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SUMMARY:Deterministic optimization for nonlinear data processing technique
 s
DTSTART:20130322T101500
DTEND:20130322T110000
DTSTAMP:20260407T051249Z
UID:c2835efc7a36a172cd45a53a5718cd8c343e36e9088ed0321c5ba584
CATEGORIES:Conferences - Seminars
DESCRIPTION:Kris Villez\nQualitative analysis techniques are used to segme
 nt time series into different episodes with a particular shape\, correspon
 ding to the first and second derivative. Such segmentation can be used for
  on-line process monitoring and diagnosis. However\, because time series a
 re typically noisy\, random\, noisy features need to be separated from det
 erministic process behaviour. To this end\, a technique called Qualitative
  Representations of Trends (QRT) was previously adopted for supervisory co
 ntrol of a Sequencing Batch Reactor (SBR) for biological nutrient removal.
  Unfortunately\, this and other techniques are based on heuristic approach
 es and therefore suboptimal results are obtained. Recent developments have
  led to a branch-and-bound scheme for deterministic\, global optimality in
  the context qualitative analysis. This approach is based on shape-constra
 ined spline functions and has been successfully applied for diagnosis of a
  simulated SBR system as well as for a real-life on-line phosphorus sensor
 . Based on the demonstrated potential of deterministic optimization in dat
 a mining and process monitoring applications\, a similar strategy is under
  development for nonlinear process monitoring models such as nonlinear pri
 ncipal component analysis (NLPCA). The talk will include theoretical aspec
 ts\, results as well as demonstrations of the implemented algorithms.
LOCATION:ME C2 405
STATUS:CONFIRMED
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