Research

Artificial Intelligence and Machine Learning

 

Definition


In the course of digitalization, interconnected plants and intelligent products are generating ever-increasing amounts of data. Artificial intelligence methods, especially machine learning, make it possible to analyze this data profitably and generate knowledge from it. This knowledge, in turn, must be represented and linked in such a way that existing data silos can be broken down, end-to-end data integration can be established, and user-friendly applications can be realized.

Vision

The technology field supports industry in identifying and exploiting the various potentials of AI, especially machine learning. Furthermore, it serves the knowledge transfer within the institute.

Focus areas

  • Smart sensors and signal processing
  • Communication standards (e.g., OPC UA, MQTT)
  • Data management and database systems (e.g., SQL, NoSQL)
  • Edge/cloud architectures and IIoT platforms
  • Data preparation and explorative data analysis
  • Machine learning, deep learning (e.g., Convolutional Neural Networks) and reinforcement learning
  • Generative AI (e.g., large language models such as ChatGPT, GANs)
  • Semantic technologies (e.g., Knowledge Graphs, Semantic Web)

Application Fields

  • Data-driven optimization in the Industrie 4.0 environment (e.g., predictive maintenance, predictive quality, machine vision)
  • Knowledge-based assistance systems in engineering (e.g., constraint-based configurators)
  • AI systems in smart home and medical technology applications (e.g., object recognition and environment segmentation)

Head of Technology Field

Members