This paper aims at showing a method to discover signatures (or models of chronicles) from a discrete event sequence (alarms) generated by a Monitoring Cognitive Agent (MCA). When the counting process of the events generated by a couple (Process, MCA) behaves like a Poisson process, this couple can be considered as stochastic discrete event generator SDEG (Pr, MCA) and modeled as a superposition of Poisson and an homogeneous discrete time Markov chain. The 'BJT' algorithm uses these two representations in order to help in the discovering of signatures. The results obtained on an industrial process monitored with a Sachem system have been validated by Experts, confirming so the relevance of the approach within an industrial frame.
Defining activity models in order to monitor human behavior in smart environments is one of the major issues at the moment of building systems of activity supervision for diagnosis, prediction and control. For the purpose of addressing this problem, this paper proposes a general theoretical approach based on the use of a Knowledge Engineering methodology and a Machine Learning process, which are funded on a general theory of dynamic process modeling, the Timed Observation Theory.
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