Aurélien Hazan

A Schelling model with switching agents: decreasing segregation via random allocation and social mobility

AbstractWe study the behaviour of a Schelling-class system in which a fraction f of spatially-fixed switching agents is introduced. This new model allows for multiple interpretations, including: (i) random, non-preferential allocation (e.g. by housing associations) of given, fixed sites in an open residential system, and (ii) superimposition of social and spatial mobility in a closed residential system. We find that the presence of switching agents in a segregative Schelling-type dynamics can lead to the emergence of intermediate patterns (e.g. mixture of patches, fuzzy interfaces) as the ones described in [E. Hatna, I. Benenson, J. Artif. Soc. Social. Simul. 15, 6 (2012)]. We also investigate different transitions between segregated and mixed phases both at f = 0 and along lines of increasing f, where the nature of the transition changes.

Trajectory clustering for vibration detection in aircraft engines

The automatic detection of the vibration signature of rotating parts of an aircraft engine is considered. This paper introduces an algorithm that takes into account the variation over time of the level of detection of orders, i.e. vibrations ate multiples of the rotating speed. The detection level over time at a specific order are gathered in a socalled trajectory. It is shown that clustering the trajectories to classify them into detected and non-detected orders improves the robustness to noise and other external conditions, compared to a traditional statistical signal detection by an hypothesis test. The algorithms are illustrated in real aircraft engine data.

Topological characterization of mobile robot behavior

We propose to classify the behaviors of a mobile robot thanks to topological methods as an alternative to metric ones. To do so, we adapt an analysis scheme from physics of nonlinear systems in chaotic regime, assuming a dissipative dynamics that relaxes on a low-dimensional manifold. Sensor data recorded from a mobile robot during a wall-following experiment allows to compute topological invariants that give a standardized representation of the structure of the set of trajectories, and enable us to discriminate among similar behaviors in a systematic and quantitative way