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 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 Feasibility analysis of using Model Predictive Control in Demand-Side Management of residential building(2020) Ramachandran Selvaraj, Sri VishnuThe energy systems are becoming smart recently with an increase in communication capabilities between producer, distributor and consumer. Also, many distributed renewable energy producers both in large and domestic scale are adding to the system day by day. Executing Smart Demand-Side Management (DSM) programs can help in providing financial benefits and stability of the energy system without compromising the comfort of end-users. Model Predictive Control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. Due to its ability to predict future events and generate optimal control, it is widely used in process industries since the 1980s and in recent years it is introduced in power systems. This motivates to study the economic feasibility of using MPC in executing DSM for Residential building, to optimize the power consumption costs and stability of the energy system in the presence of local renewable energy sources (E.g., PV system). The main contribution of this thesis work is to measure the economic benefit of using MPC on DSM of household electricity consumption. A detailed study of modeling the demand side, i.e the appliances of a smart home, along with the domestic energy generators is done in the initial part. Apart from the physical properties of the renewable energy generators, the influence of external factors like weather, dynamic-pricing of electricity and changing user preference is also considered in the model. This formulated model is used to perform simulation of the residential building to generate an optimized energy consumption schedule and calculate the resulting economic benefits. The periodic changes in weather forecast and dynamic-prices are fed into the simulation to improve the prediction accuracy of the system. Lastly, the model is evaluated on a physical implementation to analyze its performance. There are multiple findings as part of the result of this thesis, like the economic benefit of using such a system will encourage many users to participate in Demand response programs, this in turn will help in the reduction of pollution originating from non-renewable energy generators.Item Open Access Prompt-based personality profiling : filtering social media posts using reinforcement learning(2024) Hofmann, JanAuthor profiling is the task of inferring characteristics about individuals by analyzing content they share. To date, systems that perform this task automatically, predominantly use supervised machine learning approaches and borrow from advances in the field of natural language understanding. However, while for many language understanding tasks immense progress has been made in recent years (e.g. by using pre-train then fine-tune paradigm), such progress most often does not transfer to automated profiling systems directly. One reason for this is that author profiling is inherently different from typical text inference tasks due to the possibly large amounts of content associated with an author. Therefore, this work proposes a new method for profiling that tries to select the most relevant parts of content shared by an author before inferring a characteristic. Here, instead of relying on ground-truth labels, this work uses the feedback from the zero-shot capabilities of a large language model to learn such a selection model via reinforcement learning, and evaluates this approach for personality profiling in social media. In experiments predicting big five personality traits, this work finds that prediction quality of such a system is comparable yet slightly worse to using all content associated to a profile in a zero-shot setting, while prediction time is reduced significantly due to the limited amount of content used for inferring personality in the proposed method. In addition, this work finds that simply selecting content arbitrarily leads to performance degradation for most traits, and therefore, this work concludes that, to some extent, the proposed method is able to distinguish between relevant and irrelevant content. Further, this work compares the proposed approach to existing supervised approaches and finds that such methods outperform the proposed method substantially. Still, since the ability of the proposed system to distinguish between relevant and irrelevant content of authors is closely tied to the capabilities of large language models, it can be expected that, with advances of such models, prediction quality of the proposed approach will increase in the future.Item Open Access Individual characteristics of successful coding challengers(2017) Wyrich, MarvinAssessing a software engineer's problem-solving ability to algorithmic programming tasks has been an essential part of technical interviews at some of the most successful technology companies for several years now. Despite the adoption of coding challenges among these companies, we do not know what influences the performance of different software engineers in solving such coding challenges. We conducted an exploratory study with software engineering students to find hypothesis on what individual characteristics make a good coding challenge solver. Our findings show that the better coding challengers have also better exam grades and more programming experience. Furthermore, conscientious as well as sad software engineers performed worse in our study.Item Open Access Development of an infrastructure for creating a behavioral model of hardware of measurable parameters in dependency of executed software(2021) Schwachhofer, DenisSystem-Level Test (SLT) gains traction not only in the industry but as of recently also in academia. It is used to detect manufacturing defects not caught by previous test steps. The idea behind SLT is to embed the Design Under Test (DUT) in an environment and running software on it that corresponds to its end-user application. But even though it is increasingly used in manufacturing since a decade there are still many open challenges to solve. For example, there is no coverage metric for SLT. Also, tests are not automatically generated but manually composed using existing operating systems and programs. This master thesis introduces the foundation for the AutoGen project, that will tackle the aforementioned challenges in the future. This foundation contains a platform for experiments and a workflow to generate Systems-on-Chip (SoCs). A case study is conducted to show an example on how on-chip sensors can be used in SLT applications to replace missing detailed technology-information. For the case study a “power devil” application has been developed that aims to keep the temperature of the Field Programmable Gate Array (FPGA) it runs on in a target range. The study shows an example on how software and parameters influence the extra-functional behavior of hardware.Item Open Access Economic feasibility analysis of vehicle-to-grid service from an EV owner's perspective in the german electricity market(2020) Malya, Prasad PrakashThe increasing number of Electrical Vehicles (EV) has led to a tremendous amount of inac- cessible electric energy stored in the EV batteries. Vehicle-to-grid (V2G) services can utilize this energy to profit the EV owners’ and stabilize the grid during faults and fluctuations. This thesis presents a novel way of estimating the profitability of V2G from the EV owner’s perspective. The main contribution of this thesis is the formulation of a profit model that includes the EV battery degradation due to V2G. The work done so far considers fixed battery degradation cost, whereas in this work, an online battery degradation model is used. This model takes into account the parameters that represent real-life scenarios resulting in more accurate battery degradation estimation. The V2G profit model uses the electricity price signal from the German energy market for the year 2019 and estimates the annual profit. The first part of the thesis calculates the profitability of V2G, where EV can participate freely in energy arbitrage. This analysis explores the range of profit when EV participates in V2G purely based on the EV owner’s discretion. A sensitivity analysis is done with respect to battery capacity, battery efficiency, and driving distance. The second part of the thesis evaluates the profitability of EV participating in the German energy market’s frequency regulation ancillary service.=. The analysis compares the profitability of EV participating in primary, secondary, and tertiary frequency regulation services. The results of this thesis provide several findings, the potential profit from V2G services should encourage EV owners’ to participate in the V2G services. Additionally, participating in V2G service can extend the life of the battery. However, this depends on the battery technology and battery usage during V2G services. Ancillary services provide higher potential profit compared to energy arbitrage because of the high remuneration scheme. The ancillary services with both capacity and energy payment result in higher profit compared to ancillary services with only capacity payment.Item Open Access Enhancing HTN planning with deep reinforcement learning for method selection(2025) Bahrami, SepidehAutomated planning is a central area within Artificial Intelligence (AI), enabling intelligent behavior in domains such as cloud computing, autonomous systems, context-aware activity recognition, and smart environments. Hierarchical Task Network (HTN) planning, which decomposes complex tasks into simpler subtasks using predefined methods, has proven effective in such structured domains. However, its performance is often constrained by static method selection strategies that lack adaptability to varying planning contexts. To address this limitation, this thesis proposes a neuro-symbolic framework that integrates HTN planning with Deep Reinforcement Learning (DRL), combining the strengths of symbolic reasoning and data-driven learning. Among the available DRL algorithms, Deep Q-Learning (DQL) is particularly suitable due to its off-policy nature, batch-efficient learning, and robust generalization across symbolic planning states. These characteristics align well with deterministic and hierarchical planners, enabling offline learning from curated datasets without requiring interactive exploration. The proposed integration introduces a learning-based decision layer that improves adaptability while preserving the reproducibility and determinism of the underlying planner. The effectiveness of this approach is demonstrated through a comprehensive evaluation across planning efficiency, memory consumption, and plan quality. Results highlight the potential of reinforcement learning to enhance classical HTN systems and support intelligent decision-making in complex, structured environments.Item Open Access Concept and implementation of digital beacons(2015) Chughtai, Muhammad BilalItem Open Access Die Rolle von Verschränkung im Quantencomputing : Speedup und Konsensusprotokolle(2019) Salm, Marie OliviaIn Zukunft werden Quantencomputer Probleme womöglich effizienter lösen als klassische Computer. Dies wäre eine bahnbrechende Errungenschaft und erweckt daher große Hoffnungen bei Forschung und Wirtschaft. Noch befindet sich das Gebiet der Quanteninformatik und des Quantencomputings vor allem in der Grundlagenforschung, und die Entwicklung eines leistungsfähigen Quantencomputers liegt noch in weiter Ferne. Dennoch werden bereits heute Quantenalgorithmen entwickelt, die eine Überlegenheit gegenüber klassischen Algorithmen aufzeigen könnten. So könnten verteilte Systeme von den quantenmechanischen Eigenschaften unter anderem durch Kommunikationsersparnisse profitieren. In dieser Arbeit wurde untersucht, ob das Phänomen der Verschränkung für den möglichen Speedup gegenüber klassischen Computer verantwortlich ist. Dazu wurden Annahmen wissenschaftlicher Arbeiten zusammengefasst. Des Weiteren wurde das Konsensusprotokoll Paxos mit quantenmechanischen Konzepten erweitert. Für eine der Erweiterungen wurde der verschränkte W-Zustand für die Wahl eines Proposers eingesetzt. In der zweiten Erweiterung wurde für die Bestimmung einer Rundennummer Superposition verwendet. Zudem wurde das 2-Phasen-Commit-Protokoll in unterschiedlichen Varianten mit dem GHZ-Zustand erweitert. Auch für das 3-Phasen-Commit-Protokoll wurde der W-Zustand für die Wahl eines Koordinators verwendet. Die Ergebnisse zeigen unter anderem, dass eine Reduzierung des Kommunikationsaufwands bei Paxos und dem 3-Phasen-Commit-Protkoll möglich ist. Es zeigt sich auch, dass eine Deblockierung des erweiterten 2-Phasen-Commit-Protokolls in der behandelten Weise nicht möglich ist.