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 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 Load-balancing for scalable simulations with large particle numbers(2021) Hirschmann, Steffen; Pflüger, Dirk (Prof. Dr.)Item Open Access Metrics and algorithms for locally fair and accurate classifications using ensembles(2022) Lässig, Nico; Oppold, Sarah; Herschel, MelanieTo obtain accurate predictions of classifiers, model ensembles comprising multiple trained machine learning models are nowadays used. In particular, dynamic model ensembles pick the most accurate model for each query object, by applying the model that performed best on similar data. Dynamic model ensembles may however suffer, similarly to single machine learning models, from bias, which can eventually lead to unfair treatment of certain groups of a general population. To mitigate unfair classification, recent work has thus proposed fair model ensembles , that instead of focusing (solely) on accuracy also optimize global fairness . While such global fairness globally minimizes bias, imbalances may persist in different regions of the data, e.g., caused by some local bias maxima leading to local unfairness . Therefore, we extend our previous work by including a framework that bridges the gap between dynamic model ensembles and fair model ensembles. More precisely, we investigate the problem of devising locally fair and accurate dynamic model ensembles, which ultimately optimize for equal opportunity of similar subjects. We propose a general framework to perform this task and present several algorithms implementing the framework components. In this paper we also present a runtime-efficient framework adaptation that keeps the quality of the results on a similar level. Furthermore, new fairness metrics are presented as well as detailed informations about necessary data preparations. Our evaluation of the framework implementations and metrics shows that our approach outperforms the state-of-the art for different types and degrees of bias present in training data in terms of both local and global fairness, while reaching comparable accuracy.Item Open Access Data transfer in partitioned multi-physics simulations : interpolation & communication(2019) Lindner, Florian; Mehl, Miriam (Prof. Dr. rer. nat. habil.)Partitioned multi-physics simulations allow to reuse existing solvers and to combine them to multi-physics scenarios. This provides not only greater flexibility and improved time-to-solution, but also helps to manage the increasing complexity of modern scientific software. This thesis sees itself as a continuation of the works of B. Gatzhammer and B. Uekermann who developed a comprehensive tool to couple independent simulation codes. I focus on the two important aspects of interpolation between non-matching grids as well as communication between several parallel codes and conclude with aspects of software development of the coupling library preCICE. The interpolation part puts special emphasis on radial-basis function interpolation. It starts with a thorough review of existing interpolation methods with special consideration of the black-box approach to multi-physics simulations and explores promising enhancements to RBF interpolation. Numerical experiments provide a rigorous testing for accuracy, stability and scaling behavior of different variants of RBF implementations. Following the insights gained from the numerical experiments, a highly-optimized parallel implementation for preCICE is developed, containing various measures to improve accuracy and stability of the interpolation. The communication part first defines the requirements for partitioned simulations in terms of communication. A new technique for peer-to-peer communication networks between distinct MPI domains is developed and evaluated against existing approaches. Furthermore, a fast method to establish connections via the file system is presented. Both measures optimize the initialization phase and achieve a considerable speedup. Finally, a strategy to fully decouple algorithmically independent participants on the communication protocol level is implemented and tested. In the last part, the software-related challenges in developing a parallel scientific application involving multiple independent solvers are outlined. I show how the preCICE project handles testing, profiling and integration of a large parallel scientific software with multiple participants. A profiling library for distributed applications has been developed and is extensively used in preCICE and potentially other projects.Item Open Access A network abstraction for control systems(2014) Carabelli, Ben W.; Dürr, Frank; Koldehofe, Boris; Rothermel, KurtNetworked control systems (NCS), such as the smart power grid, implement feedback control loops by connecting distributed sensors and actuators to a remote controller over a communication network. In order to avoid the costly and time-consuming installation of dedicated networks, NCS can benefit from utilizing readily available IP networks such as the Internet. However, as control systems are typically sensitive to delay and loss, the integration of such systems over best-effort networks becomes a challenge, which we address in this paper with two main contributions. First, we propose an end-to-end transport abstraction for NCS based on a novel probabilistic quality of service specification which (1) is compatible with existing control models and (2) provides the network with application-specific knowledge about the relation between system performance and network-relevant metrics. Second, we realize this abstraction at the network layer with an optimal routing algorithm, which fulfils the required QoS while minimizing the usage of network resources. We show that our approach lends itself to the implementation with state-of-the-art software-defined networking (SDN) technologies, and demonstrate its effectiveness in our evaluation.Item Open Access Analyzing the influence of hyper-parameters and regularizers of topic modeling in terms of Renyi entropy(2020) Koltcov, Sergei; Ignatenko, Vera; Boukhers, Zeyd; Staab, SteffenTopic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models-Probabilistic Latent Semantic Analysis (pLSA), Additive Regularization of Topic Models (BigARTM), Latent Dirichlet Allocation (LDA) with Gibbs sampling, LDA with variational inference (VLDA)-we, first of all, show that the minimum of Renyi entropy coincides with the “true” number of topics, as determined in two labelled collections. Simultaneously, we find that Hierarchical Dirichlet Process (HDP) model as a well-known approach for topic number optimization fails to detect such optimum. Next, we demonstrate that large values of the regularization coefficient in BigARTM significantly shift the minimum of entropy from the topic number optimum, which effect is not observed for hyper-parameters in LDA with Gibbs sampling. We conclude that regularization may introduce unpredictable distortions into topic models that need further research.Item Open Access Using geographic models in the simulation of mobile communication(2008) Stepanov, Illya; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Network simulation tools are frequently used for the performance analysis of mobile networks. Their common shortcoming lies within the approaches they use for the modeling of user mobility and radio wave propagation. The provided mobility models describe random movements within the area, which is similar to the motion of molecular particles. For the modeling of a radio channel, the tools assume a line of sight between communicating nodes, and thus, a simple dependency of the signal loss to the distance from the transmitter. These models poorly reflect real scenarios, in which the characteristics of the spatial environment have a significant impact on the network performance. In this thesis more realistic mobility and radio propagation models are described and integrated into a network simulation. These models are based on the solutions from related research areas like physics, transportation planning, traffic modeling, and electrical engineering, which have been validated against real-world data. They consider digital maps of the simulation area, which are taken from a geographic information system (GIS). This thesis analyzes common geospatial data standards to provide input to the used mobility and radio propagation models. The evaluations show significant differences between the simulation results obtained with simpler and more realistic models. It is caused by the changes in the distribution of network users due to their mobility in the area and the obstacles of the propagation environment, which simple models cannot reflect.Item Open Access Modeling and simulation of cabin air filtration with focus on electrostatic effects(2019) Schober, Carolin; Mehl, Miriam (Prof. Dr. rer. nat. habil.)Cabin air filters serve to remove harmful pollutants from the air flow supplied to the car passenger compartment. Electrostatic charges on cabin air filter media significantly improve the degree of particle separation without compromising the air permeability, thus achieving superior filtration performance. In order to optimize the performance metrics, a basic understanding of electrostatic filtration effects is required. However, these effects are largely unexplored due to limited experimental measurement options. Numerical simulations allow a deeper insight into fundamental physical processes than the measurement of macroscopic quantities. However, the uni-directionally coupled status quo simulation approach leads to results deviating from experimental observations for electrostatically charged systems. Numerous unknown parameters such as the charge distribution on filter fibers and dust particles and the lacking implementation of all simultaneously effective electrostatic separation mechanisms cause these differences. This dissertation provides an enhanced fully-coupled modeling approach to simulate specific electrostatic filtration effects. The new simulation model includes the interaction of highly bipolar charged dust particles with each other, with filter fibers, and with the background air flow. Extensive studies demonstrate the necessity of this high level of detail in order to dissolve electrostatic agglomeration effects in the inflow area. In addition, combined numerical and experimental test scenarios provide qualitative results allowing to observe the effect of induced dipoles and mirror charges. A combination of the fully-coupled modeling approach with the status quo simulation method in a two-step procedure is highly recommended for further research studies.Item Open Access AssistML : an approach to manage, recommend and reuse ML solutions(2023) Villanueva Zacarias, Alejandro Gabriel; Reimann, Peter; Weber, Christian; Mitschang, BernhardThe adoption of machine learning (ML) in organizations is characterized by the use of multiple ML software components. When building ML systems out of these software components, citizen data scientists face practical requirements which go beyond the known challenges of ML, e. g., data engineering or parameter optimization. They are expected to quickly identify ML system options that strike a suitable trade-off across multiple performance criteria. These options also need to be understandable for non-technical users. Addressing these practical requirements represents a problem for citizen data scientists with limited ML experience. This calls for a concept to help them identify suitable ML software combinations. Related work, e. g., AutoML systems, are not responsive enough or cannot balance different performance criteria. This paper explains how AssistML, a novel concept to recommend ML solutions, i. e., software systems with ML models, can be used as an alternative for predictive use cases. Our concept collects and preprocesses metadata of existing ML solutions to quickly identify the ML solutions that can be reused in a new use case. We implement AssistML and evaluate it with two exemplary use cases. Results show that AssistML can recommend ML solutions in line with users’ performance preferences in seconds. Compared to AutoML, AssistML offers citizen data scientists simpler, intuitively explained ML solutions in considerably less time. Moreover, these solutions perform similarly or even better than AutoML models.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.