Universität Stuttgart
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Item Open Access Forming a hybrid intelligence system by combining Active Learning and paid crowdsourcing for semantic 3D point cloud segmentation(2023) Kölle, Michael; Sörgel, Uwe (Prof. Dr.-Ing.)While in recent years tremendous advancements have been achieved in the development of supervised Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs), still the most decisive factor for their performance is the quality of labeled training data from which the system is supposed to learn. This is why we advocate focusing more on methods to obtain such data, which we expect to be more sustainable than establishing ever new classifiers in the rapidly evolving ML field. In the geospatial domain, however, the generation process of training data for ML systems is still rather neglected in research, with typically experts ending up being occupied with such tedious labeling tasks. In our design of a system for the semantic interpretation of Airborne Laser Scanning (ALS) point clouds, we break with this convention and completely lift labeling obligations from experts. At the same time, human annotation is restricted to only those samples that actually justify manual inspection. This is accomplished by means of a hybrid intelligence system in which the machine, represented by an ML model, is actively and iteratively working together with the human component through Active Learning (AL), which acts as pointer to exactly such most decisive samples. Instead of having an expert label these samples, we propose to outsource this task to a large group of non-specialists, the crowd. But since it is rather unlikely that enough volunteers would participate in such crowdsourcing campaigns due to the tedious nature of labeling, we argue attracting workers by monetary incentives, i.e., we employ paid crowdsourcing. Relying on respective platforms, typically we have access to a vast pool of prospective workers, guaranteeing completion of jobs promptly. Thus, crowdworkers become human processing units that behave similarly to the electronic processing units of this hybrid intelligence system performing the tasks of the machine part. With respect to the latter, we do not only evaluate whether an AL-based pipeline works for the semantic segmentation of ALS point clouds, but also shed light on the question of why it works. As crucial components of our pipeline, we test and enhance different AL sampling strategies in conjunction with both a conventional feature-driven classifier as well as a data-driven CNN classification module. In this regard, we aim to select AL points in such a manner that samples are not only informative for the machine, but also feasible to be interpreted by non-experts. These theoretical formulations are verified by various experiments in which we replace the frequently assumed but highly unrealistic error-free oracle with simulated imperfect oracles we are always confronted with when working with humans. Furthermore, we find that the need for labeled data, which is already reduced through AL to a small fraction (typically ≪1 % of Passive Learning training points), can be even further minimized when we reuse information from a given source domain for the semantic enrichment of a specific target domain, i.e., we utilize AL as means for Domain Adaptation. As for the human component of our hybrid intelligence system, the special challenge we face is monetarily motivated workers with a wide variety of educational and cultural backgrounds as well as most different mindsets regarding the quality they are willing to deliver. Consequently, we are confronted with a great quality inhomogeneity in results received. Thus, when designing respective campaigns, special attention to quality control is required to be able to automatically reject submissions of low quality and to refine accepted contributions in the sense of the Wisdom of the Crowds principle. We further explore ways to support the crowd in labeling by experimenting with different data modalities (discretized point cloud vs. continuous textured 3D mesh surface), and also aim to shift the motivation from a purely extrinsic nature (i.e., payment) to a more intrinsic one, which we intend to trigger through gamification. Eventually, by casting these different concepts into the so-called CATEGORISE framework, we constitute the aspired hybrid intelligence system and employ it for the semantic enrichment of ALS point clouds of different characteristics, enabled through learning from the (paid) crowd.Item Open Access Deep learning based prediction and visual analytics for temporal environmental data(2022) Harbola, Shubhi; Coors, Volker (Prof. Dr.)The objective of this thesis is to focus on developing Machine Learning methods and their visualisation for environmental data. The presented approaches primarily focus on devising an accurate Machine Learning framework that supports the user in understanding and comparing the model accuracy in relation to essential aspects of the respective parameter selection, trends, time frame, and correlating together with considered meteorological and pollution parameters. Later, this thesis develops approaches for the interactive visualisation of environmental data that are wrapped over the time series prediction as an application. Moreover, these approaches provide an interactive application that supports: 1. a Visual Analytics platform to interact with the sensors data and enhance the representation of the environmental data visually by identifying patterns that mostly go unnoticed in large temporal datasets, 2. a seasonality deduction platform presenting analyses of the results that clearly demonstrate the relationship between these parameters in a combined temporal activities frame, and 3. air quality analyses that successfully discovers spatio-temporal relationships among complex air quality data interactively in different time frames by harnessing the user’s knowledge of factors influencing the past, present, and future behaviour with Machine Learning models' aid. Some of the above pieces of work contribute to the field of Explainable Artificial Intelligence which is an area concerned with the development of methods that help understand, explain and interpret Machine Learning algorithms. In summary, this thesis describes Machine Learning prediction algorithms together with several visualisation approaches for visually analysing the temporal relationships among complex environmental data in different time frames interactively in a robust web platform. The developed interactive visualisation system for environmental data assimilates visual prediction, sensors’ spatial locations, measurements of the parameters, detailed patterns analyses, and change in conditions over time. This provides a new combined approach to the existing visual analytics research. The algorithms developed in this thesis can be used to infer spatio-temporal environmental data, enabling the interactive exploration processes, thus helping manage the cities smartly.Item Open Access Über die Lösung der Navier-Stokes-Gleichungen mit Hilfe der Moore-Penrose-Inversen des Laplace-Operators im Vektorraum der Polynomkoeffizienten(2024) Große-Wöhrmann, Bärbel; Resch, Michael (Prof. Dr.-Ing.)Die bekannten numerischen Standard-Verfahren zur Lösung partieller Differentialgleichungen basieren auf einer räumlichen Diskretisierung des Berechnungsgebiets. Ihre Performance und Skalierbarkeit auf modernen massiv-parallelen Höchstleistungsrechnern ist von der Verfügbarkeit effizienter numerischer Verfahren zur Lösung linearer Gleichungssysteme abhängig. Angesichts grundlegender Herausforderungen erscheint die Entwicklung neuer Lösungsansätze sinnvoll. Ich stelle in dieser Arbeit einen Polynomansatz zur Lösung partieller Differentialgleichungen vor, der nicht auf einer räumlichen Diskretisierung beruht und mit Hilfe der Moore-Penrose-Inversen des Laplace-Operators die Entkopplung der Navier-Stokes-Gleichungen ermöglicht. Dabei ist der Grad der Polynome nicht grundsätzlich beschränkt, so dass eine hohe räumliche Auflösung erreicht werden kann.Item Open Access Multiscale modeling and stability analysis of soft active materials : from electro- and magneto-active elastomers to polymeric hydrogels(Stuttgart : Institute of Applied Mechanics, 2023) Polukhov, Elten; Keip, Marc-André (Prof. Dr.-Ing.)This work is dedicated to modeling and stability analysis of stimuli-responsive, soft active materials within a multiscale variational framework. In particular, composite electro- and magneto-active polymers and polymeric hydrogels are under consideration. When electro- and magneto-active polymers (EAP and MAP) are fabricated in the form of composites, they comprise at least two phases: a polymeric matrix and embedded electric or magnetic particles. As a result, the obtained composite is soft, highly stretchable, and fracture resistant like polymer and undergoes stimuli-induced deformation due to the interaction of particles. By designing the microstructure of EAP or MAP composites, a compressive or a tensile deformation can be induced under electric or magnetic fields, and also coupling response of the composite can be enhanced. Hence, these materials have found applications as sensors, actuators, energy harvesters, absorbers, and soft, programmable, smart devices in various areas of engineering. Similarly, polymeric hydrogels are also stimuli-responsive materials. They undergo large volumetric deformations due to the diffusion of a solvent into the polymer network of hydrogels. In this case, the obtained material shows the characteristic behavior of polymer and solvent. Therefore, these materials can also be considered in the form of composites to enhance the response further. Since hydrogels are biocompatible materials, they have found applications as contact lenses, wound dressings, drug encapsulators and carriers in bio-medicine, among other similar applications of electro- and magneto-active polymers. All above mentioned favorable features of these materials, as well as their application possibilities, make it necessary to develop mathematical models and numerical tools to simulate the response of them in order to design pertinent microstructures for particular applications as well as understand the observed complex patterns such as wrinkling, creasing, snapping, localization or pattern transformations, among others. These instabilities are often considered as failure points of materials. However, many recent works take advantage of instabilities for smart applications. Investigation of these instabilities and prediction of their onset and mode are some of the main goals of this work. In this sense, the thesis is organized into three main parts. The first part is devoted to the state of the art in the development, fabrication, and modeling of soft active materials as well as the continuum mechanical description of the magneto-electro-elasticity. The second part is dedicated to multiscale instabilities in electro- and magneto-active polymer composites within a minimization-type variational homogenization setting. This means that the highly heterogeneous problem is not resolved on one scale due to computational inefficiency but is replaced by an equivalent homogeneous problem. The effective response of the macroscopic homogeneous problem is determined by solving a microscopic representative volume element which includes all the geometrical and material non-linearities. To bridge these two scales, the Hill-Mandel macro-homogeneity condition is utilized. Within this framework, we investigate both macroscopic and microscopic instabilities. The former are important not only from a physical point of view but also from a computational point of view since the macroscopic stability (strong ellipticity) is necessary for the existence of minimizers at the macroscopic scale. Similarly, the investigation of the latter instabilities are also important to determine the pattern transformations at the microscale due to external action. Thereby the critical domain of homogenization is also determined for computation of accurate effective results. Both investigations are carried out for various composite microstructures and it is found that they play a crucial role in the response of the materials. Therefore, they must be considered for designing EAP and MAP composites as well as for providing reliable computations. The third part of the thesis is dedicated to polymeric hydrogels. Here, we develop a minimization-based homogenization framework to determine the response of transient periodic hydrogel systems. We demonstrate the prevailing size effect as a result of a transient microscopic problem, which has been investigated for various microstructures. Exploiting the elements of the proposed framework, we explore the material and structural instabilities in single and two-phase hydrogel systems. Here, we have observed complex experimentally observed and novel 2D pattern transformations such as diamond-plate patterns coupled with and without wrinkling of internal surfaces for perforated microstructures and 3D pattern transformations in thin reinforced hydrogel composites. The results indicate that the obtained patterns can be controlled by tuning the material and geometrical parameters of the composite.Item Open Access Physics-informed regression of implicitly-constrained robot dynamics(2022) Geist, Andreas René; Allgöwer, Frank (Prof. Dr.-Ing.)The ability to predict a robot’s motion through a dynamics model is critical for the development of fast, safe, and efficient control algorithms. Yet, obtaining an accurate robot dynamics model is challenging as robot dynamics are typically nonlinear and subject to environment-dependent physical phenomena such as friction and material elasticities. The respective functions often cause analytical dynamics models to have large prediction errors. An alternative approach to analytical modeling forms the identification of a robot’s dynamics through data-driven modeling techniques such as Gaussian processes or neural networks. However, solely data-driven algorithms require considerable amounts of data, which on a robotic system must be collected in real-time. Moreover, the information stored in the data as well as the coverage of the system’s state space by the data is limited by the controller that is used to obtain the data. To tackle the shortcomings of analytical dynamics and data-driven modeling, this dissertation investigates and develops models in which analytical dynamics is being combined with data-driven regression techniques. By combining prior structural knowledge from analytical dynamics with data-driven regression, physics-informed models show improved data-efficiency and prediction accuracy compared to using the aforementioned modeling techniques in an isolated manner.Item Open Access Identifikation und Klassifikation von Druckluft-Leckagen mit Methoden des maschinellen Lernens in Maschinen(Stuttgart : Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA, 2022) Dierolf, Christian; Sauer, Alexander (Univ.-Prof. Dr.-Ing.)In der Industrie wird Druckluft vielseitig eingesetzt. Als teure Energieform steigt zunehmend die Relevanz, denn noch heute existieren Druckluft-Leckageraten von 15 % bis 50 % und verursachen hohe Kosten. Es sind viele Produkte und Methoden zur Leckage-Erkennung in Druckluftnetzen bekannt und etabliert. Für Maschinen sind diese jedoch nur teilweise marktreif. Es ist also nicht möglich, automatisiert mit wenig Messtechnik sowie geringem Implementierungsaufwand Leckagen zu lokalisieren. Die Unterscheidung von Leckage-Ursachen war bislang Aufgabe von Fachkräften. Sie erfolgte bei wiederkehrenden Inspektionen der druckluftbetriebenen Maschine. Im Fokus der Arbeit steht, wie automatisiert in einem Druckluftsystem in Maschinen Pneumatikaktoren detektiert und Ursachen von Druckluft-Leckagen klassifiziert werden können. Durch die zentrale Druck- und Durchflussmessungen am Maschinenanschlusspunkt bleibt der Messaufwand gering. Die Arbeit beschreibt ein methodisches Vorgehen in fünf Schritten, das mit einem maschinell erlernten, datenbasierten Modell endet. Beginnend mit der Detektion des Beginns einer Aktoraktion werden Merkmale erstellt, hinsichtlich ihrer Relevanz ausgewählt und im nächsten Schritt damit Klassifikatoren erzeugt. Das Modell ist die Verknüpfung von Detektor und Klassifikator. Das Modell klassifiziert Aktorarten, -dimensionierungen und -aktionen, die häufig in der Industrie eingesetzt werden sowie hierbei auftretende Ursachen von Leckagen. Unter Berücksichtigung der 37 möglichen Klassen, konnten die Leckagen in einer druckluftbetriebenen Maschine anhand von vier Anwendungsszenarien kontinuierlich und automatisiert lokalisiert sowie die Ursachen mit guten Ergebnissen unterschieden werden. Diese Eigenschaften machen die Lösung gegenüber dem aktuellen Stand der Technik und Wissenschaft einzigartig. Bislang wirtschaftlich nicht vertretbare jedoch technisch mögliche Einsparpotenziale sind jetzt durch diese Leckage-Erkennung für druckluftbetriebene Maschinen nutzbar.Item Open Access Dependable reconfigurable scan networks(2022) Lylina, Natalia; Wunderlich, Hans-Joachim (Prof.)The dependability of modern devices is enhanced by integrating an extensive number of extra-functional instruments. These are needed to facilitate cost-efficient bring-up, debug, test, diagnosis, and adaptivity in the field and might include, e.g., sensors, aging monitors, Logic, and Memory Built-In Self-Test (BIST) registers. Reconfigurable Scan Networks (RSNs) provide a flexible way to access such instruments as well the device's registers throughout the lifetime, starting from post-silicon validation (PSV) through manufacturing test and finally during in-field operation. At the same time, the dependability properties of the system can be affected through an improper RSN integration. This doctoral project overcomes these problems and establishes a methodology to integrate dependable RSNs for a given system considering the most relevant dependability aspects, such as robustness, testability, and security compliance of RSNs.Item Open Access Architektur für einen Verzeichnisdienst in der serviceorientierten Produktionstechnik(Stuttgart : Fraunhofer Verlag, 2022) Kretschmer, Felix; Verl, Alexander (Prof. Dr.-Ing. Dr. h. c.)In dieser Arbeit wird eine Architektur entworfen, welche den Bedürfnissen einer serviceorientierten Produktionstechnik gerecht wird und einen Verzeichnisdienst bereitstellt, der unter Adaption serviceorientierter Architekturen den Schulterschluss von IT und OT ermöglicht. Hierfür werden zunächst verfügbare Technologien und Lösungen verglichen. Durch die weite Verbreitung von Verzeichnisdiensten in der IT existieren bereits zahlreiche Lösungen, welche sich mit sehr unterschiedlichem Erfolg durchgesetzt haben. In der OT hingegen existieren bisher keine vergleichbaren Lösungen, dennoch stehen standardisierte Technologien zur Verfügung, welche die Funktionalität eines Verzeichnisdienstes erfüllen können. Auf Basis von OPC UA wird ein Verzeichnisdienst entworfen, welcher Fähigkeiten und Schnittstellen von cyber-physischen Systemen im Produktionsumfeld bereitstellt. Dies erfolgt über die Abbildung der Daten in Informationsmodellen, welche auf LDAP-Verzeichnisse in der Office-IT synchronisiert werden können.Item Open Access Optimierte Zuteilungsmechanismen zur Leistungssteigerung von virtuellen Maschinen in Cloud Infrastrukturen(Stuttgart : Höchstleistungsrechenzentrum, Universität Stuttgart, 2020) Gienger, Michael; Resch, Michael M. (Prof. Dr.-Ing. Dr. h.c. Dr. h.c. Prof. E.h.)Cloud Computing ist ein flexibler und leistungsfähiger Ansatz, um Informationstechnologiedienste jeglicher Art zur Verfügung zu stellen. Die Dienste werden mit Hilfe einer Virtualisierungstechnologie bereitgestellt, über die auf einem einzigen Server mehrere virtuelle Instanzen dynamisch und ohne großen Managementaufwand verwaltet werden können. Dadurch können Serviceinstanzen mit limitierten Anforderungen auch leistungsstarken Servern zugeteilt werden, ohne deren Kapazitäten gänzlich zu blockieren. Der Vorteil effizienter Ressourcennutzung hat jedoch auch einen großen Nachteil. Aufgrund des gemeinsamen Ressourcenzugriffs können Prozessor, Hauptspeicher, Netzwerkschnittstelle oder auch lokale Festspeicher überlastet werden, so dass die gesamte Leistungsfähigkeit des Servers und damit aller virtuellen Instanzen sinkt. Ziel dieser Arbeit ist es, das Problem der Überlast in Cloud Umgebungen zu adressieren, um nachhaltig eine erhöhte Anwendungs- oder Diensteffizienz, aber auch eine verbesserte Infrastrukturauslastung, zu erreichen. Dementsprechend werden optimierte Zuteilungsstrategien für virtuelle Instanzen entwickelt, die anhand von spezifischen Anforderungsprofilen komplementäre Arbeitslasten identifizieren und diese effizient den vorhandenen Servern zuteilen können. Hierzu wird ein Mechanismus entwickelt, der sowohl initial mit Hilfe von Benutzerinformationen eine effektive Zuteilung ermöglicht, aber auch durch kontinuierliche Leistungsüberwachung von virtuellen Instanzen reaktiv eine optimale Zuteilung erreicht. Für die Bestimmung der Resultate dieser Arbeit wurde eine prototypische Anwendung implementiert, die die optimierten Zuteilungsstrategien umsetzt. Im Grundsatz basiert diese auf Anwendungscharakteristika und Echtzeitinformationen einer Cloud Infrastruktur, die vor der Bereitstellung einer virtuellen Instanz in Relation gesetzt werden. Somit kann für jede Server/Instanzkombination innerhalb der Cloud Umgebung ein Kostenfaktor berechnet werden, über den der optimale Server ausgewählt wird. Die entwickelte Lösung wurde mit Hilfe einer Anwendung aus dem Bereich Ingenieurswissenschaften in einer modifizierten, privaten Cloud Infrastruktur evaluiert und validiert. Dazu wurden individuelle Szenarien definiert, die sowohl herkömmliche als auch optimierte Zuteilungsmechanismen abbilden. Die gesammelten, realen Messwerte beinhalten folglich Referenzdaten sowie Daten von optimierten Zuteilungen, so dass eine hinreichende Datengrundlage für die Bewertung der Ergebnisse geschaffen werden kann. Die Resultate dieser Arbeit zeigen auf, dass der gewählte Ansatz die Leistungsfähigkeit einer Cloud Infrastruktur und deren Anwendungen deutlich verbessert. So kann bei gleichbleibender Infrastruktureffizienz die Anwendungsleistung durch die optimierte Zuteilung von virtuellen Instanzen um mehr als 20% gesteigert werden. Je nach Anwendungsanforderungen ist im Optimalfall sogar eine Leistungssteigerung um mehrere Faktoren möglich.Item Open Access Performance-oriented communication concepts for networked control systems(2022) Carabelli, Ben W.; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Networked control systems (NCS) integrate sensors, actuators, and digital controllers using a communication network in order to control physical processes. They can be found in diverse application areas, including automotive and aircraft systems, smart homes, and smart manufacturing systems in the context of Industry 4.0. Because control systems have demanding Quality of Service (QoS) requirements, the provisioning of appropriate communication services for NCS is a challenge. Moreover, the trend of steadily increasing digitization in many fields will likely lead to control applications with more complex system integration, especially in large-scale systems such as smart grids and smart cities. The proliferation of NCS in such an environment clearly depends on strong methods for integrating communication and control. However, there currently remains a gap between these two domains. On the one hand, the control-theoretic design and analysis methods for NCS have been based on simplistic and abstract network connection models. On the other hand, communication networks are optimized for conventional performance metrics such as throughput and latency, which do not readily translate into application specific Quality of Control (QoC) metrics. The goal of this thesis is to provide performance-oriented concepts for the design of communication services for NCS. In particular, methods for scheduling and routing the traffic of NCS and increasing their reliability through replication are developed on the basis of integrated models that capture the relationship between control-relevant characteristics of communication services and the methods that are used to provide those communication services in the network. This thesis makes the following contributions. First, we address the problem of optimally arbitrating limited communication bandwidth for a group of NCS in a shared network by designing a performance-aware dynamic priority scheduler. The resulting first scheduling policy provides asymptotic stability guarantees for each NCS and performance bounds on the joint QoC. While it is efficient to implement on the data link layer with stateless priority queueing, it requires a large optimization problem comprising all NCS to be solved initially for determining scheduler parameters. To increase the scalability, we therefore relax the scheduling problem by separating the NCS traffic into deterministic transmissions with real-time guarantees and opportunistic traffic used for QoC optimization. The resulting second scheduling policy imposes no QoS constraints on opportunistic traffic, yields less conservative stability guarantees, and allows scheduler parameters to be calculated for each NCS separately and thus much more efficiently. Second, we address the problem of optimally routing NCS traffic in networks with random latency distributions by designing a cross-layer communication service for stochastic NCS. The routing algorithm exploits trade-offs between delay and in-time arrival probabilities to find a route that provides a predefined level of QoC while minimizing network load. Third, we address the problem of active replication for controllers in order to increase the reliability of NCS subject to crash failures and message loss. While existing replication schemes for real-time systems focus only on ensuring that no conflicting values are sent to actuators, we develop stronger consistency concepts that provide replication transparency for control systems. We present a corresponding replication management protocol that achieves high availability and low latency at low message cost, and evaluate it using physical experiments.
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