• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.277-285
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• 2020

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1039-1048
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• 2013

Generally, the buck converter controller was designed to control output voltage of the converter. However, design of the controller in photovoltaic power conditioning system is different from general design. the controller in photovoltaic power conditioning system controls input voltage of the converter(output voltage of the solar cell) for MPPT(Maximum Power Point Tracking). This paper proposes novel buck converter model which can control input voltage of the converter. We integrate this model with a model of solar cell. and linearize at the operating point(MPP). In addition, we determine whether or not suitable for the general linear controller design into small and large signal analysis.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.6
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• pp.954-959
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• 2022

Marine outfalls are used to discharge treated liquid effluents to the environment. An efficiently designed, constructed and operated marine outfall effectively dilutes the discharged effluent, thereby reducing the risk to biota and humans dependent upon the marine environment. In this study, we investigated the effluent transport from a marine outfall at different depths in Masan Bay. A particle-tracking model was used to predict the dispersion of effluent. The model results indicate that some particles released from a depth of 13 m move to the inner area of Masan Bay within 48 h. As the release depth increases after 48 h, the particles move further southward. This suggests that effluent from the outer area of Masan Bay can affect the inner area, and that this effect can be reduced by increasing the depth of effluent release.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.166-169
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• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model, and the GA is applied to identify this fuzzy model. The proposed method is compared with the AIMM algorithm in simulations.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.389-392
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• 2002

In many industrial applications, multiphase flow analysis is the norm rather than an exception as compared to more-conventional single-phase investigation. This paper describes the implementation of the multiphase flow simulation capability in the general purpose CFD software AVL FIRE/SWIFT. The governing equations are discretized based on a finite volume method (FVM) suitable fur very complex geometry, The pressure field is obtained using the SIMPLE algorithm. Depending on the characteristics of the multiphase flow to be examined, the user can choose either the two-fluid model or an explicit interface-tracking model based on the Volume-of-Fluid approach. For truly 'multi'-phase flow problems, it is also possible to apply a hybrid model where certain phases are explicitly tracked while the other phases are handled by the two fluid model. In order to demonstrate the capability of the method, applications to the Taylor bubble flow simulations are presented.

• The Journal of Korea Robotics Society
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• v.8 no.2
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• pp.104-115
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• 2013

For natural human-robot interaction, we need to know location and shape of facial feature in real environment. In order to track facial feature robustly, we can use the method combining particle filter and active appearance model. However, processing speed of this method is too slow. In this paper, we propose two ideas to improve efficiency of this method. The first idea is changing the number of particles situationally. And the second idea is switching the prediction model situationally. Experimental results is presented to show that the proposed method is about three times faster than the method combining particle filter and active appearance model, whereas the performance of the proposed method is maintained.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.8-15
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• 2024

This paper proposes a linear model predictive control of 6-DOF remotely operated underwater vehicles using nonlinear robust internal-loop compensator (NRIC). First, we design a integrator embedded linear model prediction controller for a linear nominal model, and then let the real model follow the values calculated through forward dynamics. This work is carried out through an NRIC and in this process, modeling errors and external disturbance are compensated. This concept is similar to disturbance observer-based control, but it has the difference that H_∞ optimality is guaranteed. Finally, tracking results at trajectory containing the velocity discontinuity point and the position tracking performance in the disturbance environment is confirmed through the comparative study with a traditional inverse dynamics PD controller.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.862-866
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• 2004

Non-intrusive methods based on active remote IR illumination for eye tracking is important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tracking methodology that works under variable and realistic lighting conditions. Based on combining the bright-pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils are not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tracking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.