• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.115-121
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• 2012

In spite of serious injuries caused by traffic accidents of car-pedestrian, the dispute is constantly occurring and economic losses and mental suffering is escalating since the cause of accidents is not scientifically identified. This study reviewed vehicle dynamics, driving dynamics, collision dynamics, traffic and road engineering for traffic accidents analysis based on traffic accidents related physically objective evidence and analysed the cause of accidents by getting results which applied vehicle initial collision velocity before collision, processing trajectory, collision stance, vehicle velocity before & after collision and parameter by using PC-Crash program. I found that skid mark and collision velocity of car-pedestrian had the error of 11.2%, 2,27% compared to theoretical values.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.6
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• pp.659-665
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• 2010

This paper proposes a control algorithm for quadruped robots moving on irregularly sloped uneven surfaces. Since the body balance of a quadruped robot is controlled by the forces acting on its feet during touchdown, the ground reaction force (GRF) is controlled for stable running. The desired GRF for each foot is generated on the basis of the desired galloping pattern; this GRF is then compared with the actual contact force. The difference between the two forces is used to modify the foot trajectory. The desired force is realized by considering a combination of the rate change of the angular and linear momenta at flight. Then, the amplitude of the GRF to be applied at each foot in order to achieve the desired linear and angular momenta is determined by fuzzy logic. Dynamic simulations of galloping motion were performed using RecurDyn; these simulations show that the proposed control method can be used to achieve stable galloping for a quadruped robot on irregularly sloped uneven surfaces.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.14-26
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• 2009

This study presented a novel method to estimate travel times under incident conditions. Predictive travel time information was defined and evaluated with the proposed method. The proposed method utilized individual vehicle speeds obtained from global positioning systems (GPS) and inter-vehicle communications(IVC) for more reliable real-time travel times. Individual vehicle trajectory data were extracted from microscopic traffic simulations using AIMSUN. Market penetration rates (MPR) and IVC ranges were explored with the accuracy of travel times. Relationship among travel time accuracy, IVC ranges, and MPR were further identified using regression analyses. The outcomes of this study would be useful to derive functional requirements associated with traffic information systems under forthcoming ubiquitous transportation environment

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.68-80
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• 2016

This study proposes the traffic prediction and optimal traffic control system based on cell transmission model and genetic algorithm in cloud environment. The proposed prediction and control system consists of four parts. 1) Data preprocessing module detects and imputes the corrupted data and missing data points. 2) Data-driven traffic prediction module predicts the future traffic state using Multi-level K-Nearest Neighbor (MK-NN) Algorithm with stored historical data in SQL database. 3) Online traffic simulation module simulates the future traffic state in various situations including accident, road work, and extreme weather condition with predicted traffic data by MK-NN. 4) Optimal road control module produces the control strategy for large road network with cell transmission model and genetic algorithm. The results show that proposed system can effectively reduce the Vehicle Hours Traveled upto 60%.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.99-108
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• 1998

In this paper, we proposed a multi-layer neural network for direct control method of nonlinear system. The proposed control method uses neural network as the controller to learn inverse model of plant. The neural network used consists of two parts; one part is for identification of linear part, and the other is for identification of nonlinear part of inverse system. The neural network has to be learned the liner part with RLS algorithm and the nonlinear part with error of plant. From the simulation and experiment of tracking control to use one link manipulator as plant, we proved usefulness of the proposed control method to comparing to conventional direct neural network control method. By comparing the two methods, from simulation and experiment, we were convinced that the proposed control method is more simple and accuracy than the conventional method. Moreover, number of weight and bias to be controller parameter are small, and it has smaller steady state error than conventional method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.666-673
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• 2008

To construct the accurate radio satellite navigation system, the efficient communication each satellite with the ground station is very important. Throughout the communication, the orbit of each satellite can be corrected, and those information will be used to analyze the satellite satus by the operator. Since there are limited resources of ground station, the schedule of antenna's azimuth and elevation angle should be optimized. On the other hand, the satellite in the medium earth orbit does not pass the same point of the earth surface due to the rotation of the earth. Therefore, the antenna pass schedule must be updated at the proper moment. In this study, Q learning approach which is a form of model-free reinforcement learning and genetic algorithm are considered to find the optimal antenna schedule. To verify the optimality of the solution, numerical simulations are conducted.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.648-657
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• 2014

This paper is about the study on a newly developed small waterproofed 4-axis robot arm and the analysis of its kinematics and dynamics. The structure of robot arm is designed to have Pitch-Pitch-Pitch-Yaw joint motion for inspection using a camera on itself and the joint actuator driving capacity are selected and the joint actuators are designed and test for 10m waterproofness. The closed-form solution for the robot arm is derived through the forward and inverse kinematics analysis. Also, the dynamics model equation including the damping force due to the mechanical seal for waterproofness is derived using Newton-Euler method. Using derived dynamics equation, a sliding mode controller is designed to track the desired path of the developed robot arm, and its performance is verified through a simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.268-275
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• 2011

This paper proposes two optimal path planning methods of a mobile robot using uDEAS (univariate Dynamic Encoding Algorithm for Searches). Before start of autonomous traveling, a self-controlled mobile robot must generate an optimal global path as soon as possible. To this end, numerical optimization method is applied to real time path generation of a mobile robot with an obstacle avoidance scheme and the basic path generation method based on the concept of knot and node points between start and goal points. The first improvement in the present work is to generate diagonal paths using three node points in the basic path. The second innovation is to make a smooth path plotted with the blending polynomial using uDEAS. Effectiveness of the proposed schemes are validated for several environments through simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.108-117
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• 2012

This paper focuses on a performance analysis of TRN among various nonlinear filtering methods. In a TRN research, extended Kalman filter(EKF) is a basic estimation algorithm. In this paper, iterated EKF(IEKF), EKF with stochastic linearization(SL), and unscented Kalman filter(UKF) algorithms are introduced to compare navigation performance with original EKF. In addition to introduced sequential filters, bank of Kalman filters method, which is one of the batch method, is also presented. Finally, by simulating an artificial aircraft mission, EKF with SL was chosen as the most consistent filter in the introduced sequential filters. Also, results suggested that the bank of Kalman filters can be alternative for TRN, when a fast convergence of navigation solution is needed.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.297-306
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• 2007

Model reference control is control method such that overall response of a plant plus controller approaches that of a given reference model. The reference model provides desired trajectory the plant should follow. There are many kinds of control methods in MRC. However, this paper focuses on Model Reference Sliding Mode Control. The plant of these controls is an uncertain and linear system varying in time, of second order, and with SISO. In this paper, a design scheme of reference model is proposed for MRSMC. The scheme determines reference model based on the information on bounded control inputs, reference inputs and system parameters. It is used to choose a Fixed Reference Model in the process of controller design, to update Variable Reference Model when stepwise reference inputs change and to update Instant Reference Model at every sampling time. The simulation results show that the proposed method yields better control performance than the conventional MRC subject to the stepwise reference input when applied to the position control system for motor system.