Using models in different contexts poses major integration challenges, ranging from technical to conceptual levels. Independently of each other developed model components cannot b...
Model is a kind of codified knowledge that has been verified in solving problems. Solving a complex problem usually needs a set of models. Using components, the composition of a s...
This paper reports on a theoretical Bayesian modeling development for residual life prediction in the context of condition-based maintenance. At each monitoring point during a comp...
Principal components analysis (PCA) is one of the most widely used techniques in machine learning and data mining. Minor components analysis (MCA) is less well known, but can also...
Max Welling, Felix V. Agakov, Christopher K. I. Wi...
: Embedded Computer-based Systems are becoming highly complex and hard to implement because of the large number of concerns the designers have to address. These systems are tightly...