Publications

High-tech cyber-physical systems (CPS) are becoming increasingly diverse: they may have many variations and configurations, might be part of product families, and may be highly customizable. Additionally, CPSs tend to continuously evolve—have variation in time—for example due to technology updates, changing demands, or changing requirements.

The performance of cyber-physical systems (CPS) is a determining factor for their success and often needs to be guaranteed. When performance issues occur, their analysis and the identification of the root-cause should be fast. Typically the analysis requires the relation of system observations (i.e., tracing) to design and implementation artifacts.

The functionality that we use as organizations and citizens increasingly arises from a complex interplay of man-built systems, individuals and organizations, and the environment. It is a challenge to get the desired functionality and features consistently, reliably and affordably, without unwanted side effects.

Manufacturing companies of complex distributed cyber-physical systems (dCPS) are encountering challenges with respect to designing their next-generation machines. They need efficient Design Space Exploration (DSE) techniques to evaluate possible design decisions and their consequences on nonfunctional aspects of the systems.

The lack of explicit and precise specifications of software interfaces between components often leads to integration issues during development and maintenance. To address this, we have developed a framework named ComMA (Component Modeling and Analysis) that supports model based engineering of high-tech systems by precisely defining components and their interfaces.

Software analysis often relies on pattern matching in terms of Abstract Syntax Trees (ASTs), but AST patterns are known to be tedious to specify. Concrete syntax patterns with placeholders have been proposed as a user-friendly alternative. Several designs for this proposal have been implemented, but these typically focus on specific parsing technologies.

This work presents the latest advancements in leveraging artificial intelligence (AI) and digital twin (DT) technologies to automate the operation of electron microscopes, with a particular focus on exploring the feasibility of automated electron microscope alignment. The alignment of electron microscopes is a laborious process, demanding significant time and expert knowledge.

WP3 is concerned with the development of a technical approach and a reference architecture for DT-supported AI-based system optimisation. System optimisation can be performed by connecting AI to both the physical system and its DT. By allowing the AI to take control over the DT, a learning cycle based on reward and punishment can be constructed to validate its actions.

The ASIMOV project investigates the combination of Digital Twins (DTs) and Artificial Intelligence (AI) to find the opportunities and challenges for automated optimization and calibration of complex high-tech systems in complex environments. In many cases the actual system is not available for training AI components, therefore a dedicated digital twin or digital model is set up for providing that training data.