• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.211-218
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• 2010

An analysis model on debris transport in the containment floor of pressurized water reactors is developed in which the flow field is calculated by Eulerian conservation equations of mass and momentum and the debris particles are traced by Lagrange equations of motion using the pre-determined flow field data. For the flow field calculation, two-dimensional Shallow Water Equations derived from Navier Stokes equations are solved using the Finite Volume Method, and the Harten-Lax-van Leer scheme is used for accuracy to capture the dry-to-wet interface. For the debris tracing, a simplified two-dimensional Lagrangian particle tracking model including drag force is developed. Advanced schemes to find the positions of particles over the containment floor and to determine the position of particles reflected from the solid wall are implemented. The present model is applied to calculate the transport fraction to the Hold-up Volume Tank in Advanced Power Reactors 1400. By the present model, the debris transport fraction is predicted, and the effect of particle density and particle size on transport is investigated.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.863-870
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• 2008

In this paper, a new filtering scheme for vehicle tracking with distorted measurement is presented. This filtering scheme is essential for the implementation of the adaptive cruise control (ACC) system. The proposed method combines the IMM and the probabilistic fuzzy model and is named as the Fuzzy IMM (FIMM). The IMM is employed to recognize the intention of the preceding vehicle. The probabilistic furry model is introduced to compensate the distortion of the range sensor. Finally, a computer simulation is performed to illustrate the validity of the suggested algorithms.

• Smart Structures and Systems
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• v.31 no.5
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.9-21
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• 2013

Tracking is a process which explores user equipment (UE) in the area of tracking in terms of cells. In this paper, two tracking schemes based on macrocell-microcell tiers in hierarchical LTE networks, PMMT and IMMT, are evaluated. In this network, UE can receive a signal from macrocells and overlapping microcells, and can be called from each macrocell or microcell-tier in the PMMT. Also, the UE can be called from the combined macrocell-tier and microcell-tier in the IMMT. Finally, we analyze the optimization of load balance between marcocell-tier and microcell-tier, and an analytical model is developed to evaluate those two arrangements.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.223-228
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• 2005

In oprical disk drives, surface defects on a disk distort tracking error signal and disturb a precision tracking control.. A conventional method against disk defect is held the tracking control signal when a defective portion is detected. However, if the defective portion is getting longer, objective lens will get away from following track. In order to keep the postion of spot from following track, the servo system must predict tracking error and control the object lens in the defective portion. A tracking control system for optical disk drives was proposed recently based on both Coprime Factorization(CF) and Zero Phase Erro. Tracking(ZPET) control. The system was proposed for overcome the limit of previously tracking error. But there were no research about the method against the defective portion. This paper proposes a new and simple ZPET construct. as a new method against the defective portion. From experimental results, we have proved that proposed method improves the performance against the defective portion, decreases the uncertainty of a model, and requires less memory than the previously proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.561-566
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• 2005

This paper presents a visual tracking algorithm for the golf swing motion analysis by using the information of the pixels of video frames and movement of the golf club to solve the problem fixed center point in model based tracking method. The model based tracking method use the polynomial function for trajectory displaying of upswing and downswing. Therefore it is under the hypothesis of the no movement of the center of gravity so this method is not for the amateurs. we proposed method using the information of pixel and movement, we first detected the motion by using the information of pixel in the frames in golf swing motion. Then we extracted the club head and hand by a properties of club shaft that consist of the parallel line and the moved location of club in up-swing and down-swing. In addition, we can extract the center point of user by tracking center point of the line between center of head and both foots. And we made an experiment with data that movement of center point is big. Finally, we can track the real trajectory of club head, hand and center point by using proposed tracking algorithm.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.