• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.392-393
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• 2012

This paper proposes a new document clustering method using clustering and Wikipedia. The proposed method can well represent the concept of cluster topics by means of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of Wikipedia. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.269-285
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• 2011

Caching of frequently accessed data in mobile ad-hoc networks is a technique that can improve data access performance and availability. Cooperative caching, which allows sharing and coordination of cached data among several clients, can further enhance the potential of caching techniques. In this paper, we propose a cluster-based fuzzy cooperative caching method for mobile ad-hoc networks. The performance of the proposed caching method is evaluated through an analytical model and is compared to that of other cooperative caching methods.

• Journal of the Korean Magnetics Society
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• v.22 no.4
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• pp.121-124
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• 2012

We have studied electronic and magnetic structure of cubane-type Mn4 cluster using OpenMX method based on density functional method. The calculated density of states shows that the octahedron of O atoms split $e_g$ and $t_{2g}$ energy levels like bulk MnO with cubic structure. Total energy with antiferromagnetic spin configuration is lower than those of other spin configurations because of super exchange interaction. Calculated exchange interaction J between Mn atoms with anti-parallel spin is larger than between Mn atoms with parallel spin.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.179-184
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• 2013

Traditional clustering methods are usually based on the bag-of-words (BOW) model. A disadvantage of the BOW model is that it ignores the semantic relationship among terms in the data set. To resolve this problem, ontology or matrix factorization approaches are usually used. However, a major problem of the ontology approach is that it is usually difficult to find a comprehensive ontology that can cover all the concepts mentioned in a collection. This paper proposes a new document clustering method using semantic features and fuzzy relations for solving the problems of ontology and matrix factorization approaches. The proposed method can improve the quality of document clustering because the clustered documents use fuzzy relation values between semantic features and terms to distinguish clearly among dissimilar documents in clusters. The selected cluster label terms can represent the inherent structure of a document set better by using semantic features based on non-negative matrix factorization, which is used in document clustering. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.211-222
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• 2006

The cluster system with shared-nothing storage cannot escape from the problem of skewed request toward specific contents. This paper, therefore, suggests streaming method using MDC (Multiple Description Coding) instead of using single original content; this method is able to cope with skewed request in shared-nothing storage server as well as to continue to provide services in case of the system failure. Also, the system can support adaptive streaming service according to user player type, network status, the load of server, and client.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.605-618
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• 2007

Adaptive sampling design is the selection procedure which depends on observed values of the variable of interest. It is the method which could be applied to the rare and unapproachable population. Adaptive cluster sampling strategies are more efficient than simple random sampling on equivalent sample size. Adaptive sampling with new estimators through the Rao-blackwell method have lower variance than Horvitz-Thompson (HT) and Hansen-Hurwitz (HH). Also, to determine suitable sample size, it was used expected sample and the method finding appropriate sample size by changing initial sample size were studied.

• Bulletin of the Korean Chemical Society
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• v.21 no.10
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• pp.953-954
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• 2000

The equilibrium geometrical structures of silver atom clusters at their electronic ground states have been theo-retically determined by using the nonrelativistic semiempirical INDO/1 method. The clusters investigated are Agn, Agn+, and Agn- (n = 7 , 8, 9). In order to find the most stable structure, i.e., the global minimum in energy hypersurface, geometry optimization and energy calculation processes have been repeatedly performed for all the possible graphical models by changing the bond parameters (resonance integral values). The heptamers are pentagonal bipyramidal-Ag7(D5h), Ag7+ (D5h), Ag7- (D5h); the octamers are pentagonal bipyramidal with one atom capped-Ag8(D2d), Ag8+ (Cs), Ag8- (D2d); the nonamers are pentagonal bipyramidal with two atoms capped -Ag9(C2v), Ag9+ (C2v), Ag9- (C2v). Our structures are in good agreement with those by ab initio calculations ex-cept for the anionic Ag9- cluster. And it is noted that the INDO/1 method can accurately predict the Ag cluster geometries when a proper set of bond parameters is used.

• Journal of IKEEE
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• v.23 no.2
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• pp.419-424
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• 2019

The FCM algorithm finds the optimal solution through iterative optimization technique. In particular, there is a difference in execution time depending on the initial center of clustering, the location of noise, the location and number of crowded densities. However, this method gradually updates the center point, and the center of the initial cluster is shifted to one side. In this paper, we propose a TI-FCM(Triangular Inequality-Fuzzy C-Means) clustering algorithm that determines the cluster center density by maximizing the distance between clusters using triangular inequality. The proposed method is an effective method to converge to real clusters compared to FCM even in large data sets. Experiments show that execution time is reduced compared to existing FCM.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.4
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• pp.293-308
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• 2012

The shear behavior at the particle/surface interface such as rock joint can determine the mechanical behavior of whole structure. Therefore, a fundamental understanding of the mechanisms governing its behavior and accurately estimation of the interface strength is essential. In this paper, PFC, a numerical analysis program of discrete element method was used to investigate the effects of the surface roughness on interface strength. The surface roughness was characterized by smooth, intermediate, and rough surface, respectively. In order to investigate the effects of particle shape and crushing on particle/surface interface behavior, one ball, clump, and cluster models were created and their results were compared. The shape of particle was characterized by circle, triangle, square, and rectangle, respectively. The results showed that as the surface roughness increases, interface strength and friction angle increase and the void ratio increases. The one ball model with smooth surface shows lower interface strength and friction angle than the clump model with irregular surface. In addition, a cluster model has less interface strength and friction angle than the clump model. The failure envelope of the cluster model shows non-linear characteristic. From these findings, it is verified that the surface roughness and particle shape effect on the particle/surface interface shear behavior.