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 Classifying physical exercises and counting repetitions using three-dimensional pose estimation(2023) Wallmann, JonasResistance training is known to increase physical and mental health but requires a lot of knowledge and experience to be done effectively and safely. Personal trainers and physiotherapists provide their knowledge to athletes but their profession requires a lot of learning and experience, thus making their services often not affordable to the general public. Automating certain aspects of their work will make their services more available to the general population and therefore lead to more safe and more effective athletes. The first steps of automating personal training lie in observing a subject train and understanding their performed workout. This provides the basics for future work of automating providing feedback on exercise execution and improving their training regimes. In order to do so, we developed a proof-of-concept program, that uses a two-dimensional camera video as an input to classify what exercise a user performs and automatically counts the number of performed repetitions, in real-time. It should work without imposing requirements in the camera perspective or needing to know what exercise will be performed in advance. This is achieved by using a three-dimensional pose estimation model and defining a rule-based algorithm, that considers the position and angle of joints that characterize the performed exercises We evaluate our proof-of-concept program using videos of subjects performing squats and push-ups in order to understand the accuracy in a real-world scenario. Our program achieved an overall accuracy of 95.57% for the squats and 93.69% for the push-up evaluation.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 A systematic mapping study on development and use of AI planning tools(2021) Philippsohn, RobertArtificial intelligence (AI) planning is a big area in the AI field with many needs and special problems. Therefore, it needs tools to suit these special problems and request, as well as for trends in the AI planning community. Since 1971 there has been an influx of many tools that assist insolving planning problems and making plans. To give a better overview of the available landscape of AI planning tools this systematic mapping study was conducted and try also to shows what software engineering principles are used in creating the tools. We also try to depict in which industry domains the AI planning tools are used and how many papers mention the tools being used in the industry. In the end, we conclude that there are at least 106 different tools out there, with only a fraction being used in the industry. While only a small part of the tools are talked about being used in the industry, this small part is covering a wide array of industry domains.Item Open Access Locking-enabled security analysis of cryptographic circuits(2024) Upadhyaya, Devanshi; Gay, Maël; Polian, IliaHardware implementations of cryptographic primitives require protection against physical attacks and supply chain threats. This raises the question of secure composability of different attack countermeasures, i.e., whether protecting a circuit against one threat can make it more vulnerable against a different threat. In this article, we study the consequences of applying logic locking, a popular design-for-trust solution against intellectual property piracy and overproduction, to cryptographic circuits. We show that the ability to unlock the circuit incorrectly gives the adversary new powerful attack options. We introduce LEDFA (locking-enabled differential fault analysis) and demonstrate for several ciphers and families of locking schemes that fault attacks become possible (or consistently easier) for incorrectly unlocked circuits. In several cases, logic locking has made circuit implementations prone to classical algebraic attacks with no fault injection needed altogether. We refer to this “zero-fault” version of LEDFA by the term LEDA, investigate its success factors in-depth and propose a countermeasure to protect the logic-locked implementations against LEDA. We also perform test vector leakage assessment (TVLA) of incorrectly unlocked AES implementations to show the effects of logic locking regarding side-channel leakage. Our results indicate that logic locking is not safe to use in cryptographic circuits, making them less rather than more secure.Item Open Access The impact of domain models on energy consumption of classical planners(2025) Tekin, SerhatThe increasing integration of artificial intelligence into real-world systems has intensified concerns about the ecological footprint of computational processes. As the capabilities of AI expand and their applications spread into diverse areas of society, questions of efficiency are no longer confined to algorithmic performance alone but extend to the broader impact of computation on energy usage. Within this context classical planning provides a particularly relevant case since it is a core technique in automated planning. Research in this field has traditionally emphasized runtime efficiency, plan quality and algorithmic design while the energetic dimension has remained largely neglected. This thesis examines that omission by shifting the focus from planners to the domain models that constitute their input. Through systematic modifications of syntactic, semantic and solvability related features it demonstrates that modeling decisions can exert a measurable influence on energy consumption. Rather than viewing energy use as an inherent property of planners, the study shows it to emerge from the interaction between algorithmic behavior and representational form. The work introduces a replicable framework that combines controlled domain transformations with fine grained energy measurements, thereby enabling systematic evaluations of energy usage in symbolic AI. The empirical analysis indicates that syntactic variations usually result in only minor fluctuations, whereas modeling inefficiencies can increase energy demand, with operator arity standing out as a recurring factor. The most pronounced effects arise from solvability constraints which, depending on the planner and the domain, can lead to substantial increases in energy usage or in some cases reductions. Taken together the results highlight that domain modeling is not only a matter of syntactic correctness or semantic adequacy but also of energetic efficiency. The contribution of this thesis is twofold. It establishes a framework for investigating the energy implications of domain features and provides empirical evidence that modeling choices shape the energy profile of planning systems. These findings offer a foundation for further research and provide practical guidance for approaching domain modeling with energy consumption in mind.Item Open Access Service Injection von TOSCA-basierten Diensten in Java-Anwendungen(2019) Kiefer, LeonViele Cloud Anwendungen bestehen aus mehreren Komponenten und Services, die miteinander kommunizieren. Die Topology and Orchestration Specification for Cloud Applications (TOSCA) definiert einen Standard, um solche Cloud Anwendungen zu beschreiben und zu managen. Um Cloud Services in lokalen Anwendungen zu verwenden, müssen abhängig von der verwendeten Kommunikationstechnologie und der Implementierung der Cloud Services Verbindungsinformationen ausgetauscht werden und spezielle Client Bibliotheken verwendet werden. Dies sorgt für eine hohe Komplexität und schlechte Wiederverwendbarkeit der Implementierung von lokalen Anwendungen. In dieser Arbeit wird ein Konzept vorgestellt, bei dem die komplexe und technologiespezifische Kommunikation nicht in der lokalen Anwendung implementiert wird. Stattdessen werden vorgefertigte Adapter für die jeweilige Technologie mit den passenden Verbindungsinformationen der externen Services in die lokale Anwendung injiziert. Es wird ein Programmiermodell vorgestellt mit dem diese lokalen Anwendungen entwickelt und bereitgestellt werden können. Externen Services werden automatisiert bereitgestellt, wenn diese von der lokalen Anwendung benötigt werden. Die Umsetzbarkeit des Konzeptes wird anhand einer prototypischen Implementierung in Java und der Verwendung von TOSCA-basierten Cloud Services validiert.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.