• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1396-1406
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• 2018

Lane detection is a key function in developing autonomous vehicle technology. In this paper, we propose a lane marker detection algorithm robust to environmental variation targeting low cost embedded computing devices. The proposed algorithm consists of two phases: initialization phase which is slow but has relatively higher accuracy; and the tracking phase which is fast and has the reliable performance in a limited condition. The initialization phase detects lane markers using a set of filters utilizing the various features of lane markers. The tracking phase uses Kalman filter to accelerate the lane marker detection processing. In a tracking phase, we measure the reliability of the detection results and switch it to initialization phase if the confidence level becomes below a threshold. By combining the initialization and tracking phases we achieved high accuracy and acceptable computing speed even under a low cost computing resources in which we cannot use the computing intensive algorithm such as deep learning approach. Experimental results show that the detection accuracy is about 95% on average and the processing speed is about 20 frames per second with Raspberry Pi 3 which is low cost device.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.174-184
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• 1997

This paper presents a robust force tracking control of a smart flexible gripper featured by a piezoceramic actuator characterizing its durability and quick response time. A mathematical governing equation for the proposed gripper structure is derived by employing Hamilton's principle and a state space control model is subsequently obtained through model analysis. Uncertain system parameters such as frequency variation are included in the control model. A sliding mode control theory which has inherent robustness to the sys- tem uncertainties is adopted to design a force tracking controller for the piezoceramic actuator. Using out- put information from the tip force sensor, a full-order observer is constructed to estimate state variables of the system. Force tracking performances for desired trajectories represented by sinusoidal and step func- tions are evaluated by undertaking both simulation and experimental works. In addition, in order to illustrate practical feasibility of the proposed method, a two-fingered gripper is constructed and its performance is demonstrated by showing a capability of holding an object.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.87.5-87
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• 2001

In the past, we presented the tracking system with one sampling delay. In this paper, first we propose a tracking system with N-th sampling delay, in the case where an input-output pulse transfer function of a plant Z$\_$-N/. Secondly we propose a system configuration converting an input-output pulse transfer function of a plant into Z$\_$-N/ with the inverse system of the plant. Moreover, the proposed tracking system configuration is applied to an actual Ball and Beam system and good results are obtained.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.2980-2988
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• 2000

In this paper, H(sub)$\infty$ depth and course controllers of autonomous underwater vehicles using H(sub)$\infty$ servo control are proposed. An H(sub)$\infty$ servo problem is foumulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the H(sub)$\infty$servo problem is as follows; firest, this problem is modified as an H(sub)$\infty$ control problem for the generalized plant that includes a reference input mode, and than a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach, The H(sub)$\infty$depth and course controllers are designed to satisfy the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(was force, wave moment, tide). The performances(the robustness to the uncertainties, depth and course tracking properties) of the designed controlled are evaluated with computer simulations, and finally these simulation results show the usefulness and applicability of the propose H(sub)$\infty$ depth and course control systems.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.345-355
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• 2000

In this paper, the depth controller of an underwater vehicle based on an $H_\infty$ servo control is designed for the depth keeping of the underwater vehicle under wave disturbances. The depth controller is designed in the form of the $H_\infty$ servo controller, which has robust tracking property, and an $H_\infty$ servo problem is considered for the $H_\infty$ servo controller design. In order to solve the $H_\infty$ servo problem for the underwater vehicle, this problem is modified as an $H_\infty$ control problem for the generalized plant that includes a reference input mode, and a suboptimal solution that satisfies a given performance criteria is calculated with the LMI (Linear Matrix Inequality) approach. The $H_\infty$ servo controller is designed to have robust stability about the perturbation of the parameters of the underwater vehicle and the robust tracking property of the underwater vehicle depth under wave force and moment disturbances. The performance, robustness about the uncertainties, and depth tracking property, of the designed depth controller is evaluated by computer simulation, and finally these simulation results show the usefulness and applicability of the proposed $H_\infty$ depth control system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.215-215
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• 2000

In this paper, depth and course controllers of autonomous underwater vehicles using H$_{\infty}$ servo control are proposed. An H$_{\infty}$ servo problem is formulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the H$_{\infty}$ servo problem is as follows: first, this problem is modified as an H$_{\infty}$ control problem for the generalized plant that includes a reference input mode, and then a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach. The H$_{\infty}$ depth and course controllers ate designed to satisfy with the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(wave force, wave moment, tide). The performances(the robustness to the uncertainties, depth and course tracking properties) of the designed controllers are evaluated with computer simulations, and finally these simulation results show the usefulness and application of the proposed H$_{\infty}$ depth and course control systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1171-1176
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• 2014

The eye-tracking [ET] is used on the human computer interaction [HCI] analysing the movement status as well as finding the gaze direction of the eye by tracking pupil's movement on a human face. Nowadays, the ET is widely used not only in market analysis by taking advantage of pupil tracking, but also in grasping intention, and there have been lots of researches on the ET. Although the vision based ET is known as convenient in application point of view, however, not robust in changing environment such as illumination, geometrical rotation, occlusion and scale changes. This paper proposes two steps in the ET, at first, face and eye regions are discriminated by Haar classifier on the face, and then the pupils from the discriminated eye regions are tracked by CAMShift as well as Template matching. We proved the usefulness of the proposed algorithm by lots of real experiments in changing environment such as illumination as well as rotation and scale changes.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.26-35
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• 2007

We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.265-276
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• 2017

In this paper, a robust and more accurate trajectory tracking control method for a mobile robot is proposed using WIPDC(Weighted Integral Parallel Distributed Compensation) and T-S Fuzzy disturbance observer. WIPDC reduces the steady state error by adding weighted integral term to PDC. And, T-S Fuzzy disturbance observer makes it possible to estimate and cancel disturbances for a T-S fuzzy model system. As a result, the trajectory tracking controller based on T-S Fuzzy disturbance observer shows robust tracking performance. When the initial postures of a mobile robot and the reference trajectory are different, the initial control inputs to the mobile robot become too large to apply them practically. In this study, also, the problem is solved by designing an initial approach path using a path planning method which employs $B\acute{e}zier$ curve with acceleration limits. Performances of the proposed method are proved from the simulation results.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.