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-Saclay
Laboratory : LRI - LaHDAK

Defended on 29/11/2018, committee :

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.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.