• Transactions of the Korean Society of Mechanical Engineers
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• v.9 no.5
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• pp.590-597
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• 1985

A recursive geometric adaptive control method to compensate for machining straightness error in the finished surface due to tool deflection and guideway error generated by end milling process is developed. The relationship between the tool deflection and the feedrate is modeled by a modified Taylor's tool life equation. Without a priori knowledge on the variations off cutting parameters, time varying parameters are then estimated by an exponentially windowed recursive least squares method with only post-process measurements of the straightness error. The location error is controlled by shifting the milling bed in the direction perpendicular to the finished surface and adding a certain amount of feedrate with respect to the tool deflection model before cutting. The waviness error is compensated by adjusting the feedrate during machining. Experimental results show that location error is controlled within a range of fixturing error of the bed on the guideway and that about 60% reduction in the waviness error can be achieved within a few steps of parameter adaption under wide operating ranges of cutting conditions even if the parameters do not converge to fixed values.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.314-320
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• 2016

Inertial navigation system (INS) has used aided systems and sensors to compensate navigation error. Global navigation satellite system (GNSS), velocity measurement sensor (VMS), and radar are commonly used to aid INS. Land navigation system (LNS) also mainly uses VMS when GNSS cannot be used such as at tunnel or on jammed scenario. A straight drive is required when VMS-aided navigation is used, because there is only speed of straight direction whereas no crossways and vertical directions. In local environment, even an expressway has lack of straight drive which is constraint of VMS-aided navigation algorithm. This paper proposes an enhanced VMS-aided navigation algorithm for LNS with indirect drive by restricting filter update condition. Also, there is a result of vehicle test to prove performance of the proposed algorithm.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.43-56
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• 2004

The KHCM procedure is a micro level analysis at signalized intersections requiring a lot of input variables and complex computations. The research was to investigate the possibility of simplifying the analysis procedures by using the generalized or the combined variables that less influence on the adjusted traffic volume and through-car equivalents of left or right turns. It was also tried to make lane grouping into directional flow ratio(v/s) based on a field surveys. The maximum and minimum values of each variables were compared with each other through the KHCM analysis procedures in terms of control delay. The lane grouping and the synthetical influence of a simplified method was evaluated with the scenario built in prevailing maximum and minimum conditions. The study showed that the control delay was not significantly sensitive to the selected variables and the lane grouping and their synthetical influence as well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.3
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• pp.266-272
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• 2004

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method will be used to estimate the vehicle parameters such as mass, time constant, etc. The nonlinear model between the driving force and the vehicle acceleration will be chosen to design the state observer for the vehicle velocity and acceleration. It will be shown that the proposed observer is exponentially stable, and that the adaptive controller proposed in this paper is stable by the Lyapunov function candidate. It will be proved that the errors of the relative distance, velocity and acceleration converge to zero asymptotically fast, and that the overall system is also asymptotically stable. The simulation results are presented to investigate the effectiveness of the proposed method.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.157-167
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• 2009

This study deals with the turn penalty functions in the urban transportation demand forecasting. The objectives are to develop the penalty functions of left-turn traffic in the case of signalized intersections, and to analyze the applicability of the functions to the traffic assignment models. This is based on the background that the existing models can not effectively account for the delays of left-turn traffic which is bigger than that of through traffic. In pursuing the above, this study gives particular attention to developing the penalty functions based on the degrees of saturation by simulation results of Transyt-7F, and analyzing the applicability of the functions by the case study of Cheongju. The major findings are the followings. First, two penalty functions developed according to the degrees of saturation, are evaluated to be all statistically significant. Second, the results that the above functions apply to the Cheongju network, are analyzed to be converging, though the iteration numbers increase. Third, the link volumes forecasted by turn penalty functions are evaluated to be better fitted to the observed data than those by the existing models. Finally, the differences of traffic volumes assigned by two functions, which are exponential and divided forms, are analyzed to be very small.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.9-15
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• 2023

In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.207-214
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• 2011

In this paper, using Zigbee-based wireless sensor networks and Lego MindStorms NXT robot, a remote monitoring and navigation system for mobile robot has been developed. Mobile robot can estimate its position using encoder values of its motor, but due to the existing friction and shortage of motor power etc., error occurs. To fix this problem and obtain more accurate position of mobile robot, a ultrasound module on wireless sensor networks has been used in this paper. To overcome disadvantages of ultrasound which include straightforwardness and narrow detection coverage, we rotate moving node attached to mobile robot by $360^{\circ}$ to measure each distance from four fixed nodes. Then location of mobile robot is estimated by triangulation using measured distance values. In addition, images are sent via a network using a USB Web camera to smart phone. On smart phones we can see location of robot, and images around places where robot navigates. And remote monitoring and navigation is possible by just clicking points at the map on smart phones.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.79-84
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• 2021

The most important device of the AGV is the guide sensor, and the typical function of this sensor is high accuracy and extraction of the road. If the accuracy of the guide sensor is low or the sensor device is extracted the wrong track, this causes the problems such as the AGV collision, track-out, the load falling due to AGV swing. In order to improve these problems, this study is proposed a signal processing method of the guide sensor based on multi-maskings and the center of gravity method, and evaluated its performance. As a result, the proposed method showed that the mean error of absolute value is 2.32[mm] and it showed performance improvement of 27[%] than the center of gravity method of existence. Therefore, when the proposed signal processing method is applied, It is thought that the posture control and driving stability of the AGV will be improved.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.111-119
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• 2019

The high-speed railway system is mainly composed of tunnel, bridge, and viaduct to meet the straightness needed for keeping the high speed up to 400 km/s. Seismic fragility for the high-speed railway infrastructure can be assessed as two ways: one way is studying each element of infrastructure analytically or numerically, but it requires lots of research efforts due to wide range of railway system. On the other hand, empirical method can be used to access the fragility of an entire system efficiently, which requires case history data. In this study, we collect the 2004 M_W 6.6 Niigata earthquake case history data to develop empirical seismic fragility function for a railway system. Five types of intensity measures (IMs) and damage levels are assigned to all segments of target system for which the unit length is 200 m. From statistical analysis, probability of exceedance for a certain damage level (DL) is calculated as a function of IM. For those probability data points, log-normal CDF is fitted using MLE method, which forms fragility function for each damage level of exceedance. Evaluating fragility functions calculated, we observe that T=3.0 spectral acceleration (SA_T3.0) is superior to other IMs, which has lower standard deviation of log-normal CDF and low error of the fit. This indicates that long-period ground motion has more impacts on railway infrastructure system such as tunnel and bridge. It is observed that when SA_T3.0 = 0.1 g, P(DL>1) = 2%, and SA_T3.0 = 0.2 g, P(DL>1) = 23.9%.