• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.123-129
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• 2009

It is important to locate nodes in the research of wireless sensor network. In this paper, we propose a method that estimates the positions of nodes by using adjacent node information and signal strength in wireless sensor network. With this method, we can find positions of nodes easily because we use Information that nodes have. And we can make a map for all the nodes because we can measure a relative position for an node whose position is not known based on anchor nodes whose positions are already known. In addition, we can confirm whether nodes are placed appropriately. We confirmed that we can locate positions of unknown nodes with small error through verifying the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.209-218
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• 2001

Ultrasonic motors are newly developed motors which are expected to be useful as actuators in many practical systems such as robot arms or manipulators because of several advantages against the electromagnetic motors. However, the precise control of the ultrasonic motor is generally difficult due to the absence of appropriate and rigorous mathematical model. Furthermore, owing to heavy nonlinearity, the position control of a pendulum system driven by the ultrasonic motor has a problem that control method using multiple neural network controllers based on a fuzzy inference system that can determine the initial position of the pendulum in the beginning of control operation. In addition, and appropriate neural network controller that has been learned to operate well at the corresponding initial position is adopted by switching schemes. The effectiveness of the proposed method was verified and evaluated from real experiments.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.213-219
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• 2018

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim's positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.6
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• pp.465-472
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• 2013

In this paper, we suggest how to determine the parachute deploy position for accurate landing of a UAV at a desired position. The 9-DOF dynamic modeling of UAV-parachute system is required to construct the proposed algorithm based on neural network nonlinear function approximation technique. The input and output data sets to train the neural network are obtained from simulation results using UAV-parachute 9-DOF model. The input data consist of the deploy position, UAV's velocity, and wind velocity. The output data consist of the cross range and down range of landing positions. So we predict the relative landing position from the current UAV position. The deploy position is then determined through distance compensations for the relative landing positions from the desired landing position. The deploy position is consistently calculated and updated.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.625-633
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• 2004

In this paper, position control of a car-like mobile robot using a neural network is presented. positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as a reference, the robot posture is corrected by a cascaded controller. To improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The neural network functions as a compensator to minimize the positional errors in on-line fashion. A car-like mobile robot is built as a test-bed and experimental studies of several controllers are conducted and compared. Experimental results show that the best position control performance can be achieved by a cascaded controller with a neural network.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.177-180
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• 2010

In cadastral field GPS mainly applies to fundamental survey, while there are numerous research about cadastral detail survey using GPS application in order to increase surveying efficiency as survey technology improve. The purpose of this experiment is to analyze the accuracy of position and estimate the efficiency of GPS/GLONASS combination surveying with control points. As the result of this experiment, Network RTK-GPS/GLONASS combination survey is superior to Newtork RTK-GPS with respect to position accuracy and work efficiency.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.294-300
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• 2009

The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. An adaptive robust term is applied to overcome the external disturbances. The proposed controller is implemented on a high performance digital signal processing DSP TMS320C6713-300. The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.145-148
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• 2003

In this paper an nonlinear neuro PID controller is constructed by the control system of general PID controller using a Self-Recurrent Neural Network. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Applying to the position control system, it's performance is verified through the results of computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.304-309
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• 2003

Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system. In order to realize these tasks autonomously, the three dimensional position of target object such as electric line and the stand of insulator must be recognized accurately and rapidly. The approaching of an insulator and the wrenching of a nut task is selected as the typical task of the maintenance of active electric power distribution lines in this paper. Image recognition by multilayer neural network and optimal target position calculation method are newly proposed in order to recognize the center 3 dimensional position of the bolt hole in the stand of insulator. By the proposed image recognition method, it is proved that the center 3 dimensional position of the bolt hole can be recognized rapidly and accurately without regard to the pose of the stand of insulator. Finally the approaching and wrenching task is automatically realized using 6-link electro-hydraulic manipulators.