. Thus, in this work, we investigate how this speedup technique for Dijkstra improves the query performances of “Personalizable Route Planning” compared to “Personalizable Contraction Hierarchies”. Furthermore, we study the performances on uncorrelated metrics. We introduce a graph structure to compare the personalized speedup techniques “Personalizable Contraction Hierarchies”, “Personalizable Customizable Route Planning” and “Personalizable Route Planning”. Three graph partitioning algorithms have been implemented to realize “Customizable Route Planning”: K-means, Gonzales, and Merge. Our experiments show that Merge works well in combination with “Personalizable Contraction Hierarchies” preprocessing. We found that “Personalizable Customizable Route Planning” is a good alternative, as it uses much fewer edges for finding the costs of the shortest path. For uncorrelated metrics, “Personalizable Customizable Route Planning” and “Personalizable Route Planning” achieved speedups higher than “Personalizable Contraction Hierarchies”. Our contribution comprises a novel graph structure for comparing different Dijkstra variants. With our experiments, we provide a deeper understanding of the personalized route planning problem. Additionally, we propose improvements for “Personalizable Contraction Hierarchies” for less contracted graphs with uncorrelated metrics.
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    Untersuchung der Antriebsstrangdynamik in Windenergieanlagen
    (2020) Horch, Joachim
    Diese Arbeit beschäftigt sich damit die Stabilität und Funktionstüchtigkeit des Antriebsstranges einer Windenergieanlage der Größenordnung 10 MW zu untersuchen. Hierfür erfolgt der Aufbau eines Computermodells einer 10-MW-Windenergieanlage mithilfe des Mehrkörpersimulationsprogrammes SIMPACK. Weiterhin wird eine Parameterstudie durchgeführt, welche über eine Matlab-induzierte SIMPACK-Simulation speziell ausgewählte Parameter des Antriebsstranges variiert, Simulationen durchführt und so den Einfluss bestimmter Parameter, sowie Parameterkombinationen, auf die Stabilität des Antriebsstranges prüft. Auf diese Weise sollen Stabilitätskriterien für einen Antriebsstrang dieser Größenordnung ermittelt werden. Es erfolgen sowohl statische, als auch dynamische Untersuchungen.
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    Visual prediction of quantitative information using social media data
    (2017) Fatehi Ebrahimzadeh, Hamed
    In recent years, the availability of a vast amount of user-generated data via social media, has given an opportunity to researchers for analyzing these data sources and discovering meaningful information. However, processing and understanding this immense amount of data is challenging and calls for automated approaches, and involvement of field experts to use their field knowledge and experience to enhance the data analysis. So far, existing approaches only enable the detection of indicative information from the data such as the occurrence of critical incidents, relevant situation reports etc. Consequently, the next step would be to better relate the user provided information to the real-world quantities. In this work, a predictive visual analytics approach is developed that offers semi-automated methods to estimate quantitative information (e.g. number of people who participate in a public event). At first, the approach provides interactive visual tools to explore social media data in time and space and select features required as input for training and prediction interactively. Next, a suitable model can be trained based on these feature sets and applied for prediction. Finally, the approach also allows to visually explore prediction results and measure quality of predictions with respect to the ground truth information obtained from past observations. The result of this work is a generic visual analytics approach, that provides expert user with visual tools for a constant interaction between human and machine, for producing quantitative predictions based on social media data. The results of predictions are promising, especially in cases that the location, time and other related information to public events are considered together with the content of user-generated data.
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    Integration of IoT devices via a blockchain-based decentralized application
    (2017) Ahmad, Afzaal
    Blockchains are shared, immutable ledgers for recording the history of transactions. They foster a new generation of transactional applications that establish trust, accountability, and transparency. It enables contract partners to secure a deal without involving a trusted third party. Initially, the focus was on financial industry for digital assets trading like Bitcoin, but with the emergence of Smart Contracts, blockchain becomes a complete programmable platform. Many research and commercial organization start diving into blockchain world, bringing new ideas of its application in different sectors like supply chain, Health, and autonomous shopping. This thesis presents an idea to integrate Internet of Things (IoT) devices via a blockchain based decentralize application based on Ethereum. The application consists of front-end application which can be deployed to any web server, and a smart contract which will be deployed on a private blockchain network comprises of Peer-to-Peer (P2P) connected IoT devices acting as full Ethereum node. The application emulates the digital transport ticketing system where the asset is a ticket which can be purchased and paid by the user using ether in their Ethereum account on the blockchain. Once the purchase transaction is mined, it is propagated to all the peers. Ticket can now be accessed locally without requesting any centralized system, which makes the system easily accessible and safe because of the security, data integrity and decentralization of the blockchain-based systems.
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    Modeling and timing analysis of micro-ROS application on an off-road vehicle control unit
    (2022) Bappanadu, Suraj Rao
    ROS is known to be the most popular middleware for the development of software in modern day robots. It's next version, ROS 2 is highly modular and offers flexibility by supporting on microprocessors running desktop operating systems. Micro-ROS puts the major ROS 2 features on microcontrollers, i.e., highly resource-constrained computing devices running specialized real-time operating systems. ROS 2 is also of great importance for other domains, including autonomous driving and the off-road sector. Accordingly, there is significant interest in bringing micro-ROS to typical automotive control units. These embedded platforms support AUTOSAR Classic OSEK-like operating system which is very different in many aspects when compared to the platforms supported by micro-ROS. Some of the aspects have already been addressed in a previous work. This thesis mainly focuses on mapping the micro-ROS execution scheme to AUTOSAR scheme and dynamic memory management of the micro-ROS stack. From the micro-ROS architecture perspective, to successfully port the stack on an AUTOSAR-based ECU, the middleware and other layers of the stack are also analysed and adapted using a standard approach to support tasks-like execution model instead of threads-like execution model. Additionally, the support for standard CAN protocol based on custom transport configuration with the hardware CAN on the BODAS ECU is introduced. Model-based development methods have proven their utility in automotive industry. Therefore, we also focus on describing the timing properties of the micro-ROS stack in a model-based approach. We develop a generic model which is independent of a specific modeling language. In the next step, we realize the generic model using the widely used AMALTHEA language and analyse how well the developed model predicts the timing behavior of micro-ROS tasks. Finally, the effectiveness of the approach regarding timing and modeling is demonstrated with a micro-ROS test application first on Linux and then on the off-road vehicle control unit BODAS RC18-12/40 by Bosch Rexroth.
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    Developing a multimodal feedback motion guidance system in VR for people with motion disabilities
    (2021) Wennrich, Kevin
    Motion is an important aspect in the area of physiotherapy. The correctness of those motions is even more important, especially in the home exercises. In this thesis, the prototype of a multimodal guidance system in virtual reality, which tracks the movements of the users and compares it to the correct position in the field of physiotherapy exercises was created. The get the requirements for the system, people who needed to go to physiotherapy, because of an injury or a disability (stroke, MS, NPC), were interviewed, as well as a physiotherapist. Based on the results, we have implemented a virtual physiotherapist and the auditory guidance as two modalities. Further modalities have been the ghostarm and the haptic guidance as vibration bands. The prototype in which the user can choose and combine the guidances have been developed. The system, the modalities and its limits have been evaluated in a online study and a pilot study, with the results, that until now the ghostarm and virtual physiotherapist are the most liked guidances. A user study is planned for the future.
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    Modeling recommendations for pattern-based mashup plans
    (2018) Das, Somesh
    Data mashups are modeled as pipelines. The pipelines are basically a chain of data processing steps in order to integrate data from different data sources into a single one. These processing steps include data operations, such as join, filter, extraction, integration or alteration. To create and execute data mashups, modelers need to have technical knowledge in order to understand these data operations. In order to solve this issue, an extended data mashup approach was created - FlexMash developed at the University of Stuttgart - which allows users to define data mashups without technical knowledge about any execution details. Consquently, modelers with no or limited technical knowledge can design their own domain-specific mashup based on their use case scenarios. However, designing data mashups graphically is still difficult for non-IT users. When users design a model graphically, it is hard to understand which patterns or nodes should be modeled and connected in the data flow graph. In order to cope with this issue, this master thesis aims to provide users modeling recommendations during modeling time. At each modeling step, user can query for recommendations. The recommendations are generated by analyzing the existing models. To generate the recommendations from existing models, association rule mining algorithms are used in this thesis. If users accept a recommendation, the recommended node is automatically added to the partial model and connected with the node for which recommendations were given.
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    Interdisciplinary composition of E-Learning platforms based on reusable low-code adapters
    (2022) Meißner, Niklas
    Electronic Learning (E-Learning) platforms or Learning Management Systems (LMSs) are becoming increasingly popular and, accordingly, are being used more and more by teachers at schools and university professors. They are used to digitally distribute educational material to students and provide the opportunity to, e.g., upload and collect assignments, solve tasks, and view grades. LMSs have been growing in popularity and are used alongside in-person lectures as an adjunct to self-study. Due to digital teaching during the COVID-19 pandemic, LMSs have increased in importance significantly. Even in after-pandemic times, with returning in-person lectures, it is hard to imagine teaching at universities without these platforms. The possibilities of working with the established LMSs are enormous. However, a closer look also reveals some negative aspects that were not considered in developing and using these platforms. The existing LMSs lack individualization for lecturers of their courses and a motivating design for students. Plugins attempt to remedy this, but they are complex and time-consuming to use. Thus, the underlying problems are, on the one hand, that lecturers are limited in the design of their courses and, on the other hand, that students experience disadvantages in terms of motivation and interactivity. This thesis aims to develop a concept for an e-learning platform that addresses these problems, supports lecturers in designing their courses, and motivates and assists students in learning. Under the aspect of generalization, a concept for a Software Product Line (SPL) was developed for the requirements of a wide variety of study programs, providing lecturers with a base platform and enabling them to use low-code adapters to design and modify their courses. In addition, the platform and a support team will assist lecturers in using the LMS and creating educational material. For the conceptual design of the LMS, some already existing solutions and approaches were examined to address a similar problem. However, similar problems have been insufficiently solved or overlap with the problem statement of this thesis only to a limited extent. After a requirements analysis, the requirements were gathered and listed so that solutions could then be developed. The prototypical implementation of the concept ’Interactive Training Remote Education Experience (IT-REX)’ was used to design the base e-learning platform and to include gamification aspects. However, since IT-REX was designed for computer science and software engineering students in the first semesters, it had to be modified for a broader range of uses. To evaluate the approach of the concept, a case study was conducted in which a low-fidelity prototype of the concept was presented to lecturers and other experts in the field of higher education didactics, learning psychology, and vocational and technical pedagogy. Subsequently, a questionnaire was used to assess and evaluate the previously defined requirements. The result of this elaboration is the concept for the e-learning platform with the corresponding prototype. Based on the feedback of the lecturers and experts, improvements and revisions could be identified. Furthermore, the evaluation helped to investigate how the platform’s usability could be enhanced to improve the structuring and design of the courses by the lecturers. Finally, future developments and further investigations based on the concept were described.
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    Das Ordnungsproblem für Automatengruppen und verwandte Fragestellungen
    (2019) Bühler, Andreas
    In dieser Arbeit werden Problemstellungen in der Klasse der Automatenhalbgruppen untersucht. Ein besonderer Augenmerk gilt dabei dem Ordnungsproblem welches im Allgemeinen sowohl für Automatenhalbgruppen als auch für Automatengruppen unentscheidbar ist. Es wird dann für die Klasse der Automatenhalbgruppen mit beschränkter Aktivität ein Algorithmus mit überraschend geringem Platzbedarf vorgestellt. Danach wird ein Entscheidungsalgorithmus für das Mitgliedschaftsproblem in ultimativ periodischen Teilmengen von Automatenhalbgruppen beschränkter Aktivität erarbeitet. Dieses Problem beinhaltet insbesondere das Mitgliedschaftsproblem in monogenen Unterhalbgruppen, welches dadurch ebenfalls in Automatenhalbgruppen beschränkter Aktivität entscheidbar ist.
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    Anonymisierung von Daten : von der Literatur zum Automobilbereich
    (2023) Herkommer, Jan
    Die Datenanonymisierung im Automobilbereich gewinnt immer mehr an Bedeutung. Jedoch gibt es kaum Literatur und Ansätze, die sich mit der Anonymisierung von Automobildaten beschäftigen. In dieser Arbeit werden deshalb mit Hilfe einer strukturierten Literaturrecherche die aktuell verbreitetsten Verfahren und Anwendungsbereiche erörtert und die wichtigsten Erkenntnisse der Recherche zusammengefasst. So werden bei den analysierten Paper der Anwendungsbereich, die Methodik sowie der zu anonymisierende Datentyp ermittelt. DesWeiteren werden die Metriken zum Vergleich von unterschiedlichen Ansätzen betrachtet. Mit Hilfe dieser Erkenntnisse wird im Anschluss auf die Anonymisierung von Fahrzeugdaten anhand verschiedener Anwendungsfälle eingegangen und Herausforderungen und Lösungsansätze skizziert. Zuletzt wird beispielhaft ein Ansatz zur Anonymisierung von Routen implementiert, um mit Hilfe eines GPS-Sensors aufgezeichnete Fahrzeugrouten zu anonymisieren. Dabei werden zusätzliche Probleme wie der Umgang mit Messungenauigkeiten und Messfehlern sowie die tatsächlichen Auswirkungen von reduzierter Datennutzbarkeit verdeutlicht.