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김의선 ( Eui Sun Kim ),김원호 ( Won Ho Kim ) 한국센서학회 2010 센서학회지 Vol.19 No.3
In recent studies, many methods have been studied for mobile robot using magnetic markers on its pathway. This is not influenced by the weather conditions, and makes possible to develop controller with low level processors and simple algorithms. However, the interval between magnets is restricted by the magnetic field intensity and it is impossible to get road information ahead. This paper suggests a method of widening markers and expressing the road information ahead using magnetic markers, and explains a sensor arrangement considering suggested methods. Also, magnetic field analysis was done to investigate the effects of widening magnetic markers with various environments. A small mobile robot was made to figure out the performance of suggested methods, and driving experiments were performed on the straight and curved road with magnetic markers. The results show that the robot moved the prearranged pathway with 0.5cm lateral displacements and stopped at a stop line using magnetic information on the road.
정의헌(Cheang Eui-Heang),문채주(Moon Chae-Joo),김의선(Kim Eui-Sun),장영학(Chang Young-Hak) 한국태양에너지학회 2009 한국태양에너지학회 논문집 Vol.29 No.6
There has been a continuous increase in the utilization and utility value of renewable energy such as wind power generation in modern society. Wind condition is the absolute variable to the energy volume in the case of a wind power generation system. For this reason, wind power generators have already been installed in areas where wind velocity is high and the possibility of danger is very low. In other words, instability is likely if the wind velocity in an area is high and where a wind power generation system can be built. On the contrary, low wind velocity is possible in an area with high stability. Therefore, the design and manufacture of a wind power generation system should be carried out in a more complicated topography in order to secure a bigger market. This study examines and suggest how topography affects wind shear by analyzing the measured data in order to predict wind power generation more reliably.