• Journal of Information Processing Systems
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• v.17 no.1
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• pp.63-74
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• 2021

According to the double-phase cluster-head election method (DCE), the final cluster heads (CHs) sometimes are located at the edge of cluster. They have a long distance from the base station (BS). Sensor data is directly transmitted to BS by CHs. This makes some nodes consume much energy for transmitting data and die earlier. To address this problem, energy efficient multi-hop cluster-head election strategy (EEMCE) is proposed in this paper. To avoid taking these nodes far from BS as CH, this strategy first introduces the distance from the sensor nodes to the BS into the tentative CH election. Subsequently, in the same cluster, the energy of tentative CH is compared with those of other nodes, and then the node that has more energy than the tentative CH and being nearest the tentative CH are taken as the final CH. Lastly, if the CH is located at the periphery of the network, the multi-hop method will be employed to reduce the energy that is consumed by CHs. The simulation results suggest that the proposed method exhibits higher energy efficiency, longer stability period and better scalability than other protocols.

• Journal of Web Engineering
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• v.21 no.1
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• pp.27-52
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• 2021

With recent increases in the number of network-connected devices, the number of edge computing services that provide similar functions has increased. Therefore, it is important to recommend an optimal edge computing service, based on quality-of-service (QoS). However, in the real world, there is a cold-start problem in QoS data: highly sparse invocation. Therefore, it is difficult to recommend a suitable service to the user. Deep learning techniques were applied to address this problem, or context information was used to extract deep features between users and services. However, edge computing environment has not been considered in previous studies. Our goal is to predict the QoS values in real edge computing environments with improved accuracy. To this end, we propose a GAIN-QoS technique. It clusters services based on their location information, calculates the distance between services and users in each cluster, and brings the QoS values of users within a certain distance. We apply a Generative Adversarial Imputation Nets (GAIN) model and perform QoS prediction based on this reconstructed user service invocation matrix. When the density is low, GAIN-QoS shows superior performance to other techniques. In addition, the distance between the service and user slightly affects performance. Thus, compared to other methods, the proposed method can significantly improve the accuracy of QoS prediction for edge computing, which suffers from cold-start problem.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.277-278
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• 2021

본 논문에서는 멀티클러스터 기반 협업 엣지 네트워크 모델과 지능형 협업 엣지 자원 할당을 위한 정책 생성기 모델을 제안한다. 최근 K8s 기반 클러스터 엣지 시스템 개발이 활발히 진행 중이다. 본 논문에서는 이런 클러스터 기반 엣지 시스템을 네트워크를 통해 멀티클러스터 기반 엣지 시스템으로 구성하는 모델을 제시하고 이 네트워크 모델에서 협업으로 엣지 자원을 할당 할 수 있는 지능형 자원 할당 정책 생성기 구조 및 방법 등을 제안한다.

• Journal of The Korean Astronomical Society
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• v.28 no.2
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• pp.177-196
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• 1995

We present UBV CCD photometry of the double cluster NGC 1850 located at the NW edge of the bar of the Large Magellanic Cloud. The color-magnitude diagram shows that NGC 1850 has a prominent population of massive core-He burning stars which is incomparably richer than any other known star clusters. The reddening is estimated from the (U-B) - (B-V) diagram to be E(B - V) = $0.15{\pm}0.05$. We have estimated the ages of NGC 1850 and a very compact blue star cluster (NGC 1850A) located at ${\sim}30''$ west of NGC 1850 using isochrones based on the convective overshooting models: $80{\sim}10$ Myrs and $5{\sim}2$ Myra, respectively. Several evidence suggest that it is probably the compact cluster NGC 1850A that is responsible for the arc-shaped nebulosity (Henize N 103B) surrounding the east side of NGC 1850.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.90-95
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• 2022

This paper proposes a method to find a tree with the maximum number of terminals that can be connected by a given length when numerous terminals distributed in a cluster form are given to the Euclidean plane R2 with several constraints. First constraint is that a given terminal is distributed in a cluster form, second is that a given length cannot connect all terminals in the tree, and third is that there is no curved connection between each terminal. This paper proposes a method to establish more efficient interconnections within terminals distributed in a cluster form by improving a randomly distributed memetic genetic algorithm. The construction of interconnections has been extensively used in design-related fields, from networking to architecture. Additionally, in real life, the construction of interconnections is mostly distributed in the form of clusters. Therefore, the heuristic algorithm proposed in this paper can be effectively utilized in real life and is expected to provide various cost savings.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.58-69
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.700-702
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• 2004

본 논문에서는 내용 기반의 화상 검색 시스템을 위한 화상 특징 추출을 고속으로 수행하기 위하여 TCP/IP 프로토콜을 사용하는 LAN 환경에서 유휴(Idle) PC들을 사용한 PC 클러스터에 관해 연구하였다. 실험에 사용한 화상 특징(Image feature)으로서는 칼라의 응집도를 사용하는 CCV(Color Coherence Vector), 화상의 엔트로피를 정량화한 PIM(Picture Information Measure), Gaussian-Laplacian 에지 검출 연산을 사용한 SEV(Spatial Edge Histogram Vector)로서 이들을 추출하기 위한 Task를 Master rude에서 Slave rude들로 전송하고, 연산에 사용 될 화상 데이터를 전송한 후 연산을 수행하고 결과를 다시 Master node로 전송하는 전통적인 Task-Farming형태의 PC Cluster를 구성하였다. 연산에 참여하는 클러스터 노드의 개수를 증가시키며 Task와 화상데이터를 전송하여 이에 따른 연산시간을 측정하고 비교하였다. 실험 결과는 유휴 PC들로 구성된 PC클러스터를 이용한 효율적인 내용기반의 화상 검색 시스템을 구성하기 위해 활용이 가능하다.

• Bulletin of the Korean Chemical Society
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• v.14 no.2
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• pp.225-234
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• 1993

As an application of the cluster orbitals proposed previously, nickel-, palladium-, and platinum-hydrogen systems are studied. Density of states, projected density of states, HOMO levels, and stabilization energies are calculated and compared with those obtained by extended Huckel method for small clusters. These calculations are extended to large clusters to find the size dependence of several physical quantities. Reduced overlap populations are also calculated to clarify the charge transfer phenomena reported earlier. The calculated physical quantities show no dependence on the cluster size. It is also found that the charge transfer occurs due to the intrinsic character of palladium, not due to the edge effect which may be present in small clusters.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.101-108
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• 2010

Many works on energy-efficient cluster event detection schemes have been done considering the energy restriction of sensor networks. The existing cluster event detection schemes transmit only the boundary information of detected cluster event nodes to the base station. However, If the range of the cluster event is widened and the distribution density of sensor nodes is high, the existing cluster event detection schemes need high transmission costs due to the increase of sensor nodes located in the event boundary. In this paper, we propose an energy-efficient cluster event detection scheme using the minimum boundary polygons (MBP) that can compress and summarize the information of event boundary nodes. The proposed scheme represents the boundary information of cluster events using the MBP creation technique in the large scale of sensor network environments. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through the performance evaluation. Simulation results show that our scheme maintains about 92% accuracy and decreases about 80% in energy consumption to detect the cluster event over the existing schemes on average.

• Smart Media Journal
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• v.6 no.4
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• pp.32-40
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• 2017

In this paper, a robust text detection method based on edge enhanced contrasting extremal region (CER) is proposed using stroke width transform (SWT) and tensor voting. First, the edge enhanced CER extracts a number of covariant regions, which is a stable connected component from input images. Next, SWT is created by the distance map, which is used to eliminate non-text regions. Then, these candidate text regions are verified based on tensor voting, which uses the input center point in the previous step to compute curve salience values. Finally, the connected component grouping is applied to a cluster closed to characters. The proposed method is evaluated with the ICDAR2003 and ICDAR2013 text detection competition datasets and the experiment results show high accuracy compared to previous methods.