• 전기학회논문지
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• 제67권8호
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• pp.1113-1118
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• 2018

Protection in HVDC(High Voltage Direct Current) have the very fast velocity of fault detection. Because Fault in HVDC has the fast propagation, large currents, high interruption cost. The focus to velocity caused possibility of errors like a detection error like a high impedance fault. In this paper, Proposed complex composition for get the reliability and velocity. That used SFCL(Super Conducting Fault Current Limiter), Protection Zone and DTS(Distributed Temperature Sensing). The SFCL was detect the fault by quench and DTS&Protection Zone were perceive the detect by variation too. To examine the proposed method, PSCAD/EMTDC simulated. The results of simulation, proposed methods could the detect of fault to whole HVDC line. And that improved the reliability of fault clearing.

• 반도체디스플레이기술학회지
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• 제14권3호
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• pp.23-26
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• 2015

A seam carving based micro-crack detection method is proposed which aims at detecting the micro-crack regions in heterogeneously textured surface of polycrystalline solar cells. By calculating the seam which is a connected path of low energy pixels in the image, the micro-crack regions can be detected. Experimental results show that the proposed seam carving based micro-crack detection method has superior efficiency in detecting the micro-crack without background noise pixels and the algorithm's computation time is less than the conventional algorithm.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.405-407
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• 1994

In order to provide reliable power service and to prevent a potentail hazard and damage, it is important to detect high impedance fault in power distribution line. This paper presents a neural network based approach for the detection of high impedance faults. A time delay neural network has been selected and trained for the fault currents obtained from field experiments. Detection experiments have been performed with the data from four different high impedance surfaces. Experimental results indicated the feasibility of using TDNN for the detection of high impedance faults.

• Smart Structures and Systems
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• 제29권3호
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• pp.421-431
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• 2022

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the sub-blocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 추계학술발표대회
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• pp.124-125
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• 2023

Defect detection during liquid crystal display (LCD) manufacturing has always been a critical challenge. This study aims to address this issue by proposing a data augmentation method based on generative adversarial networks (GAN) to improve defect identification accuracy in LCD production. By leveraging synthetically generated image data from GAN, we effectively augment the original dataset to make it more representative and diverse. This data augmentation strategy enhances the model's generalization capability and robustness on real-world data. Compared to traditional data augmentation techniques, the synthetic data from GAN are more realistic, diverse and broadly distributed. Experimental results demonstrate that training models with GAN-generated data combined with the original dataset significantly improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This study provides an effective data augmentation approach for intelligent quality control in LCD production.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.588-591
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• 2004

In this paper, we propose a ground plane detection algorithm, using a new image processing method (IPD). To extract the ground plane from the color image acquired by monocular camera, we use a new identical pixel detection method (IPD) and an edge detection method. This IPD method decides whether the pixel is identical with the ground plane pixel or not. The IPD method needs the reference area and its performance depends on the reference area size. So we propose the reference area auto-expanding algorithm in accordance with situation. And we evaluated the proposed algorithm by the experiments in the various environments. From the experiments results, we know that the proposed algorithm is efficient in the real indoor environment.

• Journal of Electrical Engineering and Technology
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• 제4권3호
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• pp.389-395
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• 2009

Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 B
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• pp.1165-1168
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• 2001

The effect of sensor faults in DTC based induction motor drives is analyzed and a fault detection problem is treated. An adaptive gain scheduling observer is proposed for the design of DTC controller and a fault detection system. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current, rotor speed that are useful for fault detection purpose. Simulations for various type of sensor faults are performed to evaluate the performance of the overall control system and the proposed sensor fault detection scheme.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.85-88
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• 2009

In this paper, we propose a DRF-based object detection method using the object adaptive patch in the satellite imagery. It is a Discriminative Random Fields (DRF) based work, so the detection is done by labeling to the possible patches in the image. For the feature information of each patch, we use the multi-scale and object adaptive patch and its texton histogram, instead of using the single scale and fixed grid patch. So, we can include contextual layout of texture information around the object. To make object adaptive patch, we use "superpixel lattice" scheme. As a result, each group of labeled patches represents the object or object's presence region. In the experiment, we compare the detection result with a fixed grid scheme and shows our result is more close to the object shape.