• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.232-239
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• 2016

Recently, in order to increase the efficiency and mission success rate of UAVs (Unmanned Aerial Vehicles), the necessity for formation flights is increased. In general, GPS (Global Positioning System) is used to obtain the relative position of leader with respect to follower in formation flight. However, it can't be utilized in environment where GPS jamming may occur or communication is impossible. Therefore, in this study, monocular vision is used for measuring relative position. General PC-based vision processing systems has larger size than embedded systems and is hard to install on small vehicles. Thus FPGA-based processing board is used to make our system small and compact. The processing system is divided into two blocks, PL(Programmable Logic) and PS(Processing system). PL is consisted of many parallel logic arrays and it can handle large amount of data fast, and it is designed in hardware-wise. PS is consisted of conventional processing unit like ARM processor in hardware-wise and sequential processing algorithm is installed on it. Consequentially HW/SW co-designed FPGA system is used for processing input images and measuring a relative 3D position of the leader, and this system showed RMSE accuracy of 0.42 cm ~ 0.51 cm.

• Journal of the Korean Society of Industry Convergence
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• v.10 no.2
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• pp.105-113
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• 2007

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of application. Sensor position is used for its data to be meaningful and for energy efficient data routing algorithm especially geographic routing. The previous works for sensor localization utilize global positioning system(GPS) or estimate unknown-location nodes position with help of some small reference nodes which know their position previously. However, the traditional localization techniques are not well suited in the senor network for the cost of sensors is too high. In this paper, we propose the sensor localization method with a mobile robot, which knows its position, moves through the sensing field along pre-scheduled path and gives position information to the unknown-location nodes through wireless channel to estimate their position. We suggest using the sensor position estimation method and an efficient mobility path model. To validate our method, we carried out a computer simulation, and observed that our technique achieved sensor localization more accurately and efficiently than the conventional one.

• Journal of the Korean Society for Precision Engineering
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• v.2 no.1
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• pp.72-81
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• 1985

This paper deals with the Z-8000 microprocessor based optimal controller problem of large rotating system. Control algorithm consists of Global Mode and Fine Mode. In Global Mode, motor is driven with maximum torque, while, in Fine Mode, the speed of response and overshoot improved by multi-gains. Friction term of the plant was measured in the 1-st test, jerking effect by the nonlinearity of friction was compensated in the 2-nd test and the 3-rd test was carried out to finalize the control system model. Test results show that the speed of response and overshoot are highly improved.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.136-138
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• 2003

UML cannot meet all the requirements offered in different software system for diverse application domain. GNSS (Global Navigation Satellite System) application domain is an especial environment that requires precise measurement and precision calculation of real-world geographical entities with the help of GPS (Global Position System) in both temporal and spatial factor. Therefore in the paper new extended iconic stereotypes for better modeling GNSS application in the UML Diagram are proposed, and the implementation of a program called StereotypeCreator, which is able to create iconic stereotypes used in one of the most popular visual modeling tools for software development, Rational Rose, will be also proposed.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.95-100
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• 2013

Localization is very important for the autonomous navigation of Unmanned Ground Vehicles; however, it is difficult that they have a precise Inertial Navigation System(INS) sensor, especially Small Unmanned Ground Vehicle(SUGV). Moreover, there are some condition such as denial of global position system(GPS), GPS/INS integrated system is not robust. This paper proposes the estimation algorithm with optical flow sensor and INS. Being compared with previous researches, the proposed algorithm is suitable for skid steering vehicles. We revised the measurement model of previous research for the accuracy of side direction position. Experimental results were performed to verify the algorithm, and the result showed an excellent performance.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.571-576
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• 2015

This paper proposes an auto-parking controller for omnidirectional mobile robots. The controller uses the multi-sensor system including ultrasonic sensor and camera. The several ultrasonic sensors of robot detect the distance between robot and each wall of the parking lot. The camera detects the global position of robot by capturing the image of artificial landmarks. To improve the accuracy of position estimation, we applied the extended Kalman filter with adaptive fuzzy controller. Also we developed the fuzzy control system to reduce the settling time of parking. The experimental results are presented to verify the usefulness of the proposed controller.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.629-632
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• 1999

In this paper, color coordinate system transformation based visual servo controller has been considered. Mobile robot always has a position error and an orientation error resulted from wheel slipping etc.. Even more, the errors have accumulative properties. So feedback from environments is important. In this paper, by using color model faster land mark extraction can be achieved. And the global position and the orientation of mobile robot can be known by only two land marks positions in image coordinate system. Finally, the adoption of visual information in path tracking problem makes visual servo control.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.27-35
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• 2008

A navigation system is one of the important components of an unmanned ground vehicle (UGV). A GPS receiver collects data signals transmitted by (Earth orbiting) satellites. However, these data signals may contain many errors resulting misinformation and depending on one's position (environment), reception may be impossible. The proposed self-driven algorithm uses three low-cost GPS in order to minimize errors of existing inexpensive single GPS's driving algorithm. By using reliable final data, which is analyzed and combined from each of three GPS's received data signals, gathering a vehicle's steering performance information and its current pin-point position is improved even with error containing signals or from a place where signal gathering is impossible. The purpose of this thesis is to explain navigation system algorithm using multiple GPS and compass sensor and prove the algorithm through experiments.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.591-595
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• 2000

In this paper color coordinate system transformation based visual servo controller has been considered Mobile robot always has a position error and an orientation error resulted from wheel slipping etc.. Even more, the errors have accumulative properties. So feedback from environments is important. In this paper by using color model faster land mark extraction can be achieved. And the global position and the orientation of mobile robot can be known by only two land mark positions in image coordinate system. Finally, the adoption of visual information in path tracking problem makes visual servo control.