Universität Stuttgart
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Item Open Access Eine OSLC-Plattform zur Unterstützung der Situationserkennung in Workflows(2015) Jansa, PaulDas Internet der Dinge gewinnt immer mehr an Bedeutung durch eine starke Vernetzung von Rechnern, Produktionsanlagen, mobilen Endgeräten und weiteren technischen Geräten. Derartige vernetzte Umgebungen werden auch als SMART Environments bezeichnet. Auf Basis von Sensordaten können in solchen Umgebungen höherwertige Situationen (Zustandsänderungen) erkannt und auf diese meist automatisch reagiert werden. Dadurch werden neuartige Technologien wie zum Beispiel "Industrie 4.0", "SMART Homes" oder "SMART Cities" ermöglicht. Komplexe Vernetzungen und Arbeitsabläufe in derartigen Umgebungen werden oftmals mit Workflows realisiert. Um eine robuste Ausführung dieser Workflows zu gewährleisten, müssen Situationsänderungen beachtet und auf diese entsprechend reagiert werden, zum Beispiel durch Workflow-Adaption. Das heißt, erst durch die Erkennung höherwertiger Situationen können solche Workflows robust modelliert und ausgeführt werden. Jedoch stellen die für die Erkennung von Situationen notwendige Anbindung und Bereitstellung von Sensordaten eine große Herausforderung dar. Oft handelt es sich bei den Sensordaten um Rohdaten. Sie sind schwer extrahierbar, liegen oftmals nur lokal vor, sind ungenau und lassen sich dementsprechend schwer verarbeiten. Um die Sensordaten zu extrahieren, müssen für jeden Sensor individuelle Adapter programmiert werden, die wiederum ein einheitliches Datenformat der Sensordaten bereitstellen müssen und anschließend mit sehr viel Aufwand untereinander verbunden werden. Im Rahmen dieser Diplomarbeit wird ein Konzept erarbeitet und entwickelt, mit dessen Hilfe eine einfache Integration von Sensordaten ermöglicht wird. Dazu werden die Sensoren über eine webbasierte Benutzeroberfläche oder über eine programmatische Schnittstelle in einer gemeinsamen Datenbank registriert. Die Sensordaten werden durch REST-Ressourcen abstrahiert, in RDF-basierte Repräsentationen umgewandelt und mit dem Linked-Data Prinzip miteinander verbunden. Durch die standardisierte Schnittstelle können Endbenutzer oder Anwendungen über das Internet auf die Sensordaten zugreifen, neue Sensoren anmelden oder entfernen.Item Open Access Modeling recommendations for pattern-based mashup plans(2018) Das, SomeshData 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.Item Open Access Anonymisierung von Daten : von der Literatur zum Automobilbereich(2023) Herkommer, JanDie 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.Item Open Access Inferring object hypotheses based on feature motion from different sources(2015) Fuchs, SteffenPerception systems in robotics are typically closely tailored to the given task, e.g., in typical pick-and-place tasks the perception systems only recognizes the mugs that are supposed to be moved and the table the mugs are placed on. The obvious limitation of those systems is that for a new task a new vision system must be designed and implemented. This master's thesis proposes a method that allows to identify entities in the world based on motion of various features from various sources. This is without relying on strong prior assumptions and to provide an important piece towards a more general perception system. While entities are rigid bodies in the world, the sources can be anything that allows to track certain features over time in order to create trajectories. For example, these feature trajectories can be obtained from RGB and RGB-D sensors of a robot, from external cameras, or even the end effector of the robot (proprioception). The core conceptual elements are: the distance variance between trajectory pairs is computed to construct an affinity matrix. This matrix is then used as input for a divisive k-means algorithm in order to cluster trajectories into object hypotheses. In a final step these hypotheses are combined with previously observed hypotheses by computing the correlations between the current and the updated sets. This approach has been evaluated on both simulated and real world data. Generating simulated data provides an elegant way for a qualitative analysis of various scenarios. The real world data was obtained by tracking Shi-Tomasi corners using the Lucas-Kanade optical flow estimation of RGB image sequences and projecting the features into range image space.Item Open Access Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows(2023) Kschidock, HelenaIn this work, we implement, test, and validate an Euler-Lagrangian point-particle tracking framework for the commercial aerodynamics and aeroacoustics simulation tool ultraFluidX, which is based on the Lattice Boltzmann Method and optimized for GPUs. Our framework successfully simulates one-way and two-way coupled particle-laden flows based on drag forces and gravitation. Trilinear interpolation is used for determining the fluid's macroscopic properties at the particle position. Object and domain boundary conditions are implemented using a planar surface approximation. The whole particle framework is run within three dedicated GPU kernels, and data is only copied back to the CPU upon output. We show validation for the velocity interpolation, gravitational acceleration, back-coupling forces and boundary conditions, and test runtimes and memory requirements. We also propose the next steps required to make the particle framework ready for use in engineering applications.Item Open Access Concepts and methods for the design, configuration and selection of machine learning solutions in manufacturing(2021) Villanueva Zacarias, Alejandro Gabriel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The application of Machine Learning (ML) techniques and methods is common practice in manufacturing companies. They assign teams to the development of ML solutions to support individual use cases. This dissertation refers as ML solution to the set of software components and learning algorithms to deliver a predictive capability based on available use case data, their (hyper) paremeters and technical settings. Currently, development teams face four challenges that complicate the development of ML solutions. First, they lack a formal approach to specify ML solutions that can trace the impact of individual solution components on domain-specific requirements. Second, they lack an approach to document the configurations chosen to build an ML solution, therefore ensuring the reproducibility of the performance obtained. Third, they lack an approach to recommend and select ML solutions that is intuitive for non ML experts. Fourth, they lack a comprehensive sequence of steps that ensures both best practices and the consideration of technical and domain-specific aspects during the development process. Overall, the inability to address these challenges leads to longer development times and higher development costs, as well as less suitable ML solutions that are more difficult to understand and to reuse. This dissertation presents concepts to address these challenges. They are Axiomatic Design for Machine Learning (AD4ML), the ML solution profiling framework and AssistML. AD4ML is a concept for the structured and agile specification of ML solutions. AD4ML establishes clear relationships between domain-specific requirements and concrete software components. AD4ML specifications can thus be validated regarding domain expert requirements before implementation. The ML solution profiling framework employs metadata to document important characteristics of data, technical configurations, and parameter values of software components as well as multiple performance metrics. These metadata constitute the foundations for the reproducibility of ML solutions. AssistML recommends ML solutions for new use cases. AssistML searches among documented ML solutions those that better fulfill the performance preferences of the new use case. The selected solutions are then presented to decision-makers in an intuitive way. Each of these concepts was evaluated and implemented. Combined, these concepts offer development teams a technology-agnostic approach to build ML solutions. The use of these concepts brings multiple benefits, i. e., shorter development times, more efficient development projects, and betterinformed decisions about the development and selection of ML solutions.Item Open Access Visual Analytics im Kontext der Daten- und Analysequalität am Beispiel von Data Mashups(2016) Behringer, MichaelViele Prozesse und Geschäftsmodelle der Gegenwart basieren auf der Auswertung von Daten. Durch Fortschritte in der Speichertechnologie und Vernetzung ist die Akquisition von Daten heute sehr einfach und wird umfassend genutzt. Das weltweit vorhandene Datenvolumen steigt exponentiell und sorgt für eine zunehmende Komplexität der Analyse. In den letzten Jahren fällt in diesem Zusammenhang öfter der Begriff Visual Analytics. Dieses Forschungsgebiet kombiniert visuelle und automatische Verfahren zur Datenanalyse. Im Rahmen dieser Arbeit werden die Verwendung und die Ziele von Visual Analytics evaluiert und eine neue umfassendere Definition entwickelt. Aus dieser wird eine Erweiterung des Knowledge Discovery-Prozesses abgeleitet und verschiedene Ansätze bewertet. Um die Unterschiede zwischen Data Mining, der Visualisierung und Visual Analytics zu verdeutlichen, werden diese Themengebiete gegenübergestellt und in einem Ordnungsrahmen hinsichtlich verschiedener Dimensionen klassifiziert. Zusätzlich wird untersucht, inwiefern dieser neue Ansatz im Hinblick auf Daten- und Analysequalität eingesetzt werden kann. Abschließend wird auf Basis der gewonnenen Erkenntnisse eine prototypische Implementierung auf Basis von FlexMash, einem an der Universität Stuttgart entwickelten Data Mashup-Werkzeug, beschrieben. Data Mashups vereinfachen die Einbindung von Anwendern ohne technischen Hintergrund und harmonieren daher ausgezeichnet mit Visual Analytics.Item Open Access Vision assisted biasing for robot manipulation planning(2018) Puang, En YenSampling efficiency has been one of the major bottlenecks of sampling-based motion planner. Although being more reliable in complex environments, Rapidly-exploring Random Tree for example often requires longer planning time than its optimisation-based counterpart. Recent developments have introduced numerous methods to bias sampling in configuration-space. Gaussian mixture model, in particular, was proposed to estimate feasible regions in configuration-space for low-variance task. Unfortunately this method does not adapt its biases according to individual planning scene during inference. Therefore, this work proposes vision assisted biasing to adapt biases by changing the weights of Gaussian components upon query. It uses autoencoder to extract features directly from depth image, and the resulted latent code is then used for either nearest neighbours search or direct weights prediction. With a modified pipeline, these extensions show improvements on not only the sampling efficiency but also path optimality of simple motion planner.Item Open Access Robust Quasi-Newton methods for partitioned fluid-structure simulations(2015) Scheufele, KlaudiusIn recent years, quasi-Newton schemes have proven to be a robust and efficient way for the coupling of partitioned multi-physics simulations in particular for fluid-structure interaction. The focus of this work is put on the coupling of partitioned fluid-structure interaction, where minimal interface requirements are assumed for the respective field solvers, thus treated as black box solvers. The coupling is done through communication of boundary values between the solvers. In this thesis a new quasi-Newton variant (IQN-IMVJ) based on a multi-vector update is investigated in combination with serial and parallel coupling systems. Due to implicit incorporation of passed information within the Jacobian update it renders the problem dependent parameter of retained previous time steps unnecessary. Besides, a whole range of coupling schemes are categorized and compared comprehensively with respect to robustness, convergence behaviour and complexity. Those coupling algorithms differ in the structure of the coupling, i.\,e., serial or parallel execution of the field solvers and the used quasi-Newton methods. A more in-depth analysis for a choice of coupling schemes is conducted for a set of strongly coupled FSI benchmark problems, using the in-house coupling library preCICE. The superior convergence behaviour and robust nature of the IQN-IMVJ method compared to well known state of the art methods such as the IQN-ILS method, is demonstrated here. It is confirmed that the multi-vector method works optimal without the need of tuning problem dependent parameters in advance. Furthermore, it appears to be especially suitable in conjunction with the parallel coupling system, in that it yields fairly similar results for parallel and serial coupling. Although we focus on FSI simulation, the considered coupling schemes are supposed to be equally applicable to various kinds of different volume- or surface-coupled problems.Item Open Access Comprehensive Support of the Lifecycle of Machine Learning Models in Model Management Systems(2019) Popp, MatthiasToday, Machine Learning (ML) is entering many economic and scientific fields. The lifecycle of ML models includes data pre-processing to transform raw data into features, training a model with the features, and providing the model to answer predictive queries. The challenge is to ensure accurate predictions by continuously updating the model with automatic or manual retraining. To be aware of all changes, e.g. datasets and parameters, it is required to store metadata over the entire ML lifecycle. In this thesis we present a concept and system for comprehensive support of the ML lifecycle. The concept includes a metadata schema, as well as a solution to collect and enrich the metadata. The metadata schema contains information about the experiment, runs, executions, executables and common artifacts in ML such as datasets, models, and metrics. The stored information can be used for comparisons, re-iterations, and backtracking of ML experiments. We achieve this by tracking the lineage of ML pipeline steps and collecting metadata such as hyperparameters. Furthermore, a prototype is implemented to demonstrate and evaluate the concept. A case study, based on a selected scenario, serves as the basis for a qualitative assessment. The case study shows that the concept meets all the requirements and is therefore a suitable approach to comprehensively support ML model lifecycle.