• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1946-1952
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• 1999

A new image compression method to preserve edge characteristics in reconstructed images using an unsupervised learning neural is proposed in this paper. By the unsupervised competitive learning which generalizes previously proposed Centroid Neural Network(CNN) algorithm with the geometric characteristics of edge area and statistical characteristics of image data, more codevectors are allocated in the edge areas to provide the more accurate edges in reconstructed image. Experimental results show that the proposed method gives improved edge in reconstructed images when compared with SOM, Modified SOM and M/R-CNN.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.197-201
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• 2004

We describe a new edge detector based on the robust rank-order (RRO) test which is a useful alternative to Wilcoxon test, using $r{\times}r$ window for detecting edges of all possible orientations in noisy images. Some experiments of statistical edge detectors based on the Wilcoxon test and T test with our RRO detector are carried out on synthetic and real images corrupted by both Gaussian and impulse noise. We also implement these edge detectors using Java on the Web.

• Bulletin of the Korean Mathematical Society
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• v.54 no.3
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• pp.763-787
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• 2017

A graph is called 1-planar if it can be drawn in the Euclidean plane ${\mathbb{R}}^2$ such that each edge is crossed by at most one other edge. The weight of an edge is the sum of degrees of two ends. It is known that every planar graph of minimum degree ${\delta}{\geq}3$ has an edge with weight at most 13. In the present paper, we show the existence of edges with weight at most 25 in 3-connected 1-planar graphs.

• Journal of the Chungcheong Mathematical Society
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• v.18 no.1
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• pp.73-80
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• 2005

The aim of the paper is to study the connectivity and the edge-connectivity of inserted graph I(G) of a graph G with the help of connectivity and the edge-connectivity of that graph G.

• Electronics and Telecommunications Trends
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• v.37 no.5
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• pp.44-53
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• 2022

The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.147-157
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• 2007

Edge detection has been widely used in computer vision and image processing. We describe a new edge detector based on the robust rank-order test which is a useful alternative to Wilcoxon test. Our method is based on detecting pixel intensity changes between two neighborhoods with a $r{\times}r$ window using an edge-height model to perform effectively on noisy images. Some experiments of our robust rank-order detector with several existing edge detectors are carried out on both synthetic images and real images with and without noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.401-404
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• 2004

Transfer characteristics of poly-Si TFTs within process window of laser energy are investigated. In terms of surface morphology and transfer characteristics, process window of laser crystallization is evaluated. While maximum mobility exists in lower edge of process window in n-channel TFTs, maximum mobility exists in higher edge of process window in p-channel TFTs.

• 한국초전도학회:학술대회논문집
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• v.9
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• pp.147-151
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• 1999

YBCO step-edge Josephson junction were fabricated on sapphire substrates. The steps were formed on R-plane sapphire substrates by using Ar ion milling with PR masks. The step angle was controlled in the wide range from 25$^{\circ}$ to 50$^{\circ}$ by adjusting both the Ar ion incident angle and the photoresist mask rotation angle relative to the incident Ar ion beam. CeO$_2$ buffer layer and in-situ YBa$_2Cu_3O_{7-{\delta}}$ (YBCO) thin films was deposited on the stepped R-plane sapphire substrates by pulsed laser deposition method. The YBCO film thickness was varied to obtain the ratio of film thickness to step height in the range from 0.5 to 1. The step edge junction exhibited RSJ-like behaviors with I$_cR_n$ product of 100 ${\sim}$ 300 ${\mu}$V, critical current density of 10$^3$ ${\sim}$ 10$^5$ A/ cm$^2$ at 77 K.

• Journal of the Korean Wood Science and Technology
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• v.19 no.4
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• pp.43-51
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• 1991

This study was carried out to determine the withdrawal strength of various screws according to root diameter of screw and embeded length on the face and edge of domestic particleboard and medium density fiberboard. The obtained results were as follows: 1. The withdrawal strength of screw in domestic particleboard and medium density fiberboard was closely related to embeded length of the screw but less dependent on root diameter of the screw. 2. The withdrawal strength on the face and edge of domestic particleboard could be predicted by means of the following expression: $F_{Pf}=4.60{\times}D^{0.24}{\times}L^{1.14}(R^2=0.87)$ $F_{Pe}=0.54{\times}D^{0.43}{\times}L^{1.73}(R^2=0.84)$ Where: $F_{Pf}$ : withdrawal strength on the face of particleboard(kgf) $F_{Pe}$=withdrawal strength on the edge of particleboard(kgf) D=diameter of the screw(mm) L=embeded length(mm) 3. The withdrawal strength on the face and edge of domestic medium density fiberboard could he predicted by means of the following expression: $FM_f=1.53{\times}D^{0.53}{\times}L^{1.39}(R^2=0.93)$ $F_{Me}=1.14{\times}D^{0.66}{\times}L^{1.36}(R^2=0.87)$ where: $F_{Mf}$ = withdrawal strength on the face of medium density fiberboard(kgf) $F_{Mf}$=withdrawal strength on the edge of medium density fiberboard(kgf).