• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1524-1531
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• 2020

The aim of this work is to develop a Geiger-Muller (GM) detector system for robot to look for a radioactive source in case of a nuclear emergency or in a high radiation environment. In order to find a radiation source easily, a detector system, including 3 detectors, was designed to search γ-ray radiation sources autonomously. First, based on GEANT4 simulation, radiation dose rates in 3 Geiger-Muller (GM) counters were simulated at different source-detector distances, distances between detectors and angles. Various sensitivity analyses were performed experimentally to verify the simulated designed detector system. A mono-energetic ¹³⁷Cs γ-ray source with energy 662 keV and activity of 1.11 GBq was used for the observation. The simulated results were compared with the experimental dose rate values and good agreements were obtained for various cases. Only based on the dose rates in three detectors, the radiation source with a specific source activity and angle was localized in the different location. A method was adopted with the measured dose rates and differences of distances to find the actual location of the lost γ-ray source. The corresponding angles of deviation and detection limits were calculated to determine the sensitivity and abilities of our designed detector system. The proposed system can be used to locate radiation sources in low and high radiation environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.720-729
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• 2005

In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.688-697
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• 2017

It is very difficult to solve mathematically the optimal control problem for non - linear mobile robots to move to target points with minimum energy related to velocity, acceleration and angular velocity in minimum time. This paper proposes a method to obtain optimal control gains with which mobile robots move with minimum energy related to velocity, acceleration and angular velocity in minimum time using genetic algorithms. Mobile robots are non - linear systems so that their optimal control gains depend on initial positions. Hence initial positions are divided into some partition points and optimal control gains are obtained at each partition point with genetical algorithms. These optimal control gains are used to train neural networks that generate proper control gains at arbitrary initial position. Finally computer simulation studies have been conducted to verify the effectiveness of the method proposed in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.4
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• pp.180-189
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• 2009

Currently, most automobile companies are interested in research on intelligent autonomous vehicle. They are mainly focused on driver's intelligent assistant and driver replacement. In order to develop an autonomous vehicle, lateral and longitudinal control is necessary. This paper presents a lateral and longitudinal control system for autonomous vehicle that has only mono-vision camera. For lane detection, we present a new lane detection algorithm using clothoid parabolic road model. The proposed algorithm in compared with three other methods such as virtual line method, gradient method and hough transform method, in terms of lane detection ratio. For adaptive control, we apply a vanishing point estimation to fuzzy control. In order to improve handling and stability of the vehicle, the modeling errors between steering angle and predicted vanishing point are controlled to be minimized. So, we established a fuzzy rule of membership functions of inputs (vanishing point and differential vanishing point) and output (steering angle). For simulation, we developed 1/8 size robot (equipped with mono-vision system) of the actual vehicle and tested it in the athletics track of 400 meter. Through the test, we prove that our proposed method outperforms 98 % in terms of detection rate in normal condition. Compared with virtual line method, gradient method and hough transform method, our method also has good performance in the case of clear, fog and rain weather.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.26-35
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• 1997

This paper deals with establishing correspondent points of feature pints and sampling period when we estimate object motion parameters from image information of freely moving objects in space of gravity-free state. Replacing the inertial coordinate system with the camera coordinate system which is equipped within a space robot, it is investigated to be able to analyze a problem of correspond points from image information, and to obtain sequence of angular velocity $\omega$ which determine a motion of object by means of computer simulation. And if a sampling period ${\Delta}t$ is shortened, the relative errors of angular velocity are increased because the relative errors against moving distance of feature points are increased by quantization. In reverse, if a sampling period ${\Delta}t$ is lengthened too much, the relative error are likewise increased because a sampling period is long for angular velocity to be approximated, and we confirmed the precision that grows according to ascending of resolution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.175-178
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• 2008

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. But the most of fuzzy systems are difficult to make fuzzy inference rules in the case of MIMO system. The past days, We had proposed the MIMO fuzzy inference which had extended a Z. Cao's fuzzy inference to handle MIMO system. But many times and effort needed to determine the relation matrix elements of MIMO fuzzy inference by heuristic and trial and error method in order to improve inference performances. In this paper, we propose a MIMO fuzzy inference method with the learning ability witch is used a gradient descent method in order to improve the performances. Through the computer simulation studies for the inverse kinematics problem of 2-axis robot, we show that proposed inference method using a gradient descent method has good performances.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.734-741
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• 2020

This article proposes a new pedestrian avoidance algorithm for social robots that coexist and communicate with humans and do not induce stress caused by invasion of psychological safety distance(Social Distance). To redefine the pedestrian model, pedestrians are clustered according to the pedestrian's gait characteristics(straightness, speed) and a social distance is defined for each pedestrian cluster. After modeling pedestrians(obstacles) with the social distances, integrated navigation algorithm is completed by applying the newly defined pedestrian model to commercial obstacle avoidance and path planning algorithms. To show the effectiveness of the proposed algorithm, two commercial obstacle avoidance & path planning algorithms(the Dynamic Window Approach (DWA) algorithm and the Timed Elastic Bands (TEB) algorithm) are used. Four cases were experimented in applying and non-applying the new pedestrian model, respectively. Simulation results show that the proposed algorithm can significantly reduce the stress index of pedestrians without loss of traveling time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.120-130
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• 1994

The leadwire cutting machine that has been used recently cuts leadwires by putting one-side force with the same priciple as a saw, and applies a stress at soldered part of PCB. Because the stress becomes one cause of contact-defect, a leadwire cutting robot that cuts leadwire-site with nipper and does not apply stress is considered, In this paper a technique of detecting leadwire-site is studied for the purpose of using on automatic leadwire cutting robots. A technique deriving 2-dimensional site-information with many I-dimensional binary data of perspective front-view of PCB taken from various direction was proposed. Simulation and experiments were done under the same condition each other and a small universal PCB was choosen as an experimental object. As a result of simulations and experiments, the proposed technique turns out to be very useful for automatic leadwire cutting robots.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.4
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• pp.437-442
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• 2012

To analyze a multibody system, this paper proposes an implicit numerical integration method for joint coordinates subsystem synthesis method. To verify the proposed method, a multibody model for an unmanned robot vehicle, which consists of six identical independent suspension systems, is developed. The symbolic method is applied to compute the system Jacobian matrix for the implicit integration method. The proposed method is also verified by performing rough terrain run-over simulation in comparison with the conventional implicit integration method. In addition, to evaluate the efficiency of the proposed method, the CPU time obtained by using this method is compared with that obtained by using the conventional implicit method.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.235-245
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• 2007

UUV(Unmanned Underwater Vehicle) should possess an intelligent control software performing intellectual faculties such as cognition, decision and action which are parts of domain expert's ability, because unmanned underwater robot navigates in the hazardous environment where human being can not access directly. In this paper, we suggest a RVC intelligent system architecture which is generally available for unmanned vehicle and develope an autonomous navigation system for UUV, which consists of collision avoidance system, path planning system, and collision-risk computation system. We present an obstacle avoidance algorithm using fuzzy relational products for the collision avoidance system, which guarantees the safety and optimality in view of traversing path. Also, we present a new path-planning algorithm using poly-line for the path planning system. In order to verify the performance of suggested autonomous navigation system, we develop a simulation system, which consists of environment manager, object, and 3-D viewer.