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임종환 濟州大學校 産業技術硏究所 2001 산업기술연구소논문집 Vol.12 No.1
This paper presents a technique for localization of a mobile robot using sonar sensor. Localization is the continual provision of a knowledge of position which is deduced from its a priori position estimation. The environment of a robot is modeled into a two dimensional grid map. We define a physically-based sonar sensor model and employ an extended Kalman filter to estimate position of the robot. The performance and simplicity of the approach is demonstrated with the results produced by sets of experiments using a mobile robot.
임종환,김명석,박병권,황윤환,황미현,박승춘,윤효인 한국독성학회 2005 Toxicological Research Vol.21 No.4
The aim of the present study was to evaluate systemic bioavailability of surfactin and to determine its pharmacokinetic profiles. The stability of surfactin to pH, temperature and protease was evaluated. Surfactin was resistant to high temperature, a wide range of pH and the action of hydrolytic enzymes. The pharmacokinetic natures of surfactin which were shown the short half-life, rapid clearance and poor bioavailability. The results of study should provide preliminary data of surfactin for further dose-finding studies and for the design of application forms. It is also be important to a context of the safety of surfactin.
임종환,권순국 한국농공학회 1989 한국농공학회논문집 Vol.31 No.4
A multiple box model which is suitable for the prediction of water quality in shallow lakes with active mixing is a water quality model expected to be used widely in estuary reservoir. In this study, a multiple box water quality model for estuary reservoirs (MBQER) was developed arid the applicability of the MBQER was tested by applying data obtained from Asan-estuary reservoir. The results of this study can be summarized as follows. 1. The MBQER, dynamic water quality model, was developed to estimate 10-day water qualities of estuary reservoirs. For the proper analysis and the application of hydraulics needed to build a model, lake hydraulics was simplified by condisering only hydrological inflow and lake mixing currents. The box division in the MBQER is longitudinal one dimension for upper and middle part, and two layers for lower part of the reservoir. 2. The methods of box division for the multiple box model were ekamined and applied to Asan-estuary reservoir. For determining the number of boxes, Pe number and Pk number were used. In case of three boxes, the error by the model simplification would be estimated about 5 % Therefore, in Asan reservoir, the proper number of boxes was three. 3. The MBQER was calibrated and verified using measured data in Asan-estuary reservoir from 1986 to 1988. The Root Mean Squares(RMS) for the differences between measured data and simulated results by the MBQER were 1.10$^{\circ}$C C for water temperature, 75.8mg/1 for salinity, 0.082mg/1 for total-phosphorus showing good estimations. 4. Through the simulation of water temperature and salinity by the MBQER, the exchange flow and the mixing coefficients for the estuary lake were determined. As a result of simulation, the horizontal mixing coefficients in Asan-estuary reservoir were in the range of 1.07X 105 to 1.12X 105 cm$^2$/sec and vertical mixing coefficient was 2.90X 10-1 cm$^2$/sec.
임종환,강철웅 제주대학교 공과대학 첨단기술연구소 2004 尖端技術硏究所論文集 Vol.15 No.2
This paper presents extended Kalman filter based navigation performances of an autonomous underwater vehicle(AUV). The system is composed of a mother ship (small unmanned marine prober) on the surface of the water and an unmanned underwater vehicle in the water. The mother ship is equipped with a digital compass and a GPS for position information, and extended Kalman filter is used for position estimation. For the localization of the AUV, we used only non-inertial sensors such as a digital compass, a pressure sensor, a clinometer and ultrasonic sensors. From the orientation and velocity information, a priori position of the AUV is estimated by applying the dead reckoning method. A posteriori position of the AUV is, then, updated by using the distance between the AUV and a mother ship on the surface of the water together with the depth information from the pressure sensor. The performances of the navigation system has been estimated in various situations through sets of simulations.