Publikationen

Future production technologies will comprise a multitude of systems whose core functionality is closely related to machine-learned models. Such systems require reliable components to ensure the safety of workers and their trust in the systems. The evaluation of the functional reliability and resilience of systems based on machine-learned models is generally challenging. For this purpose, appropriate test data must be available, which also includes abnormal cases. These abnormal cases can be unexpected usage scenarios, erroneous inputs, accidents during operation or even the failure of certain subcomponents. In this work, approaches to the model-based generation of an arbitrary abundance of data representing such abnormal cases are explored. Such computer-based generation requires domain-specific approaches, especially with respect to the nature and distribution of the data, protocols used, or domain-specific communication structures. In previous work, we found that different use cases impose different requirements on synthetic data, and the requirements in turn imply different generation methods [1]. Based on this, various use cases are identified and different methods for computer-based generation of realistic data, as well as for the quality assessment of such data, are explored. Ultimately we explore the use of Federated Learning (FL) to address data privacy and security challenges in Industrial Control Systems. FL enables local model training while keeping sensitive information decentralized and private to their owners. In detail, we investigate whether FL can benefit clients with limited knowledge by leveraging collaboratively trained models that aggregate client-specific knowledge distributions. We found that in such scenarios federated training results in a significant increase in classification accuracy by 31.3% compared to isolated local training. Furthermore, as we introduce Differential Privacy, the resulting model achieves on par accuracy of 99.62% to an idealized case where data is independent and identically distributed across clients.

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the ‘.cnt’, eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction.

A cooperating multi-pushdown system consists of a tuple of pushdown systems that can delegate the execution of recursive procedures to sub-tuples; control returns to the calling tuple once all sub-tuples finished their task. This allows the concurrent execution since disjoint sub-tuples can perform their task independently. Because of the concrete form of recursive descent into sub-tuples, the content of the multi-pushdown does not form an arbitrary tuple of words, but can be understood as a Mazurkiewicz trace. For such systems, we prove that the backwards reachability relation efficiently preserves recognizability, generalizing a result and proof technique by Bouajjani et al. for single-pushdown systems. While this preservation does not hold for the forwards reachability relation, we can show that it efficiently preserves the rationality of a set of configurations; the proof of this latter result is inspired by the work by Finkel et al. It follows that the reachability relation is decidable for cooperating multi-pushdown systems in polynomial time and the same holds, e.g., for safety and liveness properties given by recognizable sets of configurations.

The authors’ key area of application is training for the prevention of accidents in the process technology industries. They run a professional training center with own 3D virtual environments. At TLIC 2021, four of the present authors delivered a contribution advocating planning of human training experiences as dynamically as managing some severely disturbed technical system back into a normal operation - such as an out of control chemical reactor - and enabling human trainees who failed to complete a risky task - thereby possibly ruining a (fortunately only virtual) technical installation - to virtually travel back in time to make good the damage. At TLIC 2022, they introduced cascades of gradually more intricate categories of time travel games. With every step from one category to the next, the deployed AI gets more powerful and effective in providing adaptive guidance of human trainees. The most advanced time travel games are those that allow for the dynamic modification of events experienced in the virtual past. In this way, the game system evolves over time and adapts to the needs of human trainees with emphasis on guidance for trainees who fail repeatedly. The extended team of authors presents a novel perspective at time travel prevention games that leads to a more human-centered adaptive guidance. Training is seen through the lens of deontic modal logic. The focus is on undesired events such as explosions, fire, health hazards due to toxic vapors, and the like. The game system’s AI is reasoning about necessity and possibility of such events. It offers to human trainees/players helpful chats about modalities of decisive events of training.

In communication systems, high data rates combined with low end-to-end latencies are prime necessities for allowing a wide variety of applications, e.g., streaming of video and data or in context of IoT Systems. In contrast, applications in Industry Automation require deterministic end-to-end latencies with guaranteed deadlines. In communication systems, data rates, reliability and the achievable end-to-end latency are often a trade-off (e.g., due to buffering of data, and overall systems-design). Further, most communication systems are optimized for high data rates only, yet, deterministic end-to-end latencies are required for most Industrial Use-Cases, which are still not considered well enough in research and standardization. In this paper we focus on low-latency communication, and, outline the importance of this research aspect. Consequently, we propose a novel Mini-Slot approach for 5G and beyond communication systems to tackle the problem of minimizing uplink- and downlink communication latencies in cellular networks under load. First evaluations of our approach in context of a feasibility study show promising results. As comparison in realistic experiments with Rel-15-based 5G Commercial off-the-shelf ( COTS ) hardware, a baseline scenario (unoptimized) shows a maximum latency up to 49.04 ms. In contrast to that, our novel mini-slot approach allows to lower the maximum end-to-end communication latency to 15.51 ms. This way, our mini-slot approach constitutes as enabler for low-latency communication by using Rel-15-based 5G COTS and User Equipment ( UE ) hardware for industrial use-cases, without the need to wait for further releases of 5G systems.