• 디지털산업정보학회논문지
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• 제16권2호
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• pp.11-25
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• 2020

This study proposes a method to manipulate fragmentation of disks by arbitrarily allocating and releasing the status of a disk cluster in the NTFS file system. This method allows experiments to be performed in several studies related to fragmentation problems on disk cluster. Typical applicable research examples include testing the performance of disk defragmentation tools according to the state of fragmentation, establishing an experimental environment for fragmented file carving methods for digital forensics, setting up cluster fragmentation for testing the robustness of data hiding methods within directory indexes, and testing the file system's disk allocation methods according to the various version of Windows. This method suggests how a single file occupies a cluster and presents an algorithm with a flowchart. It raises three tricky problems to solve the method, and we propose solutions to the problems. Experiments for allocating the disk cluster to be fragmented to the maximum extent possible, it then performs a disk defragmentation experiment to prove the proposed method is effective.

• The Journal of the Acoustical Society of Korea
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• 제15권1E호
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• pp.29-33
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• 1996

A categorization for cluster is necessary when an unsupervised classfication is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic determination methods were proposed and evaluated. They are four methods. a) maximum number method : which assigns the tharget cluster to the category which occupies the largest area of that cluster b) maximum percentage method : which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster. c) minmun distance method : which assigns the target cluster to the category having minmum distance with that cluster d) element ratio matching method : which assigns local regions to the category having the most similar element ratio of that region From the results of the experiments, it was certified that the result of minimum distance method was almost the same as the result made by a human operator.

• 한국산학기술학회논문지
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• 제11권12호
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• pp.4991-4996
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• 2010

무선 센서 네트워크에서 클러스터링 기법은 클러스터를 형성하여 데이터를 통합한 후 한 번에 전송해서 에너지를 효율적으로 사용하는 기법이다. 클러스터 그룹 모델은 클러스터링에 기반을 두지만 이전의 기법과 달리 클러스터 헤드에 집중된 에너지 과부하를 클러스터 그룹 헤드와 클러스터 헤드로 분산시켜서 전체 에너지 소모량을 줄인다. 본 논문에서는 이러한 클러스터 그룹 모델에서 에너지 소모 모델의 임계값에 따라 최적의 클러스터 그룹 수와 클러스터 수를 구하고 이를 이용하여 센서 네트워크 전체 에너지 소모량을 최소화하고 네트워크 수명을 최대화한다. 실험을 통하여 제안된 클러스터 그룹 모델이 이전의 클러스터링 기법보다 네트워크 에너지 효율이 향상되었음을 보였다.

• 자동차안전학회지
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• 제13권4호
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• International Journal of Internet, Broadcasting and Communication
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• 제9권4호
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• pp.51-58
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• 2017

This paper deals with the improvement of cluster head selection method in L-SEP for heterogeneous nodes among hierarchical routing protocols of wireless sensor network. Wireless sensor networks are classified into homogeneous and heterogeneous network. In heterogeneous network, SEP, L-SEP are mainly used because cluster head selection probability is different depending on node type. But, since protocol based on SEP has different cluster head selection probabilities depending on the node type, clusters that transmit data inefficiently can be formed. to improve this, it is necessary to select the cluster head that minimizes the transmission distance of member node and the cluster head. Therefore, we propose a protocol that improve the cluster head selection method.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.189-194
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• 2017

In this study, we propose a method which is based on "Image segmentation by histogram thresholding using hierarchical cluster analysis"/HCA/ and "A nonparametric approach for histogram segmentation"/NHS/. HCA method uses that all histogram bins are one cluster then it reduces cluster numbers by using distance metric. Because this method has too many clusters, it is more computation. In order to eliminate disadvantages of "HCA" method, we used "NHS" method. NHS method finds all local minima of histogram. To reduce cluster number, we use NHS method which is fast. In our approach, we combine those two methods to eliminate disadvantages of Arifin method. The proposed method is not only less computational than "HCA" method because combined method has few clusters but also it uses local minima of histogram which is computed by "NHS".

• Bulletin of the Korean Chemical Society
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• 제14권5호
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• pp.578-583
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• 1993

By including the overlap integrals between atomic orbitals, the modified cluster orbitals for a metal cluster of face centered cubic lattice are found. The modified analytic solutions of the cluster are obtained from them with the assumption that the cluster orbitals with different state indices do not mix together. The physical properties-the HOMO levels and the unit electronic energies-of Ni, Pd, and Pt clusters of various size, calculated by the modified cluster orbital method, agree better with the results obtained by the Extended Huckel calculation than those of the previous(unmodified) cluster orbital method do. As a result, it is verified that the physical properties, at least those related to the energy levels, obtained by the Extended Huckel method may be reproduced by use of the modified cluster orbital method instead.

• 천문학회보
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• 제39권1호
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• pp.58.2-58.2
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• 2014

The Arches cluster is a young (2-4 Myr), compact (~1 pc), and massive (${\sim}2{\times}10^4M_{\odot}$) star cluster located ~30 pc away from the Galactic center (GC) in projection. Being exposed to the extreme environment of the GC such as elevated temperature and turbulent velocities in the molecular clouds, strong magnetic fields, and larger tidal forces, the Arches cluster is an excellent target for understanding the effects of star-forming environment on the initial mass function (IMF) of the star cluster. However, resolving stars fainter than ~1 $M_{\odot}$ in the Arches cluster partially will have to wait until an extremely large telescope with adaptive optics in the infrared is available. Here we devise a new method to estimate the shape of the low-end mass function where the individual stars are not resolved, and apply it to the Arches cluster. This method involves histograms of pixel intensities in the observed images. We find that the initial mass function of the Arches cluster should not be too different from that for the Galactic disk such as the Kroupa IMF.

• 대한산업공학회지
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• 제37권3호
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• pp.180-190
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• 2011

Efficient energy consumption is a critical factor for deployment and operation of wireless sensor networks (WSNs). To achieve energy efficiency there have been several hierarchical routing protocols that organize sensors into clusters where one sensor is a cluster-head to forward messages received from its cluster-member sensors to the base station of the WSN. In this paper, we propose a self-organized clustering method for cluster-head selection and cluster based routing for a WSN. To select cluster-heads and organize clustermembers for each cluster, every sensor uses only local information and simple decision mechanisms which are aimed at configuring a self-organized system. By these self-organized interactions among sensors and selforganized selection of cluster-heads, the suggested method can form clusters for a WSN and decide routing paths energy efficiently. We compare our clustering method with a clustering method that is a well known routing protocol for the WSNs. In our computational experiments, we show that the energy consumptions and the lifetimes of our method are better than those of the compared method. The experiments also shows that the suggested method demonstrate properly some self-organized properties such as robustness and adaptability against uncertainty for WSN's.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.401-405
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• 2007

센서네트워크에서 효율적인 에너지사용에 관한 클러스터 기반 라우팅 프로토콜이 다양하게 연구되고 있다. 하지만 기존의 클러스터 기반의 라우팅 프로토콜은 클러스터 재구성에 있어 센서 노드들의 불균형적인 에너지 소비문제와 클러스터 헤더를 선정함에 있어 헤더 노드와 싱크 노드가 멀리 떨어져 있을 때 연결이 제대로 이루어지지 않는다는 문제점이 있다. 본 논문에서는 클러스터의 재분할과 헤더 노드의 멀티 홉 전송방식을 제시한다. 클러스터 재분할은 기존의 클러스터를 소규모의 클러스터로 재분할하는 방식이고, 멀티 홉 전송방식은 헤더 노드들 사이의 중계전송에 관한 방식이다. 시뮬레이션을 통하여 제시한 라우팅 기법이 균등한 에너지 소비와 에너지 효율성에 있어서 기존의 라우팅 기법보다 우수함을 보인다.