• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.557-560
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• 2005

This paper considers a global asymptotic output tracking problem with a prescribed constant reference signal for a class of single-input and single output-output nonlinear systems. It is shown that under some mild conditions on such a system there is a smooth output feedback achieving global asymptotic output tracking and such a smooth output controller is explicitly constructed by a new design method proposed. The usefulness of our result is illustrated by a numerical example.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.200-205
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• 2022

This paper presents an improved maximum power point tracking method that can fast track the global maximum power point (GMPP) for a photovoltaic system under partial shading conditions. The proposed method combines the advantages of the maximum power trapezium (MPT) method and the search-skip-judge method to minimize the tracking voltage intervals. Thus, the proposed method can quickly track the GMPP by skipping unnecessary tracking voltage intervals. The superiority of the proposed method is verified through simulation results in the MATLAB/Simulink and experimental real-time operation results with the hardware-in-the-loop simulation. The simulation and experimental results demonstrated that the proposed method has a faster tracking time than the MPT method under various partial shading conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.2
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• pp.111-117
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• 2020

The photovoltaic module has the characteristic that the output power varies according to the amount of insolation. If partial shading occurs in an environment composed of an array, a number of local maximum power points (LMPPs) may be generated according to the shading state. Photovoltaic arrays require global maximum power point tracking due to variations in output characteristics caused by solar radiation and temperature. Conventional algorithms, such as P&O and Incond, do not follow the global maximum power point in a partial shaded solar array. In this study, we propose a technique to follow the global maximum power point by using the correlation of voltage, current, and power in solar arrays. The proposed control technique 2qw validated through simulation and experiments by constructing a 2-kW solar system.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.297-304
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• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.123-129
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• 2010

In this paper, we analyze the code tracking biases of single and double early-minus-late processing schemes which are widely used code tracking method for global navigation satellite systems. The code tracking bias which results from the distortion in symmetry of correlation values is arisen in the presence of multipath signals. To analyze them, two civil signals which are spreading signals modulated by binary phase shift keying and binary offset carrier are considered.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.54-60
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• 2013

In this paper, a novel method called location-based delivery (LBD), which combines the short message service (SMS) and global position system (GPS), is proposed, and further, a realistic system for tracking a target's movement is developed. LBD reduces the number of short message transmissions while maintaining the location tracking accuracy within the acceptable range. The proposed approach, LBD, consists of three primary features: Short message format, location prediction, and dynamic threshold. The defined short message format is proprietary. Location prediction is performed by using the current location, moving speed, and bearing of the target to predict its next location. When the distance between the predicted location and the actual location exceeds a certain threshold, the target transmits a short message to the tracker to update its current location. The threshold is dynamically adjusted to maintain the location tracking accuracy and the number of short messages on the basis of the moving speed of the target. The experimental results show that LBD, indeed, outperforms other methods because it satisfactorily maintains the location tracking accuracy with relatively fewer messages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.694-701
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• 2004

In this paper, we proposed a feature-based tracking system that traces moving objects with a pan-tilt camera after separating the global motion of an active camera and the local motion of moving objects. The tracking system traces only the local motion of the comer features in the foreground objects by finding the block motions between two consecutive frames using a block-based motion estimation and eliminating the global motion from the block motions. For the robust estimation of the camera motion using only the background motion, we suggest a dominant motion extraction to classify the background motions from the block motions. We also propose an efficient clustering algorithm based on the attributes of motion trajectories of corner features to remove the motions of noise objects from the separated local motion. The proposed tracking system has demonstrated good performance for several test video sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1400-1420
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• 2012

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1738-1739
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• 2011

In this paper, the range difference based the moving target tracking problem using multiple UAVs is solved within the new framework of linear robust state estimation. To do this, the relative kinematics is modeled as an uncertain linear system containing stochastic parametric uncertainties in its measurement matrix. Applying the non-conservative robust Kalman filter for the uncertain system, a quasi-optimal linear target tracking filter is designed. For its recursive linear filter structure, the proposed method can ensure the fast convergence and reliable target tracking performance. Moreover, it is suitable for real-time applications using multiple UAVs.