Three-Dimensional Particle Tracking Velocimetry Measurement for Bubbly Flow with Digital Color Image Processing
In this paper we present the study on a three-dimensional PTV measurement of bubbly flow with color digital image processing. The tracers for the liquid motions shine with yellow-green by an ultra-violet illumination. The bubbles are shadow of the incandescent lights with red-colored cellophane sheets. By using six mirrors we lead the image of the test section seen from different three directions to a high-speed color video camera. Color digital image processing makes the bubbles and tracers extracted from background light. We calculate the velocity vectors, trajectories and the three-dimensional characteristics of the bubbles. We reconstruct the whole bubbly flow and present one by OpenGL.
Each driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. GP allow the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented.