Publications

In this paper, an extension of an algorithm for the implicit discretization of a super-twisting sliding mode observer is presented. Implicit and explicit discretization algorithms for homogeneous differentiators, where no physical model information is considered, are investigated in literature. This article studies the behavior when considering models of a rather general class of nonlinear systems. The discrete equations of the super-twisting observer are reformulated as generalized equation and an algorithm for the step-by-step solution is given. The uniqueness of the derived algorithm is investigated with an equivalent variational inequality formulation which is derived for a class of nonlinear systems. Furthermore, a semi-implicit predictor-corrector discretization is presented which is an approximation method for the presented algorithms and allows an explicit implementation in practical applications. Accuracy properties under noise and sampling are given. The algorithm is applied on two mechanical example systems taken from practice.

We consider a first-order logic for the integers with addition. This logic extends classical first-order logic by modulo-counting, threshold-counting and exact-counting quantifiers, all applied to tuples of variables (here, residues are given as terms while moduli and thresholds are given explicitly). Our main result shows that satisfaction for this logic is decidable in two-fold exponential space. If only threshold- and exact-counting quantifiers are allowed, we prove an upper bound of alternating two-fold exponential time with linearly many alternations. This latter result almost matches Berman's exact complexity of first-order logic without counting quantifiers. To obtain these results, we first translate threshold- and exact-counting quantifiers into classical first-order logic in polynomial time (which already proves the second result). To handle the remaining modulo-counting quantifiers for tuples, we first reduce them in doubly exponential time to modulo-counting quantifiers for single elements. For these quantifiers, we provide a quantifier elimination procedure similar to Reddy and Loveland's procedure for first-order logic and analyse the growth of coefficients, constants, and moduli appearing in this process. The bounds obtained this way allow to restrict quantification in the original formula to integers of bounded size which then implies the first result mentioned above. Our logic is incomparable with the logic considered by Chistikov et al. in 2022. They allow more general counting operations in quantifiers, but only unary quantifiers. The move from unary to non-unary quantifiers is non-trivial, since, e.g., the non-unary version of the Härtig quantifier results in an undecidable theory.

The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and - in this way - relieving the CPU from processing. Particularly, in in-memory databases memory access becomes a performance bottleneck. Thus, PIM seems to offer an interesting solution for database processing. In this work, we investigate how commercially available PIM technology can be leveraged to accelerate query processing by offloading (parts of) query operators to memory. Furthermore, we show how to address the problem of limited PIM storage capacity by interleaving transfer and computation and present a cost model for the data placement problem.

Machine Learning (ML) powered Self-Organizing Network (SON) functions are an integral part of the 5G(+) network management to automatically learn and optimize the network performance. Third Generation Partnership Project (3GPP) Release 17 confirmed it by providing a foundation for studying ML-based solutions to tackle network management problems. However, despite their ability to provide high-quality solutions, most ML algorithms lack interpretability, leading to a lack of trust from Mobile Network Operators (MNOs) and delaying the fast integration of ML solutions into operational networks. To address this issue, Explainable Machine Learning (xML) techniques can be used to make complex ML models more interpretable, manageable, and trustworthy. In this work, we apply xML methods to explain an implicit coordination scenario between two conflicting SON functions (SFs): Coverage and Capacity optimization (CCO) and Inter Cell Interference Coordination (ICIC). We show how xML methods can be used to see the coordination problem from an ML model point of view, get meaningful insights, and confirm that the ML model captured all the relevant relationships correctly. This in turn helps to build trust in the ML model which allows it to be used for automatic network management.

With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus. For a cobot to act autonomously and as an assistant, it must understand human actions during assembly. To effectively train models for this task, a dataset containing suitable assembly actions in a realistic setting is cru-cial. For this purpose, we present the ATTACH dataset, which contains 51.6 hours of assembly with 95.2k annotated fine-grained actions monitored by three cameras, which represent potential viewpoints of a cobot. Since in an assembly context workers tend to perform different actions simultaneously with their two hands, we annotated the performed actions for each hand separately. Therefore, in the ATTACH dataset, more than 68% of annotations overlap with other annotations, which is many times more than in related datasets, typically featuring more simplistic assembly tasks. For better generalization with respect to the background of the working area, we did not only record color and depth images, but also used the Azure Kinect body tracking SDK for estimating 3D skeletons of the worker. To create a first baseline, we report the performance of state-of-the-art methods for action recognition as well as action detection on video and skeleton-sequence inputs. The dataset is available at https://www.tu-ilmenau.de/neurob/data-sets-code/attach-dataset.