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Prediction in and control of complex systems

In our department, we basically deal with two different types of complex industrial systems:

  • Measurement systems
  • Machine vision systems

Our focus in dealing with measurement systems is set on the prediction of current or future states of certain system variables in order to be able to conclude on upcoming faults in the system or simply to save expenses for measurement sensors. The complexity of the system is usually due to the high number of variables we have to deal with, typically, and depending on the non-linearity of the current problem, ranging from a few hundreds up to thousand. Applied methodologies are:

  • High-dimensional dynamic models for system identification and prediction
  • Analysis of residuals
  • Uni- and multivariate statistical methods
  • Signal processing methods

Our focus in machine vision systems relates to classification statements with high accuracy (see also 'Optical fault detection and quality control’), as well as to decision support systems for customers, users and operators. The methodologies
we apply in this context are:

  • Classifiers
  • On-line learning methods
  • Distance measures
  • Image segmentation algorithms
  • Feature extraction (low-level and high-level features)