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 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 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 Orthogonale Dünngitter-Teilraumzerlegungen(2018) Schreiber, ConstantinIn der Simulation treten Häufg hochdimensionale partielle Differentialgleichungen auf. Das Lösen dieser wird für volle Gitter sehr schnell zu teuer. In dieser Arbeit wird ein Verfahren für das Lösen partieller Differentialgleichungen mit Hilfe von Dünnen Gittern, welche für mehrdimensionale Probleme besser skalieren, sowie dessen Implementierung in das Programmpaket SG++ vorgestellt. Durch Funktionsdarstellung in einem Erzeugendensystem wird die Verwendung einer L2-orthogonalen Teilraumzerlegung ermöglicht. Projektionsoperatoren ersetzen hierbei die explizite Transformation in eine Prewavelet-Basis. Diese Zerlegung erlaubt das Lumping der Steifgkeitsmatrix, also das Weglassen von großen Blöcken der Matrix. Hiermit wird ein Algorithmus zur Matrixmultiplikation, welcher dem von Schwab und Todor ähnelt implementiert. Dieser wird in einem konjugierten Gradienten-Verfahren verwendet und auch auf krummberandete Gebieten angewendet. Des Weiteren wird die Teilraumzerlegung durch L2-Projektion mit anderen Zerlegungen in Bezug auf Laufzeit und Fehlerentwicklung verglichen.Item Open Access Speech interface for human and robot collaboration(2018) Kashif, Moin UddinIn the past, robots and machines were mostly designed to perform specific tasks without much human interaction needed. Nowadays with the advancements in technology, intelligent robots can be designed which can perform multiple tasks, interact with the surrounding environment, assist and give valuable suggestions to humans etc. so an efficient and natural mode of communication is required for this human-robot interaction. In this thesis, we proposed an architecture to develop a speech interface for human-robot interaction. The speech interface is used to give voice commands to the robot, PR2, in order to perform 5 tasks which are designed to test the performance of the speech interface. The tasks are sorting, shaping, stacking, building and balancing of 6 objects on table-top which are designed and ordered by the level of difficulty. First two tasks are comparatively easier as the user doesn't have to follow any order to finish them, next two tasks require to follow the order and in the last task, the stack of objects must be balanced in order to finish it. The speech interface receives voice commands from the user, convert them into text, maps to the corresponding command and send to the task manager to perform the operation. After that, it processes the received command, takes the appropriate decision based on the current status of the task and available actions and sends the command to the PR2 to perform the operation. Additionally, we have designed a feedback mechanism where PR2 sends back the feedback to the task manager which is delivered back to the speech manager so that it can be converted into an audio signal and play for the user. Furthermore, the system uses a TCP connection for the exchange of data and information between the speech manager and the task manager. The speech interface is also compared with other modalities such as text input and graphical user interface with the same tasks and we have also conducted user study to evaluate the system performance. The results show that the participants prefer speech interface as it feels more natural.Item Open Access API diversity for microservices in the domain of connected vehicles(2018) Gajek, FabianWeb services in the domain of connected vehicles are subject to various requirements including high availability and large workloads. Microservices are an architectural style which can fulfill those requirements by fostering the independence and decoupling of software components as reusable services. To achieve this independence, microservices have to implement all aspects of providing the services themselves, including different API technologies for heterogeneous consumers and supporting features like authentication. In this work, we examine the use of a service proxy that externalizes these concerns into a sidecar that provides multiple APIs and common service functionality in a platform-independent manner. We look at how different kinds of API styles and technologies solve selected classes of problems and how we can translate between API technologies. We design and implement a framework for building gateways that enables the creation and composition of reusable components, in the fashion of Lego bricks, to maximize flexibility, while reducing the effort for building gateway components. We design and implement selected components of common and reusable API functionality enabling us to build a reference setup with a service proxy as a sidecar using our framework. Finally, we evaluate the proposed solution to identify benefits and drawbacks of the approach of using our framework as a service proxy. We conclude that the examined approach provides benefits for the development of many polyglot microservices, but splitting one service into two components adds additional complexity that has to be managed.Item Open Access Deep reinforcement learning for high-level behavior decision making(2018) Dittrich, FlorianAs the vision of fully autonomous vehicles potentially introduces significant benefits for our society, this work investigates approaches for sequential decision making for high-level actions in highway scenarios. These scenarios are modeled using an markov decision process (MDP) and consider deep reinforcement learning to solve it. Our approach, based on deep Q-networks (DQNs), is able to fully avoid collisions and learns a policy that results in comfortable trajectories compared to baseline policies we developed. One of the main challenges for reinforcement learning are sparse rewards, which we aim to overcome employing reward shaping. Additionally, the necessity of multiple layers of non-liniearities in the DQN algorithm is empirically evaluated using our scenarios. The results support the usage of multiple levels of non-linearities, as a linear variant of the DQN is not capable of learning effective policies in our experiments. Due to a weight initialization with behavioral cloning, an acceleration of the learning procedure is achieved.Item Open Access Octo-Tiger: Binary star systems with HPX on Nvidia P100(2018) Daiß, GregorStellar mergers between two suns are a significant field of study since they can lead to astrophysical phenomena such as type Ia supernovae. Octo-Tiger simulates merging stars by computing self-gravitating astrophysical fluids. By relying on the high-level library HPX for parallelization and Vc for vectorization, Octo-Tiger combines high performance with ease of development. For accurate simulations, Octo-Tiger requires massive computational resources. To improve hardware utilization, we introduce a stencil-based approach for computing the gravitational field using the fast multipole method. This approach was tailored for machines with wide vector units like Intel's Knights Landing or modern GPUs. Our implementation targets AVX512 enabled processors and is backward compatible with older vector extensions (AVX2, AVX, SSE). We further extended our approach to make use of available NVIDIA GPUs as coprocessors. We developed a tasking system that processes critical compute kernels on the GPU or the processor, depending on their utilization. Using the stencil-based fast multipole method, we gain a consistent speedup on all platforms, over the classical interaction-list-based implementation. On an Intel Xeon Phi 7210, we achieve a speedup of 1.9x. On a heterogeneous node with an Intel Xeon E5-2690 v3, we can obtain a speedup of 1.46x by adding an NVIDIA P100 GPU.Item Open Access Master Annotator: enhancing distant supervision through visual interfaces(2018) Luo, ZherenThe rise of social networking sites and applications such as Twitter, Facebook, Instagram, Weibo in couple years provides new ways of information sharing on the Internet. Twitter is one of the most popular micro-blogging services. Recently, tweet classification has received much attention. In tweet classification, there is a demand for massive labeled training examples. However, the labeling cost can be high since the resources regarding available time, domain expert annotators are often limited. Distant supervision is a feasible solution but has some shortages such as noisy data, skewed annotations, and the challenge to generate negative training examples. This thesis tries to overcome these difficulties and shortages by combining visual analytics. Based on the distant supervision theorem, information such as keywords, hashtags, user_mentions, and URLs existing in tweets can be used as annotators to generate training examples. Through an interactive visual interface, the generated training examples can be checked and modified, which enhances the distant supervision. An iterative analytical loop is established using the interactive visual interface and the enhanced distant supervision. It allows users to make deep exploration of tweet data. Users can get tweets according to their needs, find potential topics, label or relabel tweets, train classifiers. A software is implemented to achieve the analytical loop. A case study is conducted to demonstrate the performance and usage of the approach and the software.Item Open Access Multi-criteria bicycle routing(2018) Barth, FlorianThis thesis considers the problem of finding alternative routes by weighting multiple metrics. This approach has the benefit that it always yields a Pareto optimal path. These routes have to be then checked against a similarity measure to ensure that they are different enough for a user to consider them helpful. The Splitting Algorithm designed in this thesis uses a geometric intuition to explore the possible weightings and find good alternative routes heuristically. To achieve fast query times although many routes need to be calculated and compared, the algorithm builds upon the multi-criteria version of the contraction hierarchies speed-up technique. In an implementation for cyclists with three metrics, the algorithm found up to thirteen routes that share less than 50% of their length and four or five routes in half the cases.Item Open Access Policy4TDLIoT - Policys für die Topic Description Language(2018) Lehmann, SimonIm Paradigma Internet of Things (IoT), im deutschen Internet der Dinge, werden heterogene Geräte über das Internet vernetzt. Diese Geräte enthalten Sensoren und Aktuatoren, um Daten aus ihrer Umgebung zu erfassen und in die Umwelt einzugreifen. Dies ermöglicht die Umsetzung von innovativen Systemen wie Smart Home, Smart City oder Smart Factory. Die Heterogenität der Geräte erschwert es Standardisierungen und einheitliche Metriken zu definieren. Gleichzeitig steigt die Anzahl an vernetzten Geräten und dieses Wachstum wird sich in den kommenden Jahren fortsetzen. Für diese beiden Probleme wurde die Topic Description Language für die IoT (TDLIoT) entwickelt. Topics sind Schnittstellen zwischen Sensoren oder Aktuatoren und Endverbraucher. Sie publizieren die Daten, anhand verschiedener Protokolle (z.B. MQTT, REST) an alle Verbraucher die sich bei ihnen registriert haben. Die TDLIoT ermöglicht es ein Topic mit mehreren Attributen zu beschreiben. Zudem bietet sie ein Katalog an, in dem alle Topic Beschreibungen gesammelt sind und durch Such- und Filterfunktionalitäten gefunden werden können. Die bisherige TDLIoT beinhaltet lediglich die Beschreibung von funktionalen Anforderungen, wie z.B. Datenformat, Datentyp, Zugriffspfad oder Standort. Das Ziel dieser Arbeit ist es der TDLIoT nichtfunktionale Anforderungen hinzuzufügen, um Topics genauer beschreiben zu können und dem Katalog weitere Filtermöglichkeiten zu bieten. Der Ansatz orientiert sich an WS-Policys der WSDL. Des Weiteren wird die Struktur der TDLIoT durch neue Komponenten und Rollen erweitert. Diese ermöglichen es Policys übersichtlich für die Erstellung eines Topics darzustellen und bieten eine Kontrollstruktur die neue Policys anhand der Anforderungen der TDLIoT überprüft, damit eine hohe Qualität der Policys gewährleistet werden kann. Ein Anbieter eines Topics kann beliebig viele Policys uneingeschränkt definieren. Dadurch können falsche Angaben in den Policys definiert werden. Um dem Nutzer eines Topics eine Sicherheit über den Wahrheitsgehalt eines Topics zu liefern wird es Verifikationen zur Überprüfung der Policy geben. Die Ergebnisse dieser Verifikationen geben jedem Nutzer Rückmeldung, ob das Topic die Angaben aus seinen Policys einhält.