text: Yo, check it out, I got a paper to present,
  A framework for detecting MPM, that's my intent.
  In cross-directional processes on paper machines,
  Under model-predictive control, know what I mean?
  
  First step, we use routine operating data,
  For system identification, closed loop, okay?
  Then we train a one-class SVM, don't you know,
  To predict MPM, that's how we flow.
  
  This iterative method, it's smooth and slick,
  Identifying impulse response coefficients, real quick.
  Spatial and temporal models, we alternate between,
  Converging to estimates, consistent and clean.
  
  We take those estimates, from normal operation,
  Train the one-class SVM, no hesitation.
  Now it detects MPM, during routine operation,
  No need for external excitations, it's a sensation.
  
  This approach, it's dope, it's off the chain,
  Distinguishing process model mismatches from noise, that's the game.
  We've got examples of CD processes on paper machines,
  To show the effectiveness, know what I mean?
  
  So there you have it, my paper's complete,
  A two-component framework, detecting MPM, so neat.
  In the world of paper machines, we're making strides,
  Using data and SVM, we're on the rise.