• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.6
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• pp.53-61
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• 2014

For monitoring the flight trajectory and the status of a launch vehicle, the mission control system in NARO space center process data acquired from the ground tracking system, which consists of two tracking radars, four telemetry stations, and one electro-optical tracking system. Each tracking unit exhibits its own tracking error mainly due to multi-path, clutter and radio refraction, and by utilizing only one among transmitted informations, it is not possible to determine the actual vehicle trajectory. This paper presents a way of generating flight trajectory via post-processing the data received from the ground tracking system. The post-processing algorithm is divided into two parts: compensation for atmosphere radio refraction and multi-sensor fusion, for which a decentralized Kalman filter was adopted and implemented based on constant acceleration model. Applications of the present scheme to real data resulted in the flight trajectory where the tracking errors were minimized than done by any one sensor.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.3
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• pp.127-134
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• 2003

In this paper, the speed control problem of moving camera is investigated for tracking the movement of the human-face. The camera system with pan-tilt mechanism sends an image to PC and PC sends back tracking coordinate to the camera. Then the camera tracks a human face in real time. The speed of the stepping motors for moving the camera must be controlled to the target region fast enough and smoothly, In this paper, a fuzzy logic controller is proposed for driving step motors. By creating driving acceleration and deceleration speed Profile, the speed of the motors is controled fast and smoothly. Experiments are performed to show the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.1022-1032
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• 2000

We propose a sliding mode controller tracking the states of a time-varying reference model. The reference model generates the desired trajectories of the states, and the sliding mode controller regulates robustly the errors between the desired states and the measured states. We apply this controller to the overhead crane. Its reference model generates the trajectories of the damped-out swing angle and the swing angular velocity to suppress the swinging motion caused by the acceleration and the deceleration of crane transportation. Also, this model generates the desired trajectories of the position and velocity of the crane. The crane model is identified from the experimental data using an orthogonal function. Kalman filtering is applied to estimate the crane states. The designed controller is simulated on a computer and is tested through a 2-ton industrial overhead crane using the vector-controlled servo motor system. It is verified that, from the simulated and experimental results, the sliding mode controller tracking a time-varying reference model works well.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.62-72
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• 1993

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three dimensional adaptive target tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track updata illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver level detector. A detailed simulation is conducted to test the validity of our tracking algorithm for target trajectories under various conditions of maneuver.

• Journal of Advanced Navigation Technology
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• v.11 no.4
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• pp.359-370
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• 2007

Lane tracking and obstacle avoidance are considered two of the key technologies on an unmanned ground vehicle system. In this paper, we propose a method of lane tracking and obstacle avoidance, which can be expressed as vehicle control, modeling, and sensor experiments. First, obstacle avoidance consists of two parts: a longitudinal control system for acceleration and deceleration and a lateral control system for steering control. Each system is used for unmanned ground vehicle control, which notes the vehicle's location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control strategy of the vehicle can detect obstacle and perform obstacle avoidance on the road, which involves vehicle velocity. Second, we explain a method of lane tracking by means of a vision system, which consists of two parts: First, vehicle control is included in the road model through lateral and longitudinal control. Second, the image processing method deals with the lane tracking method, the image processing algorithm, and the filtering method. Finally, in this paper, we propose a method for vehicle control, modeling, lane tracking, and obstacle avoidance, which are confirmed through vehicles tests.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.445-451
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• 2002

In this paper, we propose a new tracking algorighm that achieves good tracking performance in manuevering targets while capping the computation load to "low". Kalman Filler (KF) is generally known to be poor in tracking maneuvering targets. IMM, on the other hand, compensates the weakness inherent in the mundane KF and is considered as a promising alternative for tracking maneuvering targets. However, IMM suffers from substantially increased computational load as the number of models increases. To remedy this problem, we propose a new method focused to reducing the computational load and attaining the desirable tracking performance at least as good that of IMM. It is achieved by essentially adopting the structure of IMM and injecting Optimal Two-Stage Kalman Estimator (OTSKE). The representative simulation shows a reduction in computational load with the proposed OTSKE but further reduction is shown achieved (by about 58%) with the Interacting Acceleration Compenstation (IAC)-OTSKE approach. approach.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.1
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• pp.74-81
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• 2014

This article documents the design of a novel two-loop acceleration autopilot based on $\mathcal{L}_1$ adaptive output feedback control for tail-controlled missiles. The inner loop is an adaptive angle-of-attack tracking loop and the outer loop is the traditional PI controller for error compensation. A systematic low-pass filter design procedure is provided for minimum phase system and is applied to the inner loop design while the parameters of the outer loop are obtained from the multi-objective optimization problem. The effectiveness of the proposed autopilot is verified through numerical simulations under various conditions.

• Journal of Mechanical Science and Technology
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• v.16 no.9
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• pp.1166-1174
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• 2002

This paper presents the implementation and vehicle tests of a vehicle longitudinal control scheme for Stop and Go cruise control. The control scheme consists of a vehicle-to-vehicle distance control algorithm and throttle/brake control algorithm for acceleration tracking. The desired acceleration of a vehicle for vehicle-to-vehicle distance control has been designed using Linear Quadratic optimal control theory. Performance of the control algorithm has been investigated via vehicle tests. A millimeter wave radar sensor has been used for distance measurement. A stepper motor and an electronic vacuum booster have been used for throttle/brake actuators, respectively. It has been shown that the proposed control algorithm can provide satisfactory performance.