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Ph.D de YE Lina
YE Lina
Ph.D
Group : Artificial Intelligence and Inference Systems

Optimized Diagnosability of Distributed Discrete Event Systems Through Abstraction

Starts on 01/10/2007
Advisor : DAGUE, Philippe

Funding : A
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 07/07/2011, committee :
Mme. Marie-Odile Cordier, professeur, Universite de Rennes 1, IRISA-INRIA
M. Stephane Lafortune, professeur, The University of Michigan, Dept. Of
Electrical Engineering and Computer Science
examinateur:
M. Paul Gastin, professeur, LSV, CNRS & ENS de Cachan
Mme Fatiha Zaïdi, MCF HDR, LRI, Université Paris Sud
directeur de thèse:
M. Philippe Dague, professeur, LRI, Université Paris Sud

Research activities :

Abstract :
The subject of this thesis focuses on methods for determining the
diagnosability property of discrete event systems in distributed way
without building the global model of the system. This framework is of
primary importance for real applications: distributed systems, systems are
too complex to manage their global model, confidentiality of local models
to each other. We first investigate how to optimize distributed
diagnosability analysis by abstracting necessary and sufficient
information
from local objects to decide global diagnosability decision. The algorithm
efficiency can be greatly improved by synchronization of abstracted local
objects compared to that of non abstracted local ones.

Then we extend the distributed diagnosability algorithm from fault event
first to simple pattern and then to general pattern, where pattern can
describe more general objects in the diagnosis problem, e.g. multiple
faults, multiple occurrence of the same fault, ordered occurrence of
significant events, etc. In the distributed framework, the pattern
recognition is first incrementally performed normally in a subsystem and
then pattern diagnosability can be determined by adjusting abstracted
method used in fault event case. We prove the correctness and efficiency
of
our proposed algorithm both in theory through proof and in practice
through
implementation.

Finally we study distributed diagnosability problem in systems with
autonomous components, i.e. observable information is distributed instead
of centralized. In other words, each component can only observe its own
observable events. We first describe cooperative diagnosis architecture
for
such a system before defining a new cooperative diagnosability definition.
Then we propose an efficient way for cooperative diagnosability
verification by analyzing communication compatibility between local
objects
through certain synchronization.

Ph.D. dissertations & Faculty habilitations
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.

A NEW GENERATION OF GRAPH NEURAL NETWORKS TO TACKLE AMORPHOUS MATERIALS


SPOTTING NEURAL NETWORK BOTTLENECKS AND FIXING THEM BY ARCHITECTURE GROWTH