• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.329-338
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• 2009

The importance performance analysis is a simple and condensed analytic method for decision making based on the level of performance or satisfaction. Many researches already have witnessed usefulness of the importance performance analysis, but it also has some drawbacks from the statistical points of view. In this article, some additional techniques dealing the importance performance analysis are introduced and it is shown that these techniques would turn out to be very informative. The importance performance analysis uses the arithmetic average as the main statistic, but by the use of the median, the frequency and the cluster analysis it is shown that the importance performance analysis can be carried out with more crucial information. In addtion to that, it is demonstrated that the combination of the analytic hierarchy process and importance performance analysis could enable more reliable decision making.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.1-10
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• 2022

The packaging plastic manufacturing(blown film) industry has long developed in China, but most of them are small/medium-sized enterprises, and it is very rare to have appropriate operation plans suitable for their own business. The packaging plastic manufacturing industry(blown film) follows a typical Make-To-Order method, and the sequence of processing orders is very important. Waste of materials incurred by frequent conversions of production cannot be avoided, and generally, related costs incurred during conversion production are also different. Therefore, this study developed a job sequence determination model for improving operating profits using @RISK simulation software, compared and analyzed 3 actionable clustering treatment methods proposed by technical managers and field experts under the actual situation of the factory.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.31-42
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• 2021

The purpose of this study was to explore research topics and examine the trend in COVID19 related research papers. We identified eight topics using latent Dirichlet allocation and found acceptable validity in comparison with the structural topic model. The subtopics have been extracted using k-means clustering and plotted in PCA space. Additionally, we discovered the topics bearing negative tones and warning signs by sentiment analysis. The results flagged up the issues of the topics, Biomedical Related, International Dynamics and Psychological Impact. The findings could serve as a guideline for researchers who explore new research directions and policymakers who need to make decisions about which research projects to support.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.225-236
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• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.34-42
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• 2014

This study is a fundamental research to determine the emergency evacuation roads considering road network function and connectivity. First of all, the functional aspects of the road networks are analyzed in the target area, Sejong city, by using degree centrality(DC) and closeness centrality(CC) from GIS based database. Secondly, how network connectivity makes a change in user's travel pattern and travel time and how it affects the whole network are analyzed using TransCAD simulation program. Finally, after performing cluster analysis of index, first and second emergency evacuation roads are determined by judging the characteristics of clusters. The results of this research will be helpful for making a decision to diminish secondary damages when confronting unexpected disasters.

• Korean Journal of Microbiology
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• v.46 no.1
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• pp.1-8
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• 2010

New technologies are providing unprecedented knowledge into microbial community structure and functions. Even though nucleic acid based approaches provide a lot of information, metaproteomics could provide a high-resolution representation of genotypic and phenotypic traits of distinct microbial communities. Analyzing the metagenome from different microbial ecosystems, metaproteomics has been applied to seawater, human guts, activated sludge, acid mine drainage biofilm, and soil. Although these studies employed different approaches, they elucidated that metaproteomics could provide a link among microbial community structure, function, physiology, interaction, ecology, and evolution. These approaches are reviewed here to help gain insights into the function of microbial community in ecosystems.

• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.186-194
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• 2019

Deterministic optimization, commonly used to find the geophysical inverse solutions, have its limitation that it cannot find the proper solution since it might converge into the local minimum. One of the solutions to this problem is to use global optimization based on a stochastic approach, among which a large number of particle swarm optimization (PSO) applications have been introduced. In this paper, I developed a geophysical inversion algorithm applying PSO method for the layered-earth resistivity inversion of the small-loop electromagnetic (EM) survey data and carried out numerical inversion experiments on synthetic datasets. From the results, it is confirmed that the PSO inversion algorithm could increase the inversion success rate even when attempting the inversion of small-loop EM survey data from which it might be difficult to find a best solution by applying the Gauss-Newton inversion algorithm.

• The Journal of Engineering Geology
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• v.25 no.1
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• pp.67-81
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• 2015

A FORTRAN program was developed to determine the optimal locations of multiple recharge wells in an aquifer with different arrangements of pumping wells. The simulated annealing algorithm was used to find optimal locations of two recharge wells which satisfied three objective functions. The model results show that locating two injection wells inside the cluster of pumping wells is efficient if the recovery rate only was taken into account. In contrast, placing injection wells to the side of the cluster is desirable if the simulation considers aggregate objective function. Therefore, installing an injection well on each side of the cluster seems to yield the maximum recovery rates for the existing pumping wells, and it yields similar increases in pumping rate for all wells in the cluster. The locations of recharge wells can be arranged in numerous configurations, because there are multiple near-optimal local minima or maxima. These results indicate that the simulated annealing can yield effective evaluations of the optimal locations of multiple recharge wells. In addition, the suggested aggregate objective function can be utilized as an appropriate multi-objective optimization.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.4
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• pp.333-366
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• 2006

The purpose of this study is principle and theory of main class in a Library Classification. According to Sayers, 'The foundation of the library is the book; the foundation of librarianship is classification.' We looked at the between scientific and bibliographic classification, and at the fact that bibliographic scheme is usually an aspect classification. That is to say, the organization of topics is based on areas or activity and the first division of the scheme is into disciplines or subject domains. This first division of classification creates what are called main class. The sequence of main classes is also important. A rough definition of a amin class is that it corresponds to a sin91e notational character. Main classes usually equivalent to traditional disciplines. What constitutes a main class will vary from one classification to another. The order in which the main classes are listed is often discussed at the theoretical level, and some orders are considered to be better than others.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.759-768
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• 2021

This work introduces a new approach that classifies individual household water usage by examining the characteristics of smart meter end-user demand data. Here, one of the most well-known unsupervised machine learning, K-means algorithm, is applied to classify water consumptions by each household. The intensity and duration of end-user demands are used as main features to determine the households with similar water consumption pattern. The results showed that 21 households are classified into 13 clusters with each cluster having one, two, three, or five houses. The reasoning why multiple households are classified into the same cluster is described in this paper with respect to the collected data and end-user water consumption behavior.