• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.47-65
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• 2021

This study aims to identify specific topics, trends, and structural characteristics of scholarly communication research, based on 1,435 articles published from 1970 to 2018 in the Scopus database through Latent Dirichlet Allocation topic modeling, serial analysis, and network analysis. Topic modeling, time series analysis, and network analysis were used to analyze specific topics, trends, and structures, respectively. The results were summarized into three sets as follows. First, the specific topics of scholarly communication research were nineteen in number, including research resource management and research data, and their research proportion is even. Second, as a result of the time series analysis, there are three upward trending topics: Topic 6: Open Access Publishing, Topic 7: Green Open Access, Topic 19: Informal Communication, and two downward trending topics: Topic 11: Researcher Network and Topic 12: Electronic Journal. Third, the network analysis results indicated that high mean profile association topics were related to the institution, and topics with high triangle betweenness centrality, such as Topic 14: Research Resource Management, shared the citation context. Also, through cluster analysis using parallel nearest neighbor clustering, six clusters connected with different concepts were identified.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.113-129
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• 2021

Recently, traffic congestion in the city is continuously increasing due to the expansion of the living area centered in the metropolitan area and the concentration of population in large cities. New road construction has become impossible due to the increase in land prices in downtown areas and limited sites, and the importance of efficient data-based road operation is increasingly emerging. For efficient road operation, it is essential to classify appropriate scenarios according to changes in traffic conditions and to operate optimal signals for each scenario. In this study, the Dynamic Time Warping model for cluster analysis of time series data was applied to traffic volume and speed data collected at continuous intersections for optimal scenario classification. We propose a methodology for composing an optimal signal operation scenario by analyzing the characteristics of the scenarios for each data used for classification.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.107-116
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• 2020

The purpose of this study is to analyze the characteristics of the 11 southeastern states in the United States by using regional characteristics variables and to classify the regions. First, 19 variables from four categories of population, society, industry-economy and urban service were selected and factor analysis were conducted, and the result showed five major factors of population, economic condition, job and commuting. Based on the following factor scores, a cluster analysis was conducted, and eight types of big city, medium-sized city, bed town, small town, urban hinterland, retirement town, and rural village were derived. These types of spatial distribution characteristics showed big cities were by different types of regions and they formed metropolitan areas. Each types of classified regions were located along the road network with hierarchy. The study focused on cases in the southeastern regions of the United States and can be used as a comparison with Korean cases. If the same research method is applied to Korea in the future, or if the time series of changes is tracked by analyzing different time points, it will greatly help identify the characteristics of urban and rural mixed areas.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_1
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• pp.781-795
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• 2024

While an increasing number of people across diverse audience groups are engaging in discussions about the onset of the 4th industrial revolution, it remains challenging to pinpoint the symptoms of this phenomenon. This research, recognizing this difficulty, employed a time-series data-tuned cluster analysis to uncover evidence of industrial and technological convergence among the United States, Japan, and Korea by using time-series industrial R&D data and industrial wage data as indirect measures of industrial competitiveness and technology convergence. The results showed that the recent U.S. case of 2010-2019 data clearly featured the" phenomenon Tesla", which shows the convergence of Aerospace, Transportation equipment, and software. Supporting evidence for that comes from the results from the previous periods in the three countries, which shows a high concentration of cor manufacturing sectors, but no symptoms of convergence.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Publications of The Korean Astronomical Society
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• v.31 no.3
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• pp.43-56
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• 2016

Through time-series BV CCD photometry of the open cluster NGC 225 region, we have detected 30 variable stars including 22 new ones. They are five ${\delta}$ Scuti-type variable stars, a slowly pulsating B star, six eclipsing binary stars and 18 semi-long periodic or slow irregular variables, respectively. We have performed multiple-frequency analysis to determine pulsation frequencies of the ${\delta}$ Scuti-type stars and a slowly pulsating B star, using the discrete Fourier transform and linear least-square fitting methods. We also have derived the periods and amplitudes of 6 eclipsing binaries and a long-period variable star from the phase fitting method, and presented the light curves of all variable stars. A slowly pulsating B star is a member of NGC 225, but ${\delta}$ Scuti-type stars are not members from the positions in the color-magnitude diagram and the radial distancies from the center of the cluster. From Dias et al. (2014, A&A, 564, 79), only three variable stars including the slowly pulsating B star are members of clusters: two are in NGC 225 and one is in Stock 24. But a variable star in Stock 24 is not a member of the cluster because of its position of color-magnitude diagarm.

• Publications of The Korean Astronomical Society
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• v.25 no.2
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• pp.31-49
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• 2010

Next to Paper I (Jeon 2009a), time-series BV CCD images of the open cluster M38 were taken for 4 nights on December, 2009. The observations have been carried out for total 27 nights. In addition to the 20 variable stars in the Paper I, the discovery of 44 new variable stars has been presented in this paper: $6{\delta}$ Scuti stars, $2{\gamma}$ Doradus stars, 18 eclipsing binaries and 18 semi-long periodic and/or slow irregular type variable stars. For the V photometry of the ${\delta}$ Scuti and ${\gamma}$ Doradus stars, multi-frequency analysis was performed using the Discrete Fourier Transform and linear least-square fitting. The period search for the eclipsing binaries and the semi-long periodic and/or slow irregular type variable stars was performed by phase fitting method. As a result, the periods for 23 variable stars among the 44 ones were defined.

• Publications of The Korean Astronomical Society
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• v.25 no.4
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• pp.167-176
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• 2010

In the region of the intermediate open cluster M35 (NGC 2168), the time-series of V CCD images was taken for 12 nights from December 18, 2007 to September 25, 2010. From this observation, we detected 22 variable stars including 15 new ones. They are 6 $\delta$ Scuti, a Cepheid, an RR Lyrae, 9 eclipsing binaries and 5 semi-long periodic and/or slow irregular type variable stars. For the V photometry of the $\delta$2 Scuti stars, the multi-frequency analysis was performed using the Discrete Fourier Transform and the linear least-square fitting.