• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.51-56
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• 2018

This paper proposes the efficient preprocessing method based on curvelet transform for edge enhancement in image. The propose method is generated the edge map by using the Canny algorithm to wavelet transform, which is the sub-step of the curvelet transform. In order to improve the part of edge feature, the selective sharpening according to the generate edge map is applied. In experimental result, the propose method achieves that the enhancement of edge feature is better than conventional methods. This leads that peak to signal noise ratio, edge intensity are improvement on average about 1.92, 1.12dB respectively.

• Proceeding of EDISON Challenge
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• 2016.11a
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• pp.19-22
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• 2016

Leading edge thrust is generally caused by passing air flow from lower to upper surface and it is required to have sufficient angle of attack for notable leading edge thrust. To produce leading edge thrust at low angle of attack, utilizing expansion wave accompanying low pressure is able to be a solution. Fore structure changes the direction of flow, and this flow passes the projected edge. As a result, from a perspective of the edge, it is able to have high angle of attack, and artificial expansion wave is generated. This concept shows 9.48% increase of L/D in inviscid flow, at Mach number 1.3 and angle of attack $1^{\circ}$ in maximum, and this model shows the 3.98% of increasement at angle of attack $2^{\circ}$. Although advantage of the artificial expansion wave decreased as angle of attack increase, it shows the possibility of aerodynamical improvement with artificial expansion wave.

• Smart Structures and Systems
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• v.28 no.6
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• pp.779-789
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• 2021

Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.183-184
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• 2011

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.89-96
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• 2009

In this paper, we propose an efficient deinterlacing algorithm which is an edge dependent interpolation based on edge direction refinement. The conventional edge dependent interpolation algorithms have a visually better performance than any other Intra-field deinterlacing algorithms. However they are very sensitive to noise due to the failure of estimating edge direction. In order to exactly detect edge direction, our method detects edge direction of around interpolated pixel and refines the edge direction using weighted median filter. Simulation results have shown the efficacy of the proposed method with significant improvement over the previous methods in terms of the objective PSNR quality as well as the subjective image quality.

• Design & Manufacturing
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• v.16 no.1
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• pp.50-54
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• 2022

Coverglass of electronic equipments is thinner and slimmer, so the glass must have good bending strength. In these days, the polishing edge of glass is used by solid tool like grinding wheel. But solid tool leave micro crack or edge chipping in edge of glass. MR polishing is an optimal method by polishing edge of glass. MR polishing is used MR fluid that is a liquid tool. MR polishing doesn't leave tool path or residual stress, micro crack and edge chipping unlike grinding wheel polishing. In this paper, the results of grinding and MR polishing were compared and analyzed to improve bending strength by minimizing edge chipping of cover glass. It was derived that the depth and size of cracks have a significant influence on the bending strength of the glass edge. The edges of the glass using MR grinding were analyzed to have a better surface and higher bending strength than the glass using abrasive wheel grinding. It was confirmed that MR polishing had an effect on strength improvement by effectively removing cracks in the specimen.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.385-388
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• 2013

In this paper, we show the weakness of the image matching method by using MPEG-7 EHD(Edge Histogram Descriptor) and suggest how to improve this weakness by using image normalization. EHD algorithm is an image matching technique that collects edge's slope of distribution and same distribution. However, the EHD error rate is high because EHD is sensitive for changes of object distortion and rotation that will be matched. We improve matching performance by accurately extract edge information in image by using normalization. We compare and analyze the normalized EHD algorithm by using distortion and rotation and it shows robustness for changes of the size and rotation.

• Journal of the Semiconductor & Display Technology
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• v.15 no.1
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• pp.56-64
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• 2016

It is generally accepted that the surface quality of wafer edge is mostly damaged by the vibrations of the edge grinding machine. The surface quality of wafer edge is supposed to be the most dominant factor of the cracks, scratches, burrs and chips on the edge surfaces, which are the main defects of the wafers. In this study, the structure of a wafer edge grinder has been investigated through the frequency response experiment and the computer simulation to find ways to suppress the vibrations from the structure. The main reasons of the structural vibrations were analyzed. And further the design alterations were deduced from the results of the experiment and the simulation, and applied to the machine to check the effects of those alterations and to eventually improve the structural stability. The result shows that the machine can have much improved stability with relatively simple design changes.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.257-270
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• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.257-265
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• 2009

The present study deals with the leading edge shape on a wing-body junction to decrease a horseshoe vortex, one of the main factors to generate the secondary flow losses. The shape of leading-edge is optimized with design variables form the leading-edge shape. Approximate optimization design method is used for the optimization. The study is investigated using $FLUENT^{TM}$ and $iSIGHT^{TM}$. As the result, total pressure coefficient of the optimized design case was decreased about 9.79% compare to the baseline case.