The Continuous Stream Model of Computation for Real-Time Control
This paper presents a new Model of Computation (MoC) for real-time tasks used in control systems. This new model, named continuous stream task model, relaxes some of the constraints imposed by the traditional hard and soft real-time task models. A key advantage of the model is the possibility to easily analyse the probabilistic evolution of the delays. This leads to an easy formalisation of necessary and sufficient conditions for the stochastic stability of the closed loop system producing considerable savings in the amount of CPU bandwidth required to stabilise the system. This fact is confirmed by an extensive set of simulations.
Abstract Machine learning techniques have been widely applied to production processes with the aim of improving product quality, supporting decision-making, or implementing process diagnostics. These techniques proved particularly useful in the investment casting manufacturing industry, where huge variety of heterogeneous data, related to different production processes, can be gathered and recorded but where traditional models fail due to the complexity of the production process. In this study, we apply Support Vector Representation Machine to production data from a manufacturing plant producing turbine blades through investment casting. We obtain an instance ranking that may be used to infer proper values of process parameter set-points.