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Ph.D de

Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Modeling and Qualitative Simulation of Hybrid Systems

Starts on 01/10/2015
Advisor : DAGUE, Philippe

Funding : Contrat de thèse d'autres organismes
Affiliation : Université Paris-Sud
Laboratory : LRI - LaHDAK

Defended on 29/11/2018, committee :
Directeur :
- Philippe DAGUE Université Paris-Sud

Co-encadrant :
- Jean-Pierre GALLOIS, CEA LIST

Rapporteurs :
- Walid TAHA ,Université de Halmstad
- Goran FREHSE, ENSTA-ParisTech

Examinateurs :
- Erika ÁBRAHÁM, Université RWTH Aachen
- Sylvain CONCHON, Université Paris-Sud

Research activities :

Abstract :
Hybrid systems are complex systems that combine both discrete and continuous behaviors. Verifying behavioral or safety prop- erties of such systems, either at design stage or on-line is a challenging task. Actually, computing the reachable set of states of a hybrid system is undecidable. One way to verify those properties over such systems is by computing discrete abstractions and inferring them from the abstract system back to the original system. We are concerned with abstractions oriented towards hybrid systems diagnosability checking. Our goal is to create discrete abstractions in order to verify if a fault that would occur at runtime could be unambiguously detected in finite time by the diagnoser. This verification can be done on the abstraction by classical methods developed for discrete event systems, which provide a counterexample in case of non-diagnosability. The absence of such a counterexample proves the diagnosability of the original hybrid system. In the presence of a counterexample, the first step is to check if it is not a spurious effect of the abstraction and actually exists for the hybrid system, witnessing thus non-diagnosability. Otherwise, we show how to refine the abstraction and continue the process of looking for another counterexample.

Ph.D. dissertations & Faculty habilitations
DECODING THE PLATFORM SOCIETY: ORGANIZATIONS, MARKETS AND NETWORKS IN THE DIGITAL ECONOMY
The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

DISTRIBUTED COMPUTING WITH LIMITED RESOURCES


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