Learning Diagnoser and Supervision Pattern in Discrete Event System : Application to Crisis Management

Moussa Traore, Moamar Sayed-Mouchaweh, and Patrice Billaudel
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Sponsored by the Champagne-Ardenne region and the French ministry of higher education and research
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phmc_13_070.pdf162.5 KBOctober 11, 2013 - 2:53am

The increase of natural, industrial disasters and diverse crisis has stimulated more research interest in the world. A crisis can be industrial accident, train accident, earthquake, and etc. However, the crisis management is currently an important challenge for medical service and research, to develop new technical of decision support system to guide the decision makers. Crisis management is a special type of collaboration, therefore several aspects must be considered. The more important aspect or problem in a crisis management, is the coordination (and communication) between different actors and groups involved in the management. In this paper the focus is how to handle the coordination and interaction between these different actors and groups involved in crisis management by using a finite state automaton. The representation of the crisis management as a set of couple of states and events allows to optimize the crisis management by having real time the evolution of the situation and the prediction of their evolution at their earliest.

Publication Year: 
2013
Publication Volume: 
4
Publication Control Number: 
070
Page Count: 
9
Submission Keywords: 
discrete event system
Learning Dagnosis
Supervision Pattern
prediction
Crisis Management
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

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