• Journal of Korean Port Research
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• v.11 no.2
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• pp.295-304
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• 1997

For evaluation of maritime power, the attributes of sea power are identified in this paper by system analysis method. A many fundmental factors of sea power are selected by survey of the extensive and thorough literatures on maritime power. And the factors are classified into 11 standard attributes by cluster method. The 11 standard attributes are as follows: geographical condition, character of territory, character of the people, maritime will of the government, shipping power, navel power, shipbuilding power, fishing power, ocean research and development, dependence on seaborne trade, number of ocean population. As the sub-attributes of the standard attributes, 37 composite factors and some basic factors are defined through careful survey and discussion with some experts. As the result of this study, the maritime power is systematically identified as maritime power system. And it is realized that the evalution of maritime power system is the hybrid MADM problem with both quantitative and qualitative factors.

• Annual Conference of KIPS
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• 2023.11a
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• pp.322-326
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• 2023

We propose a method of movie recommendation that involves an algorithm known as spectral bipartition. The Social Network is constructed manually by considering the similar movies viewed by users in MovieLens dataset. This kind of similarity establishes implicit ties between viewers. Because we assume that there is a possibility that there might be a connection between users who share the same set of viewed movies. We cluster users by applying a community detection algorithm based on the spectral bipartition. This study helps to uncover the hidden relationships between users and recommend movies by considering that feature.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.93-96
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• 2024

The segmentation, detection, and extraction of infected tumour area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated many algorithm methods are available in medical imaging amongst them the Threshold technique brain tumour segmentation process gives an accurate result than other methods for MR images. The proposed method compare with the K-means clustering methods, it gives a cluster of images. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, process time and similarity of the segmented part. The experimental results achieved more accuracy, less running time and high resolution.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.16-26
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• 2024

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

• Journal of the Korean Geographical Society
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• v.43 no.3
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• pp.392-411
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• 2008

The purpose of this paper is to develop a method for exploring hotspot patterns of house price volatility where there is a high fluctuation in price and homogeneity of direction of price volatility. These patterns are formed when the majority of householders in an area show an adaptive tendency in their decision making. This paper suggests a method that consists of two analytical parts. The first part uses spatial scan statistics to detect spatial clusters of houses with a positive range of price volatility. The second part utilizes local Moran's I to evaluate the homogeneity of direction of price volatility within each cluster. The method is applied to the areas of Gangnam-Gu, Seocho-Gu, and Songpa-Gu in Seoul from August to November of 2003; the Participatory Government of Korea designated these areas and this period as the most speculative. The results of the analysis show that the area around Gaepo-Dong was as a hotspot before the Government's anti-speculative 10.29 policy in 2003; the house prices in the same area stabilized in October, 2003 and the area was identified as a coldspot in December, 2003. This case study shows that the suggested method enables exploration of hotspot of house price volatility at micro spatial scales which had not been detected by visual analysis.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.533-542
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• 2017

Novels include various character names, depending on the genre and the spatio-temporal background of the novels and the nationality of characters. Besides, characters and their names in a novel are created by the author's pen and imagination. As a result, any proper noun dictionary cannot include all kinds of character names. In addition, the novels translated into Korean have character names consisting of two or more nouns (such as "Harry Potter"). In this paper, we propose a method to extract noun phrases for character names and to cluster the noun phrases into coreferents for the same character name. In the extraction of noun phrases, we utilize KKMA morpheme analyzer and CPFoAN character identification tool. In clustering the noun phrases into coreferents, we construct a directed graph with the character names extracted by CPFoAN and the extracted noun phrases, and then we create name sets for characters by traversing connected subgraphs in the directed graph. With four novels translated into Korean, we conduct a survey to evaluate the proposed method. The results show that the proposed method will be useful for speaker identification as well as for constructing the social network of characters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.347-354
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• 2013

In this paper, we propose a enhancing document clustering method using term re-weighting by the expanded term. The proposed method extracts the important terms of documents in cluster using semantic features, which it can well represent the topics of document to expand term using WordNet. Besides, the method can improve the performance of document clustering using re-weighting terms based on the expanded terms. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than the normal document clustering methods.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.399-404
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• 2010

In this paper, we propose a method for precision improvement based on core clusters and term proximity. The method is composed by three steps. The initial retrieval documents are clustered based on query term combination, which occurred in the document. Core clusters are selected by using proximity between query terms. Then, the documents in core clusters are reranked based on context information of query. On TREC AP test collection, experimental results in precision at the top documents(P@100) show that the proposed method improved 11.2% over the language model.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.55-58
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• 2002

Parallel finite element code has been recently developed for the analysis of the incompressible Wavier-Stokes equations using domain decomposition method. Metis and MPI libraries are used for the domain partitioning of an unstructured mesh and the data communication between sub-domains, respectively. For unsteady computation of the incompressible Navier-Stokes equations, 4-step splitting method is combined with P1P1 finite element formulation. Smagorinsky and dynamic model are implemented for the simulation of turbulent flows. For the validation performance-estimation of the developed parallel code, three-dimensional Laplace equation has been solved. It has been found that the speed-up of 40 has been obtained from the present parallel code fir the bench mark problem. Lastly, the turbulent flows around the MIRA model and Tiburon model have been solved using 32 processors on IBM SMP cluster and unstructured mesh. The computed drag coefficient agrees better with the existing experiment as the mesh resolution of the region increases, where the variation of pressure is severe.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.