• The Journal of the Acoustical Society of Korea
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• v.29 no.1
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• pp.18-23
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• 2010

The RLS (Recursive Least Squares) method is a broadly used adaptive algorithm for signal processing in electronic engineering. The RLS algorithm shows a good performance and a fast adaptation within a stationary environment, but it shows a Poor performance within a non-stationary environment because the method has a fixed forgetting factor. In order to enhance 'tracking' performances, BLS methods with an adaptive forgetting factor had been developed. This method shows a good tracking performance, however, it suffers from heavy computational loads. Therefore, we propose a modified AFF-RLS which has relatively low complexity m this paper.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.558-561
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• 2009

In this paper, we propose Simple Frame Marker(SFMarker) to support recognition of characters and images included in a marker in augmented reality. If characters are inserted inside of marker and are recognised using Optical Character Recognition(OCR), it doesn't need marker learning process before an execution. It also reduces visual disturbance compared to 2D barcode marker due to familarity of characters. Therefore, proposed SFMarker distinguishes Square SFMarker that embeds images from Rectangle SFMarker with characters according to ratio of marker and applies different recognition algorithms. Also, in order to reduce preprocessing of character recognition, SFMarker inserts direction information in border of marker and extracts it to execute character recognition fast and correctly. Finally, since the character recognition for every frame slows down tracking speed, we increase the speed of recognition process using the result of character recognition in previous frame when frame difference is low.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.817-824
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• 2007

Tractography using Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a method to determine the architecture of axonal fibers in the central nervous system by computing the direction of the principal eigenvector in the white matter of the brain. However, the fiber tracking methods suffer from the noise included in the diffusion tensor images that affects the determination of the principal eigenvector. As the fiber tracking progresses, the accumulated error creates a large deviation between the calculated fiber and the real fiber. This problem of the DT-MRI tractography is known mathematically as the ill-posed problem which means that tractography is very sensitive to perturbations by noise. To reduce the noise in DT-MRI measurements, a tensor-valued median filter which is reported to be denoising and structure-preserving in fiber tracking, is applied in the tractography. In this paper, we proposed the modified gradient descent method which converges fast and accurately to the optimal tensor-valued median filter by changing the step size. In addition, the performance of the modified gradient descent method is compared with others. We used the synthetic image which consists of 45 degree principal eigenvectors and the corticospinal tract. For the synthetic image, the proposed method achieved 4.66%, 16.66% and 15.08% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively. For the corticospinal tract, at iteration number ten the proposed method achieved 3.78%, 25.71 % and 11.54% less error than the conventional gradient descent method for error measures AE, AAE, AFA respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.3
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• pp.307-316
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• 2016

A multiple radar system is comprised of early warning radar for fast detection of a target and air defense radar for precision intercept. For this reason, target take-over process is required between the two radars. The target take-over should be performed at an appropriate time by consideration of stable tracking and effective fire control. In this paper, operation characteristics of multiple radar system are analyzed and target take-over time determination method using estimation of target tracking performance is proposed for high-velocity targets. The proposed method is validated with ballistic target defense scenarios in the developed integrated simulator.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.191-201
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• 2019

A fast and accurate integrated traffic object detection framework was proposed and developed, harnessing a computer-vision based deep-learning approach performing automatic object detections, a multi object tracking technology, and video pre-processing tools. The proposed method is capable of detecting traffic object such as autos, buses, trucks and vans from video recordings taken under a various kinds of external conditions such as stability of video, weather conditions, video angles, and counting the objects by tracking them on a real-time basis. By creating plausible experimental scenarios dealing with various conditions that likely affect video quality, it is discovered that the proposed method achieves outstanding performances except for the cases of rain and snow, thereby resulting in 98% ~ 100% of accuracy.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.129-135
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• 2016

In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to ${\alpha}-{\beta}$ tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.684-691
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• 2001

In this paper, we design and implement a system which recognizes and tracks a human head on a sequence of images. In this paper, the color of the skin and ellipse modeling is used as feature vectors to recognize the human head. And the modified time-varying edge detection method and the vertical projection method is used to acquire regions of the motion from images with very complex backgrounds. To select the head from the acquired candidate regions, the process for thresholding on the basis of the I-component of YIQ color information and mapping with ellipse modeling is used. The designed system shows an excellent performance in the cases of the rotated heads, occluded heads, and tilted heads as well as in the case of the normal up-right heads. And in this paper, the combinational technique of motion-based tracking and recognition-based tracking is used to track the human head exactly even though the human head moves fast.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5459-5473
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• 2017

Recently, a tracking algorithm called the spatial-temporal context model has been proposed to locate a target by using the contextual information around the target. This model has achieved excellent results when the target undergoes slight occlusion and appearance changes. However, the target location in the current frame is based on the location in the previous frame, which will lead to failure in the presence of fast motion because of the lack of a prediction mechanism. In addition, the spatial context model is updated frame by frame, which will undoubtedly result in drift once the target is occluded continuously. This paper proposes two improvements to solve the above two problems: First, four possible positions of the target in the current frame are predicted based on the displacement between the previous two frames, and then, we calculate four confidence maps at these four positions; the target position is located at the position that corresponds to the maximum value. Second, we propose a target reliability criterion and design an adaptive threshold to regulate the updating speed of the model. Specifically, we stop updating the model when the reliability is lower than the threshold. Experimental results show that the proposed algorithm achieves better tracking results than traditional STC and other algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.9
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• pp.807-813
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• 2016

Recently, a spherical pendulum attached to an end-effector of a robot manipulator has been frequently used for a test bed of residual vibration suppression control in a multi-dimensional motion. However, there was no automatic tracking system to detect the current bob position on-line, and there was inconvenience to not be able to store the bob position in real time and plot the trajectory. In this study, we developed a two-dimensional, real-time bob-detecting system using a digital USB camera, of which the key is hardware component design and software C programming for fast image processing and interfacing. The developed system was applied to residual vibration suppression control of a two-dimensional spherical pendulum that is attached at the end-effector of a two degree-of-freedom SCARA robot, and the effectiveness of the developed system has been demonstrated.