Signal Classification

Just like the time series prediction the classifiation of sensor signals for the assessment of system states and the control of the deduced actions is one of the core components of any kind of "intelligent system".

Application Examples

Technical systems such as

  • automotive safety systems and automated driving functions, which monitor their environment and classify the danger of the current driving situation,
  • systems monitoring and controling any kind of industrial installation or facilities which detect dangerous or abnormal operational conditions to warn or autonomously control the system states,
  • environmental monitoring systems, which warn predictively to allow the early triggering of counter measures already before critical limits are exceeded,
  • any kind of autonomous robot, that has to detect objects in its environment and interpret its surroundings,
  • traffic control systems, which classify the traffic situation and select the best control strategy in an intelligent way,
  • classification and interpretation of system conditions for control systems, which are adaptive with respect to the situation,
  • any kind of diagnostic system,
  • ...

are all equipped with a number of sensors, which are monitoring the system's state and have to act predictively and in some cases autonomously to prevent critical situations and avoid dangerous states if possible.

Technology and Advantages

Machine learning methods are qualified for this, because they

  • can help by developing and implementing solutions very quickly and efficiently, especially for sophisticated problems,
  • can deal with fuzzy and uncertain or even incomplete sensor data and information,
  • can compensate systematic sensor errors,
  • are robust in development and operation,
  • can be adopted very quickly to new operational conditions, if necessary also autonomously,
  • ...