• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.681-691
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• 2006

Clustering is one of widely used methods for grouping thousands of genes by their similarities of expression levels, so that it helps to analyze gene expression profiles. This method has been used for identifying the functions of genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles because single gene might involve multiple genetic functions. Clustering methods, however, have the problems that they are sensitive to initialization and can be trapped into local optima. To solve these problems, this paper proposes an evolutionary fuzzy clustering method, where adaptive a-cut based evaluation is used for the fitness evaluation to apply different criteria considering the characteristics of datasets to overcome the limitation of Bayesian validation method that applies the same criterion to all datasets. We have conducted experiments with SRBCT and yeast cell-cycle datasets and analyzed the results to confirm the usefulness of the proposed method.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.253-262
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• 2005

Local accounting system used by UNIX-like operating system provides the accounting information of processes that are in single host. But it is impossible for this local accounting system to record the total resource consumption data of all processes for doing the same job simultaneously. In this paper, we implement accounting system to measure and manage resource usage for MPI(Message Passing Interface) job in the clustering environment. We designed and implemented the accounting system which measure resource usage of each process runs on a cluster node and record the interconnection information of the entire set of processes across network. Also we implemented accounting system which collect the resource usage data of process in the local accounting system and generate the job-level accounting information. Finally, to evaluate the resource consumption data measured by this accounting system we compare with the data collected by local scheduler that widely used in large scale clustering environment.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.123-127
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• 2006

TIM barrel domain is widely studied since it is one of most common structure and mediates diverse function maintaining overall structure. TIM barrel domain's function is determined by local structural environment at the C-terminal end of barrel structure. We classified TIM barrel domains by local structural alignment tool, LSHEBA, to understand characteristics of TIM barrel domain's functionalvariation. TIM barrel domains classified as the same cluster share common structure, function and ligands. Over 80% of TIM barrels in clusters share exactly the same catalytic function. Comparing clustering result with that of SCOP, we found that it's important to know local structural environment of TIM barrel domains rather than overallstructure to understand specific structural detail of TIM barrel function. Non TIM barrel domains were associated to make different domain combination to form a different function. The relationship between domain combination, we suggested expected evolutional history. We finally analyzed the characteristics of amino acids around ligand interface.

• IE interfaces
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• v.18 no.4
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• pp.504-511
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• 2005

The NP-hard channel assignment problem becomes more and more important to use channels as efficiently as possible because there is a rapidly growing demand and the number of usable channel is very limited. The hybrid genetic and local search (HGLS) method in this paper is a hybrid method of genetic algorithm with no interference channel assignment (NICA) in clustering stage for diversified search and local search in tuning stage when the step of search is near convergence for minimizing blocking calls. The new representation of solution is also proposed for effective search and computation for channel assignment.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.325-336
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• 2023

In August 2022, even though flood damage occurred in the metropolitan area due to heavy rain, drought warnings were issued in Jeolla province, which indicates that the regional drought is intensified recent years. To cope with regarding intensified regional droughts, many studies have been conducted to identify spatial patterns of the occurrence of meteorological drought, however, case studies of spatial clustering for water shortage are not sufficient. In this study, using the estimations of water shortage in the Han River Basin in 2030 of the Master Plans for National Water Management, the spatial characteristics of water shortage were analyzed to identify the hotspot areas based on the Local Moran's I and Getis-Ord Gi^*, which are representative indicators of spatial clustering analysis. The spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The overall results of for three anayisis periods (S0(1967-1983), S1(1984-2000), S2(2001-2018)) indicated that the lower Imjin River (#1023) was the hotspot for water shortage, and there are moving patterns of water shortage from the east of lower Imjin River (#1023) to the west during S2 compared to S0 and S1. In addition, the Yangyang-namdaecheon (#1301) was the HL area that is adjacent to a high water shortage area and a low water shortage area, and had water shortage pattern in S2 compared to S0 and S1.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.255-262
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• 2006

$\underline{Purpose}$: To evaluate the pathological prognostic factors related to local recurrence after radical surgery and adjuvant radiation therapy in advanced rectal cancer. $\underline{Materials\;and\;Methods}$: Fifty-four patients with advanced rectal cancer who were treated with radical surgery followed by adjuvant radiotherapy and chemotherapy between February 1993 and December 2001 were enrolled in this study. Among these patients, 14 patients experienced local recurrence. Tissue specimens of the patients were obtained to determine pathologic parameters such as histological grade, depth of invasion, venous invasion, lymphatic invasion, neural invasion and immunohistopathological analysis for expression of p53, Ki-67, c-erb, ezrin, c-met, phosphorylated S6 kinase, S100A4, and HIF-1 alpha. The correlation of these parameters with the tumor response to radiotherapy was statistically analyzed using the chi-square test, multivariate analysis, and the hierarchical clustering method. $\underline{Results}$: In univariate analysis, the histological tumor grade, venous invasion, invasion depth of the tumor and the over expression of c-met and HIF-1 alpha were accompanied with radioresistance that was found to be statistically significant. In multivariate analysis, venous invasion, invasion depth of tumor and over expression of c-met were also accompanied with radioresistance that was found to be statistically significant. By analysis with hierarchical clustering, the invasion depth of the tumor, and the over expression of c-met and HIF-1 alpha were factors found to be related to local recurrence. Whereas 71.4% of patients with local recurrence had 2 or more these factors, only 27.5% of patients without local recurrence had 2 or more of these factors. $\underline{Conclusion}$: In advanced rectal cancer patients treated by radical surgery and adjuvant chemo-radiation therapy, the poor prognostic factors found to be related to local recurrence were HIF-1 alpha positive, c-met positive, and an invasion depth more than 5.5 mm. A prospective study is necessary to confirm whether these factors would be useful clinical parameters to measure and predict a radio-resistance group of patients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.100-109
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• 2015

Mobile phone carriers face challenges from three synergistic dimensions: Wireless, social, and mobile. Despite significant advances that have been made about social networks and human mobility, respectively, our knowledge about the interplay between two layers remains largely limited, partly due to the difficulty in obtaining large-scale datasets that could offer at the same time social and mobile information across a substantial population over an extended period of time. In this paper, we take advantage of a massive, longitudinal mobile phone dataset that consists of human mobility and social network information simultaneously, allowing us to explore the impact of human mobility patterns on the underlying social network. We find that human mobility plays an important role in shaping both local and global structural properties of social network. In contrast to the lack of scale in social networks and human movements, we discovered a characteristic distance in physical space between 10 and 20 km that impacts both local clustering and modular structure in social network. We also find a surprising distinction in trajectory overlap that segments social ties into two categories. Our results are of fundamental relevance to quantitative studies of human behavior, and could serve as the basis of anchoring potential theoretical models of human behavior and building and developing new applications using social and mobile technologies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

• Journal of KIISE
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• v.41 no.11
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• pp.958-966
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• 2014

Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.