• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.243-246
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• 2004

본 논문은 영상인식 방법으로 개선된 Active Model을 이용한 방법을 제안한다. 제안된 방법은 모든 격자 블록 영역이 동일한 구조를 가지며, 기존의 Active net에서 문제가 되었던 목표물을 탐지하는 능력이 개선되었다. 실험 결과로서 제안된 방법이 수직, 수평 방향에서 목표물 포착에 효과적임을 보여주었으며, 실제 도로 영상에 적용한 결과 제안한 방법의 효율성을 입증할 수 있었다.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.336-344
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• 2005

In this paper, we extracted the contour of lung parenchyma on EBT images with the improved active contour model. The objects boundary in conventional active contour model can be extracted by controlling internal energy and external energy as energy minimizing form. However, there are a number of problems such as initialization and the poor convergence about concave part. Expecially, contour can not enter the concave region by discouraging characteristic about stretching and bending in internal energy. We controlled internal energy by moving local perpendicular bisector point of each control point in the contour and implemented the object boundary by minimizing energy with external energy The convergence of concave part could be efficiently implemented toward lung parenchyma region by this internal energy and both lung images for initial contour could also be detected by multi-detection method. We were sure this method could be applied detection of lung parenchyma region in medical image.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.661-669
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• 2019

High frequency active sonar is appropriate for detecting small targets such as a diver in coast environment. In case of using high frequency active sonar in shallow coastal environment, a false alarm rate is high due to clutters caused by marine biological noise, ship noise, wake, etc. In this paper, we propose an algorithm for target detection which is robust against clutter in active sonar system in shallow coastal environment. The proposed algorithm increases the rate of reduction clutter using calculation of statistical characteristics of signal and a clustering method. The algorithm is evaluated and analysed with sea trial data, as a result, that shows the rate of reducing rate of clutter of 96 % and over.

• Smart Structures and Systems
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• v.29 no.1
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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• pp.17-23
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• 2006

This paper proposes a novel active frequency drift (AFD) method for the islanding prevention of grid-connected photovoltaic inverter. To detect the islanding phenomenon of grid-connected photovoltaic (PV) inverters concerning about the safety hazards and the damage to other electric equipments, many kinds of anti-islanding methods have been presented. Among them, AFD method using chopping fraction enables the islanding detection to drift up (or down) the frequency of the voltage during the islanding situation. In this paper, injecting the periodic zero current into the basic AFD method is proposed. This proposed method shows the analytical design value of cf to meet the test procedure of IEEE Std. 1547 with various load conditions. Detection of islanding is verified using simulation tool PSIM.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.5
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• pp.341-348
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• 2015

Active infrared thermography methods have been known to possess good fault detection capabilities for the detection of defects in materials compared to the conventional passive thermal infrared imaging techniques. However, the reliability of the technique has been under scrutiny. This paper proposes the lock-in thermography technique for the detection and estimation of artificial subsurface defect size and depth with uncertainty measurement.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.31-37
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• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.2
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• pp.178-185
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• 2002

This paper describes the implementation of three-phase three-wire active power filter system with a instantaneous PSD for distorted and unbalanced power conditions. The positive sequence voltage of the distorted and the unbalanced power system is calculated by the Instantaneous PSD, and phase transformation matrix of the instantaneous power theory is achieved with detected positive sequence voltage. Finally, the proposed method is experimented and tested under unbalanced nonlinear load as well as unbalanced /distorted condition in power system.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.4
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• pp.303-310
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• 2004

In this paper, for compensating dynamic voltage sag and harmonic current, 3-phase hybrid series active power filter based on the space vector detection is proposed. The Space vector algorithm for detecting the voltage sag and the harmonic current in compared with conventional theory is a simple method for calculating the compensating reference without any coordinated transformation. The effectiveness of the proposed system is verified by the PSIM simulation in the steady state and the transient state, which the proposed system is able to simultaneously compensate harmonics and source voltage unbalance / sag.