• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.242-246
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• 2010

To determine the trajectories and the impact of rain drops on the facade of a tall building, a particle tracking method is employed form steady state simulation of turbulent flow around the building. The simulation is performed for the upper part of the building comprising a detailed louver system. Rain is trapped at relative high rates on the roof and the penthouse, with Local Intensity Factors (LIF's) of the order of 1. The upper parapets and upper floors get a fair amount of wetting with LIF's of the order of 0.6. The wetting decreases downwards reaching values of 0.2 to 0.25 at the level of the louver system.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.