05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Optimization of diffusive load-balancing for short-range molecular dynamics(2020) Hauser, SimonIn recent years, multi-core processors have become more and more important for manufacturers, which means that developers now have to think more about how to distribute a single application sensibly over several processes. This is where load balancing comes in, allowing us to move load from an overloaded process to an underloaded process. One way of load balancing is diffusive load balancing, which is a method of moving load in the local neighborhood and therefore no global communication is needed. The advantage of this is that processes that have completed the local communication and thus the load-balancing process can continue with the next calculations. This form of load balancing is found in librepa, a library that deals with the balancing of linked-cell grids and can be used in the simulation software ESPResSo. In the course of this thesis the library has been extended with the First and Second Order Diffusion. Furthermore, a feature was added that allows to keep the initial structure of the grid constant, which means that the neighborhood of each process does not change. This feature is necessary for the Second Order Diffusion. A comparison between the methods shows that both First and Second Order Diffusion distribute the load better in the system than librepa's default and prior to this work only diffusive variant. Furthermore, we show that there is no significant overhead in using the Preserving Structure Diffusion. With the use of flow iteration the imbalance values of First and Second Order Diffusion can be improved even further.Item Open Access Webanwendung für Multiphysik-Simulationen mit opendihu(2020) Tompert, MatthiasOpendihu ist ein Software-Framework zum Lösen von Multi-Physik-Problemen mit Hilfe der Finiten-Elemente-Methode. Die Anwendungen von Opendihu sind hauptsächlich im Bereich der Skelett-Muskel-Simulationen. Das Erstellen einer Simulation in Opendihu erfolgt über eine C++-Datei, in welcher verschachtelte Löserstrukturen angegeben werden und über eine Python-Datei in welcher die Parameter der verwendeten Löser konfiguriert werden. Das Bearbeiten vorhandener Simulationen und das Erstellen neuer Simulationen mit Hilfe dieser Schnittstelle erfordern gute Kenntnisse über den Sourcecode, beziehungsweise die Struktur von Opendihu. Daher wäre es Sinnvoll Opendihu um eine Nutzerfreundlichere und auch für Einsteiger geeignete Nutzerschnittstelle zu erweitern. Im Rahmen dieser Arbeit habe Ich daher eine grafische Benutzeroberfläche für Opendihu implementiert, welche die Löserstruktur und die Parameter der einzelnen Löser einer Simulation visualisiert. Außerdem ist es mit der Anwendung möglich vorhandene Simulationen zu ändern und neue Simulationen mit Hilfe eines Baukastensystems zu erstellen. Diese Bachelorarbeit erläutert den Aufbau dieser Anwendung und erforscht mit Hilfe einer Nutzerstudie ob die entstandene Benutzerschnittstelle einen Mehrwert gegenüber der bereits vorhandenen Schnittstelle bietet. Das Bearbeiten und Erstellen neuer Simulationen mit Hilfe der Anwendung wurde von den Teilnehmern der Studie im Durchschnitt als einfacher empfunden, als das Bearbeiten und Erstellen neuer Simulationen mit Hilfe der bereits vorhandenen Schnittstelle. Die entstandene Anwendung bietet also einen Mehrwert beim Bearbeiten und Erstellen von Opendihu-Simulationen. Besonders beim Erstellen neuer Simulationen wurde das Baukastensystem als hilfreich bewertet.Item Open Access Efficient sampling of transition constraints for motion planning under sliding contacts(2020) Khoury, Marie ThereseIn contact-based motion planning we consider for humanoid and multiped robots problems like going up a staircase, walking over an uneven surface or climbing a steep hill. Solving such tasks requires finding sequences of fixed and sliding contacts and planning the transition from one contact in the environment to another. However, most existing algorithms do not take sliding contacts into account for navigation problems or consider them only for manipulation scenarios. We propose an approach to contact-based planning that uses sliding contacts and exploits contact transitions. Such transitions are elementary operations required for whole contact sequences. To model sliding contacts, we develop a sliding contact constraint that permits the robot to slide on an object’s surface. To exploit contact transitions, we utilize three constraint modes to enable passage: contact with a start surface, no contact and contact with a goal surface. We develop a sampler that samples these transition modes uniformly. In this thesis we focus on the motion of one robot link’s end from an initial contact point toward a designated goal surface while the other end of the robot remains in sliding contact with the initial surface. Our method is evaluated by testing it on manipulator arms of two, three and seven degrees of freedom with different objects and various sampling-based planning algorithms. From the considered manipulator arm, it would be possible to transfer our concept to more complex robots and scenarios and extend it to a whole sequence of contacts.Item Open Access An analytics framework for the IoT platform MBP(2020) Kumar, AbishekThe emergence of IoT has introduced a huge amount of applications that generate massive amounts of data at a high rate. This data stream needs intelligent data processing and analysis. The evolution of Smart cities and Smart industries has resulted into an ocean of data from millions of sensors and devices. Surveillance systems, telecommunication systems, smart devices, and smart cars are some examples of such systems. However, this data itself doesn’t provide any information unless it is analysed. This results into a need of analytics tools and frameworks which can efficiently analyse this data and provide with useful information. Analytics is all about inspection, transformation and modelling of data to achieve information that further suggests and assists in decision making. In a world of IoT, analytics has a crucial role to play to improve life and better manage the infrastructure in a secure, sustainable and cost effective manner. The smart sensor network serves as the base for IoT. In this context, one of the major tasks is to develop advanced analytics frameworks for the interpretation of data provided by the sensors. MBP is a platform for managing IoT environments. Sensors and devices can be registered to this platform and the status of sensors can be viewed and modified from the platform. This platform will be used to collect data from the sensors and devices connected to the platform. There are two types of mining that can be performed on raw data, one technique analyses the data on the fly as it is received (Data Stream Mining) and the other can be performed on demand on the data collected for a longer period of time (Batch Processing). Both types of analysis has its own advantages. Lambda architecture is a data analytics architecture which allows us to perform both stream analysis and batch processing on the same data. This architecture defines some practical and well versed principles of handling big data. The pattern allows us to deal with both real time and historical data, but the analysis is performed separately and does not affect each other. In this thesis, we will create an analytics framework for the MBP IoT platform based on the lambda architecture.Item Open Access Data-efficient and safe learning with Gaussian processes(2020) Schreiter, Jens; Toussaint, Marc (Prof. Dr. rer. nat.)Data-based modeling techniques enjoy increasing popularity in many areas of science and technology where traditional approaches are limited regarding accuracy and efficiency. When employing machine learning methods to generate models of dynamic system, it is necessary to consider two important issues. Firstly, the data-sampling process should induce an informative and representative set of points to enable high generalization accuracy of the learned models. Secondly, the algorithmic part for efficient model building is essential for applicability, usability, and the quality of the learned predictive model. This thesis deals with both of these aspects for supervised learning problems, where the interaction between them is exploited to realize an exact and powerful modeling. After introducing the non-parametric Bayesian modeling approach with Gaussian processes and basics for transient modeling tasks in the next chapter, we dedicate ourselves to extensions of this probabilistic technique to relevant practical requirements in the subsequent chapter. This chapter provides an overview on existing sparse Gaussian process approximations and propose some novel work to increase efficiency and model selection on particularly large training data sets. For example, our sparse modeling approach enables real-time capable prediction performance and efficient learning with low memory requirements. A comprehensive comparison on various real-world problems confirms the proposed contributions and shows a variety of modeling tasks, where approximate Gaussian processes can be successfully applied. Further experiments provide more insight about the whole learning process, and thus a profound understanding of the presented work. In the fourth chapter, we focus on active learning schemes for safe and information-optimal generation of meaningful data sets. In addition to the exploration behavior of the active learner, the safety issue is considered in our work, since interacting with real systems should not result in damages or even completely destroy it. Here we propose a new model-based active learning framework to solve both tasks simultaneously. As basis for the data-sampling process we employ the presented Gaussian process techniques. Furthermore, we distinguish between static and transient experimental design strategies. Both problems are separately considered in this chapter. Nevertheless, the requirements for each active learning problem are the same. This subdivision into a static and transient setting allows a more problem-specific perspective on the two cases, and thus enables the creation of specially adapted active learning algorithms. Our novel approaches are then investigated for different applications, where a favorable trade-off between safety and exploration is always realized. Theoretical results maintain these evaluations and provide respectable knowledge about the derived model-based active learning schemes. For example, an upper bound for the probability of failure of the presented active learning methods is derived under reasonable assumptions. Finally, the thesis concludes with a summary of the investigated machine learning problems and motivate some future research directions.Item Open Access Adaptives Testwerkzeug für IoT Applikationen(2020) Seber, KatjaDurch das Internet der Dinge (IoT) wird die Möglichkeit geboten, mit Hilfe von IoT- Applikationen Verbesserungen und Vorteile in diversen Anwendungsbereichen zu schaffen. Dabei werden mit Sensoren und Aktuatoren ausgestattete IoT-Geräte miteinander verknüpft, um die sich ständig ändernde Umgebung wahrzunehmen und auf diese möglichst ohne menschliches Eingreifen reagieren zu können. Die Stabilität und die korrekte Funktionsweise einer solchen Anwendung stellen dabei wichtige Anforderungen dar, welche besonders durch Ausnahmefälle (z.B. Ausreißer bei Sensorwerten) gefährdet werden. Aus diesem Grund sind Software-Tests bei der Entwicklung von IoT-Applikationen unabdinglich. Ziel dieser Bachelorarbeit ist es daher, ein weitgehend automatisiertes Testwerkzeug bereitzustellen, um stabile und korrekt arbeitende Anwendungen zu schaffen. Um effektive Tests von verschiedenen Testfällen zu ermöglichen, sollte die Simulation von Sensoren und Aktuatoren ermöglicht werden. Zur Integration der Simulatoren und des Testwerkzeugs lag die IoT-Plattform Multi-purpose Binding and Provisioning Platform (MBP) zugrunde. Innerhalb der Literatur konnten keine Quellen gefunden werden, welche konkrete Lösungen, beziehungsweise bereits bestehende Tools zur Erfüllung der Ziele zur Verfügung stellen. Daher wurden das Testwerkzeug sowie die Simulatoren eigenständig innerhalb dieser Arbeit entwickelt. Dadurch können mit Hilfe dieser verschiedene Testfälle einer IoT-Applikation effektiv geprüft werden. Anhand der Ergebnisse eines Tests können bei detektiertem Fehlverhalten erforderliche Maßnahmen getroffen werden. Dadurch sind die Anforderungen einer stabilen und korrekten Funktionsweise einer IoT-Applikation erfüllbar.Item Open Access Models for internet of things environments : a survey(2020) Franco da Silva, Ana Cristina; Hirmer, PascalToday, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, e.g., monitoring and self-organization. To achieve context-awareness, a large amount of environment models have been developed for the IoT that contain information about the devices of an environment, their attached sensors and actuators, as well as their interconnection. However, these models highly differ in their content, the format being used, for example ontologies or relational models, and the domain to which they are applied. In this article, we present a comparative survey of models for IoT environments. By doing so, we describe and compare the selected models based on a deep literature research. The result is a comparative overview of existing state-of-the-art IoT environment models.Item Open Access Region proposal network for simple objects in grasping experiments(2020) Bauer, RubenParticularly in applied robotics, the grasping of objects is a major field which comes with various difficulties. Multiple objects with simple or complex shapes as well as different colors can be scattered on a surface in random positions and orientations. However, with the knowledge about the correct object positions, a robot has high chance of grasping them. Object detection systems can determine bounding boxes of objects in images, which can help to calculate the correct object positions. Current state-of-the-art object detection systems such as the popular Faster R-CNN and the Mask R-CNN, often use multi-stage architectures. Both models utilize a region proposal network to obtain regions which are likely to contain objects. This thesis introduces and evaluates multiple architecture variations of single-stage and two-stage models. These variations include a region proposal network, yet in the setting of grasping experiments. Usually, the training of these models is done in a supervised manner which requires lots of data with ground truth information. Generating this kind of data in a real world environment is expensive, yet it is cost-efficient to generate the same kind of data in a simulated environment. Therefore, this thesis introduces a framework to generate artificial data in a simulated grasping experiment environment. This framework implements several domain randomization techniques in order to randomize this simulation environment. The training data contains only artificial images with objects of simple geometry. The results have shown that models, which were trained only on these artificial images, can still generalize well to images of a real environment. Furthermore, the generalization to images which contain objects of complex geometry is equally possible. This thesis performs ablation studies on the employed domain randomization techniques which reveal both degradation and improvement of different techniques. Benchmarks on the model variations show that a significantly faster inference is possible compared to the originally Faster R-CNN and Mask R-CNN, while still achieving pleasant prediction results. This was made possible by using different configurations for the region proposal network, and by introducing faster feature extraction backbone architectures.Item Open Access Umsetzung anwendungsspezifischer ETL-Prozesse im Data Lake(2020) Winckler, MagdalenaIn der heutigen Zeit entstehen tagtäglich immer mehr Daten. Um diese nutzen zu können, müssen sie gespeichert, verarbeitet und analysiert werden. Da dies bei immer größer werdenden Datensätzen, welche gleichzeitig heterogen sein können, viele neue Anforderungen mit sich bringt, entstehen auch immer wieder neue Technologien. So wird es immer schwerer relevante Daten herauszufiltern oder diese zu bereinigen. Eine dieser Technologien ist der Data Lake. Ein Data Lake ist ein System in dem jegliche Arten von Daten gespeichert und zu einem späteren Zeitpunkt bei Verwendung bereitgestellt werden können. Oft wird jedoch ein Data Lake mit einem Hadoop-Cluster gleichgesetzt. Um einen Data Lake effizienter zu konzeptionieren, wird in der Literatur deshalb oft darauf hingewiesen, dass auch auf andere Technologien innerhalb eines Data Lakes zurückgegriffen werden soll. Gleichzeitig kann ein Data Lake in unterschiedliche Zonen aufgeteilt werden, um diesen effizienter zu nutzen. Die Aufteilung eines Data Lakes in Zonen, wird allgemein als Zonenmodell bezeichnet. Bisher gibt es jedoch nur wenige wissenschaftliche Arbeiten, welche sich sowohl mit der Konzeptionierung als auch mit der Implementierung eines Data Lakes mit mehreren Technologien beschäftigt. In dieser Arbeit wird ein Konzept eines Data Lakes mit mehreren Technologien entworfen und dieses prototypisch umgesetzt. Um dies zu erreichen werden unterschiedliche Zonenmodelle und bereits in der Literatur vorhandene Anwendungen dieser auf einen Data Lake betrachtet. Anschließend wird ein Beispieldatensatz vorgestellt, anhand dessen unterschiedliche Anwendungsfälle entworfen werden. Um den Data Lake mit anderen Technologien zu erweitern, werden zudem unterschiedliche Datenbankarten vorgestellt. Darauf folgt in einer prototypischen Umsetzung die Übertragung der Daten von einem Hadoop-Cluster in die unterschiedlichen Datenbanken. Abschließend folgt eine Evaluation und eine Zusammenfassung der Arbeit.Item Open Access About the design changes required for enabling ECM systems to exploit cloud technology(2020) Shao, GangSince the late 1980s, Enterprise Content Management Systems (ECM systems) have been used to store, manage, distribute all kinds of documents, media content, and information in enterprises. ECM systems also enable enterprises to integrate their business processes with contents, employing corporate information lifecycle and governance as well as automation of contents processing. The ever-changing business models and increasing demands have pushed ECM systems to evolve into a very active content repository with expectations such as high availability, high scalability, high customizability. These expectations soon became a costly financial burden for enterprises. The on-going hype around cloud computing has raised attention with its claims on improved manageability, less maintenance, and cost-effectiveness. Embracing the cloud might be a good solution for the next high-performance ECM system at an affordable price. To achieve such a goal, the designs of ECM systems must be changed before deployment into the cloud. Thus, this thesis aims to analyze the architecture design of legacy ECM systems, determine its shortcomings, and propose design changes required for embracing cloud technologies. The main proposal to design changes are i) decomposing an ECM system to its constituent components, ii) containerizing those components and create standard images, iii) decoupling the physical link between the data storage device from the applications container by utilizing docker volumes in dedicated persistent data containers instead, iv) utilizing software-defined network infrastructure where possible. These design changes then were tested with a proof-of-concept prototype, where an ECM product was successfully deployed and tested using Docker in a cloud environment backed by OpenStack.