Jean-Thomas Sexton

Université Laval | ULAVAL · Department of Computer Science

We present a novel artificial intelligence approach that encompasses both predictive and prescriptive aspects for the challenging task of model-based control of industrial wood planers. These sophisticated lumber finishing machines are known for the complexity of their operation, and the available data pertaining to the planing process exhibits com...

Synthetic data generation of industrial processes exhibiting non-stationarity and complex, non-linear dependencies between their inputs and outputs is a challenging task. We argue that vine copula models are particularly well suited for this problem and present a method combining limited available data and expert knowledge in order to generate synt...

Wood planers are high speed sophisticated lumber finishing machines that are difficult to operate and for which the available data shows complex, non-linear patterns. We present a machine learning approach to build a control loop for an industrial wood planer. In order to predict the thickness of the outgoing boards with better accuracy than the in...