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Showing papers by "Jurgen Jasperneite published in 2021"


Journal ArticleDOI
TL;DR: In this paper, the authors examine the existing challenges in this area in more detail and give an outlook on the possible solutions to ensure safety and security much quicker and with less manual effort.
Abstract: In order to ensure the safety and security of industrial systems with regard to all life cycle phases from development through operation to disposal, specific regulatory and normative requirements are imposed. Due to the digitalization, interconnection, and constantly increasing complexity of manufacturing systems in the context of Industrie 4.0, the manual effort necessary to achieve the required safety and security is becoming ever greater and almost impossible to manage, especially for small and medium-sized enterprises. Therefore, this paper examines the existing challenges in this area in more detail and gives an outlook on the possible solutions to ensure safety and security much quicker and with less manual effort. The overall vision is a (partially) automated risk assessment of modular systems with respect to safety and security, including the alignment of the corresponding processes from both domains and the formalization of the information models needed.

4 citations



Posted Content
TL;DR: LemgoRL as mentioned in this paper is a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany, which includes a traffic signal logic unit that ensures compliance with all regulatory and safety requirements.
Abstract: Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the well-known OpenAI gym toolkit to enable easy deployment in existing research work. Our benchmark tool drives the development of RL algorithms towards real-world applications. We provide LemgoRL as an open-source tool at this https URL.