• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.179-198
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• 1999

This study concerns logistics support analysis record(LASR) which provides guaranteed support, and is needed in various weapon systems development and acquisition to develop the optimal factor for integrated logistics support(ILS) by reflecting the results of logistics support analysis(LSA). We observe and analyze the logistics management information(LMI) system used in the United States under the integrated data environment(IDE), and analyze the differences between logistics management information system and the LOADERS(logistic support analysis data entry and retrieval system) which is currently used in Korea. Based on the analyzed results, a improvement model that corresponds to reality and can be applied is presented.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.697-705
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• 2014

For enhancing network efficiency, content-centric networking (CCN) allows network nodes to temporally cache a transmitted response message(Data) and then to directly respond to a request message (Interest) for previously cached contents. Also, CCN is designed to utilize a hierarchical content-name for transmitting Interest/Data instead of a host identity like IP address. This content-name included in Interest/Data reveals both content information itself and the structure of network domain of a content source which is needed for transmitting Interest/Data. To make matters worse, This content-name is human-readable like URL. Hence, through analyzing the content-name in Interest/Data, it is possible to analyze the creator of the requested contents. Also, hosts around the requester can analyze contents which are asked by the requester. Hence, for securely implementing CCN, it is essentially needed to make the content-name illegible. In this paper, we propose content-name encoding schemes for CCN so as to make the content-name illegible and evaluate the performance of our proposal.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.54-61
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• 2023

The family tree is one of the key elements of humanities classics research and is very important for accurately understanding people or families. In this paper, we introduce a method for automatically generating a family tree using information on interpersonal relationships (IIPR) from the Korean Classics Database (KCDB) and visualize interpersonal searches within a family tree using data-driven document JavaScript (d3.js). To date, researchers of humanities classics have wasted considerable time manually drawing family trees to understand people's influence relationships. An automatic family tree builder analyzes a database that visually expresses the desired family tree. Because a family tree contains a large amount of data, we analyze the performance and bottlenecks according to the amount of data for visualization and propose an optimal way to construct a family tree. To this end, we create an n-ary tree with fake data, visualize it, and analyze its performance using simulation results.

• Journal of KIISE
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• v.43 no.2
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• pp.135-142
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• 2016

Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.85-91
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• 2015

With the increasing popularity of Smart Grid infrastructure, it is much easier to collect energy usage data using AMI (Advanced Measuring Instrument) from residential housing, buildings and factories. Several researches have been done to improve an energy efficiency by analyzing the collected energy usage data. However, it is not easy to store and analyze the energy data using a traditional relational database management system since the data size grows exponentially with an increasing popularity of Smart grid infrastructure. In this paper, we are proposing a Hadoop based Big data system to store and analyze energy usage data. Based on our limited experiments, Hadoop based energy data analysis is three times faster than that of a relational database management system based approach with the current system.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.2
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• pp.41-51
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• 2017

Nowadays, research data repositories (RDR) have become progressively widespread all over the world. To expand repository services and build up inbound linking strategy, organizations list their repositories with so called Global Registries. Accordingly, such registries should be carefully described by the related data. In this study, I explore the metadata schema of re3data.org. I collect and analyze descriptions from the listed repositories, and come up with some suggestions concerning possible improvements to the metadata schema. To accomplish this, I develop a crawler program, which collects necessary data from the re3data.org. Based on the analysis results, I have identified two issues that required elements is missing, one issue that required element value is missing when the corresponding property is applied, five inconsistency issues with re3data controlled vocabulary, six issues with undescribed optional elements, and two inconsistency issues between the elements and their attributes which do not pair with. I believe this discussion can facilitate improvements to the existing re3data.org schema and further help researchers who analyze data repository trends.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.645-654
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• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.