• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.311-312
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• 2007

This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. Finally we conducted an experiment for the object tracking system based on a pan/tilt structure.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.102-110
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• 2000

In this paper, Wiener-Hopf design of the two-degree-of-freedom(2DOF) controller configuration is treated for the standard plant model. It is shown that the 2DOF structure makes it possible to treat the design of feedback properties and reference tracking problem separately. Wiener-Hopf factorization technique is used to obtain the optimal controller which minimizes a given quadratic cost index. The class of all stabilizing controllers that yield finite cost index is also characterized. An illustrative example is given for the step reference tracking problem which can not be treated by the conventional H2 controller formula.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2222-2224
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• 2001

This paper presents the implementation of an efficient car tracking system based on Inverted DGPS (IDGPS) using mobile phone and internet for data communication link. The IDGPS technique, which may provide the same level of accuracy as DGPS, is subject to degradation in real time applications when transmission time delays occur in the communication links. Therefore, an effective communication system is the key element for successful application of the IDGPS. The combination of internet and mobile phones provides a high data rate, unlimited communication packet size, flexible data structure, and wide coverage without additional communication devices. An implementation of such a data link system is described. Field test results are included to evaluate the performance of the proposed system.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.255-263
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• 2012

This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.286-286
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.154-161
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• 1996

A neural predictive tracking system for the control of structure-unknown dynamic system is presented. The control system comprises a neural network modelling mechanism for the the forward and inverse dynamics of a plant to be controlled, a feedforward controller, feedback controller, and an error prediction mechanism. The feedforward controller, a neural network model of the inverse dynamics, generates feedforward control signal to the plant. The feedback control signal is produced by the error prediction mechanism. The error predictor adopts the neural network models of the forward and inverse dynamics. Simulation results are presented to demonstrate the applicability of the proposed scheme to predictive tracking control problems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.443-448
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• 2000

In this paper, we propose a novel adaptive feedforward controller for periodic disturbance and noise cancellation, with a frequency tracking capability. It can be added to an existing feedback control system without altering the original closed-loop characteristics, which is based on adaptive algorithm. We introduce novel algorithm "Constrained AFC(adaptive feedforward controller) algorithm" that increase the convergence region regardless of the delay in the closed loop system. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the coefficients become time invariant. The proposed algorithm not only estimate the magnitude and phase of the tonal disturbance and noise but also track the frequency of the tone, which changes in quasi-static manner. The frequency tracking algorithm uses the instantaneous frequency approach based on Hilbert transform. A number of computer simulations have been carried out in order to demonstrate the effectiveness of proposed method under various conditions.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1728-1735
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• 2009

Human action tracking plays an important place in human-computer-interaction, human action tracking is a challenging task because of the exponentially increased computational complexity in terms of the degrees of freedom of the object and the severe image ambiguities incurred by frequent self-occlusions. In this paper, we will propose a novel method to track human action, in our technique, a dynamic background estimation algorithm will be applied firstly. Based on the estimated background, we then extract the human object from the video sequence, and the skeletonization method and Hough transform method will be used to detect the main structure of human body and each part rotation angle. The calculated rotation angles will be used to control a crane in the port, thus we can just control the container crane by using signalman body. And the experimental results can show that our proposed method can get a preferable result than the conventional methods such as: MIT, JPF or MFMC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.611-614
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• 2021

In this paper, we propose a widerange array microphone for lecturer tracked with Frequency Modulated Continuous Waveform (FMCW) radar sensor. Time Difference-of-Arrival (TDoA) is often used as audio tracking, but the tracking accuracy is poor because the frequency of the voice is low and the relative frequency change is large. FMCW radar has a simple structure and is used to detect obstacles for vehicles, and the resolution can be archived to several centimeter. It is shown that the sensor is useful for detecting a speaker in open area such as a lecture, and we propose an wide range 4-element array microphone beamforming system. Through some experiments, the proposed system is able to adequately track the location and showed a 8.6dB improvement over the selection of the best microphone.

• Journal of the Korean Society of Visualization
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• v.19 no.2
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• pp.32-40
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• 2021

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.