• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.64.1-64.1
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• 2016

We study the evolution of a late-type galaxy (LTG) in a rich cluster environment by using N-body/SPH simulations. To do that we perform a set of simulations of a LTG falling in a Coma-like cluster and also the LTG colliding with early-type galaxies (ETGs) multiple times in the cluster environment. We use a catalog of the Coma cluster in order to estimate the typical number of collisions and the closest approach distances that a LTG would experience in the cluster. We investigate the cold gas depletion and star formation quenching of our LTG model influenced by the hot cluster gas as well as the hot halo gas of the colliding ETGs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.1069-1084
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• 2011

This paper considers an optimal base station clustering problem for designing a mobile (wireless) communication network. For a given network with a set of nodes (base stations), the problem is to optimally partition the set of nodes into subsets (each called a cluster) such that the associated inter-cluster traffic is minimized under certain topological constraints and cluster capacity constraints. In the problem analysis, the problem is formulated as an integer programming problem. The integer programming problem is then transformed into a binary integer programming problem, for which the associated linear programming relaxation is solved in a column generation approach assisted by a branch-and-bound procedure. For the column generation, both a heuristic algorithm and a valid inequality approach are exploited. Various numerical examples are solved to evaluate the effectiveness of the LP (Linear Programming) based branch-and-bound algorithm.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.287-293
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• 2012

We introduce a methodology for the study of the application-level performance of time-sharing parallel jobs on a set of compute nodes in high performance clusters and report our findings. We assume that parallel jobs arriving at a cluster need to share a set of nodes with the jobs of other users, in that they must compete for processor time in a time-sharing manner and other limited resources such as memory and I/O in a space-sharing manner. Under the assumption, we developed a methodology to simulate job arrivals to a set of compute nodes, and gather and process performance data to calculate the percentage slowdown of parallel jobs. Our goal through this study is to identify a better combination of jobs that minimize performance degradations due to resource sharing and contention. Through our experiments, we found a couple of interesting behaviors for overlapped parallel jobs, which may be used to suggest alternative job allocation schemes aiming to reduce slowdowns that will inevitably result due to resource sharing on a high performance computing cluster. We suggest three job allocation strategies based on our empirical results and propose further studies of the results using a supercomputing facility at the San Diego Supercomputing Center.

• Communications of the Korean Mathematical Society
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• v.26 no.1
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• pp.135-149
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• 2011

Using (r, s)-preopen sets [14] and pre-${\omega}_t$-closures [6], a new kind of covering property $P^t_{({\omega}_r,s)}$-closedness is introduced in a bitopological space and several characterizations via filter bases, nets and grills [30] along with various properties of such concept are investigated. Two new types of cluster sets, namely pre-(${\omega}_r$, s)t-${\theta}_f$-cluster sets and (r, s)t-${\theta}_f$-precluster sets of functions and multifunctions between two bitopological spaces are introduced. Several properties of pre-(${\omega}_r$, s)t-${\theta}_f$-cluster sets are investigated and using the degeneracy of such cluster sets, some new characterizations of some separation axioms in topological spaces or in bitopological spaces are obtained. A sufficient condition for $P^t_{({\omega}_r,s)}$-closedness has also been established in terms of pre-(${\omega}_r$, s)t-${\theta}_f$-cluster sets.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.266-269
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• 2002

This paper explores a fundamental study of acquiring the users' KANSEI information regarding the recognition of shape models. Since there are many differences such as background differences and knowledge differences among users, they will produce different evaluations based on their KANSEI even when an identical shape model is presented. Cluster analysis is proved to be available for catching a group tendency and for constructing a mapping relation between a description of the shape model and the HANSEl database. In order to investigate an analogical relation and a mutual influence in our consciousness, first, we made a questionnaire that asked subjects to represent images having different colors and shape cones by using 4 pairs of adjectives (KANSEI words). Next, based on the cluster analysis of the questionnaire using a fuzzy set theory, we proposed a hypothesis showing how the analogical relation and the mutual influence work in our mind while viewing the shape models. Furthermore, how the properties of KANSEI depend on their descriptions was also investigated by virtue of the cluster analysis. This work will be valuable to construct a personal KANSEI database regarding the Shape Model Processing System.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.199-210
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• 1994

Most of clustering methods usually employ a center or predefined shape of a cluster to assign the input data into the cluster. When there is no information about data set, it is impossible to predict how many clusters are to be or what shape clusters take. (the shape of clusters could not be easily represented by the center or predefined shape of clusters) Therefore, it is difficult to assign input data into a proper cluster using previous methods. In this paper, to overcome such a difficulty a cluster is to be represented as a collection of several subclusters representing boundary of the cluster. And membership functions are used to represent how much input data bllongs to subclusters. Then the position of the nearest subcluster is adaptively corrected for expansion of cluster, which the subcluster belongs to by use of a competitive learning neural network. To show the validity of the proposed method a numerical example is illustrated where FMMC(Fuzzy Min-Max Clustering) algorithm is compared with the proposed method.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.792-796
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• 2009

Determining the optimal number of cluster is an important issue in research area of data clustering. It is choosing the cluster validity method and finding the cluster number where it optimizes the cluster validity. In this paper, an efficient heuristic for determining optimal number of cluster using clustering balance is proposed. The experimental results using k-means at artificial and real-life data set show that proposed algorithm is excellent in aspect of time efficiency.

• Honam Mathematical Journal
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• v.43 no.3
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• pp.417-431
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• 2021

In this study, we introduced the notions of rough statistical convergence and defined the set of rough statistical limit points of a sequence and obtained statistical convergence criteria associated with this set in 2-normed space. Then, we proved that this set is closed and convex in 2-normed space. Also, we examined the relations between the set of statistical cluster points and the set of rough statistical limit points of a sequence in 2-normed space.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• Bulletin of the Korean Chemical Society
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• v.30 no.4
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• pp.794-796
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• 2009

We have studied the structures and stabilities of Au6 to explore the origin of the large discrepancy between relative energies obtained from the density functional theory (DFT) and ab initio correlated levels of theory. The MP2 methods significantly overestimate the stability of the non-planar isomer when the double-$\zeta$ polarization quality of basis sets, such as LANL2DZ+1f and CEP31G+1f, are used. However, we show that such preference for the non-planar structure at the MP2 level mainly originates from the large basis set superposition error.