Tobias Meisen

The Contribution of Virtual Production Intelligence to Laser Cutting Planning Processes

In order to facilitate the improvement in product quality and production efficiency, many companies use simulation applications. In turn, they face the challenge of making these applications interoperable. Once the interoperability is established, the challenges of understanding and improving the processes arise. They can be overcome by modeling and analyzing the processes in question. This paper presents a use case scenario from laser cutting. A new concept is introduced addressing the challenges aforementioned. It conforms to the principles of the integration and examination of data and combines virtual production with the goal of gaining knowledge through the analysis of simulated processes.

Similarity Analysis of Time Interval Data Sets—A Graph Theory Approach

Comparison of entities, i.e., the measurement of their similarity, is a frequent, but challenging task in computer science. It requires a precise and quantifiable definition of similarity itself. Are two texts equal, if they overlap in a majority of their composing words? Does a pair of pictures resemble the same content? What defines the sameness of two songs? While certain distance-based approaches, e.g., Minkowski, make for a good starting point in defining similarity, there is no one-size-fits-all approach. In this work, we tackle a particularly interesting problem, namely, the definition of a similarity measure for comparing time interval data sets. Our approach regards the data sets as disjoint parts of a bigraph, thereby allowing for an application of methods from graph theory. We present both a formal definition of the similarity of two time intervals and our methods as well as concrete use-case from the medical domain, thus demonstrating the applicability for real-world scenarios.