• 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 Power Electronics
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• v.17 no.4
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• pp.1088-1096
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• 2017

Load variations on a distribution line result in voltage fluctuations at the point of common coupling (PCC). In order to keep the magnitude of the PCC voltage constant at its rated value and obtain zero voltage regulation (ZVR), a D-STATCOM is installed for voltage correction. Moreover, the ZVR mode of a D-STATCOM can also be used to balance the source current during unbalanced loading. For medium voltage and high power applications, a D-STATCOM is realized by the cascaded H-bridge topology. In the ZVR mode, the D-STATCOM may draw unbalanced current and in this process is required to handle different phase powers leading to deviations in the cluster voltages. Zero sequence voltage needs to be injected for ZVR mode, which creates circulating power among the phases of the D-STATCOM. The computed zero sequence voltage and the individual DC capacitor balancing controller help the DC cluster voltage follow the reference voltage. The effectiveness of the control scheme is verified by modeling the system in MATLAB/SIMULINK. The obtained simulations are further validated by the experimental results using a dSPACE DS1106 and five-level D-STATCOM experimental set up.

• Macromolecular Research
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• v.17 no.3
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• pp.187-191
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• 2009

Platinum (Pt) nanoparticles were prepared by the liquid-phase reduction of tetraammineplatinum (II) chloride $([Pt(NH_3)_4]Cl_2)$ using Nafion as a stabilizer under various conditions of the Nation phase. This method is novel in its use of electrostatic interactions between the Pt complex ions and sulfonic groups in the hydrated Nation molecules. The synthesized Pt nanoparticles of the recast film system had a cubic shape. In the case of the Nation solution system, the Pt nanoparticles mainly had a spherical shape. The shapes and sizes of the Pt nanoparticles were strongly influenced by the Nation phase.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.984-994
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• 2020

It is reported that about 20% of accidents at nuclear power plants in Korea and abroad are caused by human error. One of the main factors contributing to human error is fatigue, so it is necessary to prevent human errors that may occur when the task is performed in an improper state by grasping the status of the operator in advance. In this study, we propose a method of evaluating operator's fitness-for-duty (FFD) using various parameters including eye movement data, subjective fatigue ratings, and operator's performance. Parameters for evaluating FFD were selected through a literature survey. We performed experiments that test subjects who felt various levels of fatigue monitor information of indicators and diagnose a system malfunction. In order to find meaningful characteristics in measured data consisting of various parameters, hierarchical clustering analysis, an unsupervised machine-learning technique, is used. The characteristics of each cluster were analyzed; fitness-for-duty of each cluster was evaluated. The appropriateness of the number of clusters obtained through clustering analysis was evaluated using both the Elbow and Silhouette methods. Finally, it was statistically shown that the suggested methodology for evaluating FFD does not generate additional fatigue in subjects. Relevance to industry: The methodology for evaluating an operator's fitness for duty in advance is proposed, and it can prevent human errors that might be caused by inappropriate condition in nuclear industries.

• Annual Conference of KIPS
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• 2004.05a
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• pp.31-34
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• 2004

회복기법은 비공유 공간 데이터베이스 클러스터에서 고가용성을 위해 매우 중요하게 고려되고 있다. 일반적으로 데이터베이스 클러스터의 회복기법은 노드의 오류가 발생한 경우 로컬 로그와는 별도로 클러스터 로그를 생성하며, 이를 기반으로 해당 노드에서의 회복과정을 수행한다. 그러나, 기존의 기법은 하나의 레코드를 위해 다수의 갱신정보를 유지함으로써 클러스터 로그의 크기가 증가되고, 전송비용이 증가된다. 이는 회복노드에서 하나의 레코드에 대해 여러 번의 불필요한 연산을 실행하여 회복시간이 증가되고, 전체적인 시스템의 부하를 증가시키는 문제를 발생시킨다. 본 논문에서는 비공유 공간 데이터베이스 클러스터에서 최신버전의 클러스터 로그를 이용한 회복기법을 제안한다. 제안기법에서의 최신버전의 클러스터 로그는 레코드의 변경사항과 실제 데이터를 가리키는 포인터 정보로 구성되고, 하나의 갱신정보를 유지함으로써 클러스터 로그의 크기가 감소하며, 전송비용이 감소한다. 회복노드에서는 하나의 레코드에 대해 한번의 갱신연산만 실행하므로 빠른 회복이 가능하며, 시스템의 가용성을 향상시킨다.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.149-160
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• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1238-1259
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• 2019

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.

• International Journal of Industrial Entomology and Biomaterials
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• v.41 no.2
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• pp.56-62
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• 2020

Mulberry (Morus spp. family: Moraceae) has prime importance in the sericulture industry, and its foliage is the only natural feed of the silkworm Bombyx mori L. Traditional classification methods using morphological traits were largely unsuccessful in assessing the diversity and relationships among different mulberry species because of environmental influences on the traits of interest. For these reasons, it is difficult to differentiate between the varieties and cultivars of Morus spp. In the present study, inter-simple sequence repeat (ISSR) markers were used to investigate the genetic diversity of 48 mulberry samples genotyped using nine ISSR primers. The ISSR markers exhibited polymorphisms (53.2%) among mulberry genotypes. Furthermore, similarity coefficient estimated for these ISSR markers was found to vary between 0.67 and 0.99 for the combined pooled data. The phenogram drawn using the UPGMA cluster method based on combined pooled data of the ISSR markers divided the 48 mulberry genotypes into seven major groups. No genetic association was found in the collection area, and there was a mixed pattern between the mulberry lines. The hybridization between different mulberry species is highly likely to be homogenized due to natural hybridization.

• Journal of Korea Trade
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• v.23 no.4
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• pp.103-114
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• 2019

Purpose - This paper investigates the structure of exports across countries in terms of value added. Exports value added is examined under two categories, domestic and overseas. Using a statistical classification method by distance based on these two value added categories, this paper estimates the similarity of exports value added across countries including Korea. Design/methodology - The model of study is to employ a generalized distance function and then derive the Manhattan and Euclidean distances. The paper also performs cluster analysis using the Partitioning Around Medoids (PAM) and hierarchical methods to classify the 44 sample countries considered in this study. Findings - Our main findings are as follows. The 44 countries can be classified under 5 groups by their domestic and overseas value added in exports. Korea has a sandwich global value chains (GVCs) position between Japan, China, and Taiwan in the East Asian region. Originality/value - Existing papers point out the double counting problem of trade statistics as the intermediate goods trade across borders increases. This paper addresses the double counting problem by using the World Input-Output Table. The paper shows the need to explore the similarity of value added in exports structure across countries and investigate the GVCs position and role of each country.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.18-27
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• 2022

Construction accidents are difficult to prevent because several different types of activities occur simultaneously. The current method of accident analysis only indicates the number of occurrences for one or two variables and accidents have not reduced as a result of safety measures that focus solely on individual variables. Even if accident data is analyzed to establish appropriate safety measures, it is difficult to derive significant results due to a large number of data variables, elements, and qualitative records. In this study, in order to simplify the analysis and approach this complex problem logically, data preprocessing techniques, such as latent class cluster analysis (LCCA) and predictor importance were used to discover the most influential variables. Finally, the correlation was analyzed using an alluvial flow diagram consisting of seven variables and fourteen elements based on accident data. The alluvial diagram analysis using reduced variables and elements enabled the identification of accident trends into four categories. The findings of this study demonstrate that complex and diverse construction accident data can yield relevant analysis results, assisting in the prevention of accidents.