Universität Stuttgart
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Item Open Access Making historical gyroscopes alive - 2D and 3D preservations by sensor fusion and open data access(2021) Fritsch, Dieter; Wagner, Jörg F.; Ceranski, Beate; Simon, Sven; Niklaus, Maria; Zhan, Kun; Mammadov, GasimThe preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical technologies emerged, together with optical instruments. In the meantime, it is not only museums who showcase these developments, but also companies, universities, and private institutions. Gyroscopes are fascinating instruments with a history dating back 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition towards their sustainable future. Here, the systems are digitized in 2D, 2.5D, and 3D and are made available for a worldwide community using open access platforms. The technologies being used are computed tomography, computer vision, endoscopy, and photogrammetry. We present a novel workflow for combining voxel representations and colored point clouds, to create digital twins of the physical objects with 0.1 mm precision. This has not yet been investigated and is therefore pioneering work. Advantages and disadvantages are discussed and suggested work for the near future is outlined in this new and challenging field of tech heritage digitization.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 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 Load-balancing for scalable simulations with large particle numbers(2021) Hirschmann, Steffen; Pflüger, Dirk (Prof. Dr.)Item Open Access Coupled simulations and parameter inversion for neural system and electrophysiological muscle models(2024) Homs‐Pons, Carme; Lautenschlager, Robin; Schmid, Laura; Ernst, Jennifer; Göddeke, Dominik; Röhrle, Oliver; Schulte, MiriamThe functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.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 Optimality principles in human point-to-manifold reaching accounting for muscle dynamics(2020) Wochner, Isabell; Driess, Danny; Zimmermann, Heiko; Häufle, Daniel F. B.; Toussaint, Marc; Schmitt, SynHuman arm movements are highly stereotypical under a large variety of experimental conditions. This is striking due to the high redundancy of the human musculoskeletal system, which in principle allows many possible trajectories toward a goal. Many researchers hypothesize that through evolution, learning, and adaption, the human system has developed optimal control strategies to select between these possibilities. Various optimality principles were proposed in the literature that reproduce human-like trajectories in certain conditions. However, these studies often focus on a single cost function and use simple torque-driven models of motion generation, which are not consistent with human muscle-actuated motion. The underlying structure of our human system, with the use of muscle dynamics in interaction with the control principles, might have a significant influence on what optimality principles best model human motion. To investigate this hypothesis, we consider a point-to-manifold reaching task that leaves the target underdetermined. Given hypothesized motion objectives, the control input is generated using Bayesian optimization, which is a machine learning based method that trades-off exploitation and exploration. Using numerical simulations with Hill-type muscles, we show that a combination of optimality principles best predicts human point-to-manifold reaching when accounting for the muscle dynamics.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.Item Open Access Knowledge-based modeling of simulation behavior for Bayesian optimization(2024) Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, MiriamNumerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.