Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
Predictive Modular Neural Networks: Applications to Time Series
Predictive Modular Neural Networks: Applications to Time Series
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Supervised classification with temporal data
Supervised classification with temporal data
Reducing energy waste through eco-aware everyday things
Mobile Information Systems - Internet of Things
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In this paper, a temporal machine learning method is presented which is able to automatically construct rules allowing to detect as soon as possible an event using past and present measurements made on a complex system. This method can take as inputs dynamic scenarios directly described by temporal variables and provides easily readable results in the form of detection trees. The application of this method is discussed in the context of switching control. Switching (or discrete event) control of continuous systems consists in changing the structure of a system in such a way as to control its behavior. Given a particular discrete control switch, detection trees are applied to the induction of rules which decide based on the available measurements whether or not to operate a switch. Two practical applications are discussed in the context of electrical power systems emergency control.