• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.6
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• pp.510-515
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• 2006

The putative EPA synthesis gene cluster was mined from the entire genome sequence of Shewanella oneidensis MR-1. The gene cluster encodes a PKS-like pathway that consists of six open reading frames (ORFs): ORFSO1602 (multi-domain beta-ketoacyl synthase, KS-MAT-4ACPs-KR), ORFSO1600 (acyl transferase, AT), ORFSO1599 (multi-domain beta-ketoacyl synthase, KS-CLF-DH-DH), ORFSO1597 (enoyl reductase, ER), ORFSO1604 (phosphopentetheine transferase, PPT), and ORFSO1603 (transcriptional regulator). In order to prove involvement of the PKS-like machinery in EPA synthesis, a 20.195-kb DNA fragment containing the genes was amplified from S. oneidensis MR-1 by the long-PCR method. Its identity was confirmed by the methods of restriction enzyme site mapping and nested PCR of internal genes orfSO1597 and orfSO1604. The DNA fragment was cloned into Escherichia coli using cosmid vector SuperCos1 to form pCosEPA. Synthesis of EPA was observed in four E. coli clones harboring pCosEPA, of which the maximum yield was 0.689% of the total fatty acids in a clone designated 9704-23. The production yield of EPA in the E. coli clone was affected by cultivation temperature, showing maximum yield at $20^{\circ}C$ and no production at $30^{\circ}C$ or higher. In addition, production yield was inversely proportional to glucose concentration of the cultivation medium. From the above results, it was concluded that the PKS-like modules catalyze the synthesis of EPA. The synthetic process appears to be subject to regulatory mechanisms triggered by various environmental factors. This most likely occurs via the control of gene expression, protein stability, or enzyme activity.

• Journal of Practical Engineering Education
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• v.11 no.1
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• pp.117-123
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• 2019

The aim of this study was to investigate the priority partner countries for technical vocational education and training (TVET), using the cluster analysis. The partner countries were prioritized for finance supports and knowledge sharing. We had also redesigned the TVET assistance process as well as reflected the needs of recipient countries for TVET. Especially, by redesigning the methodological support process, we also increased the effectiveness and efficiency of TVET official development assistance. In research method, potential 24 priority partner countries have been designated by the international development cooperation committee and selection criteria of international cooperation agencies and banks. Then, we conducted the cluster analysis, using three main variables: economic factor, labor market factor, education factor. In results, we clustered four and three types of the priority partner countries for TVET. In future, we suggested the new approach for selecting the priority partner countries in terms of employment and labor as well as non-designated partner countries, which will need to cooperate for TVET.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.363-370
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• 2023

Due to the spread of COVID-19, the use of public transportation such as urban railroads has changed significantly since the beginning of 2020. Therefore, in this study, daily time series data for each urban railway station were collected for three years before COVID-19 and after the spread of COVID-19, and the similarity of time series analysis was evaluated through DTW (Dynamic Time Warping) distance method to derive regression centers for each cluster, and the effect of various external events such as COVID-19 on changes in the number of users was diagnosed as a time series impact detection function. In addition, the characteristics of use by cluster of urban railway stations were analyzed, and the change in passenger volume due to external shocks was identified. The purpose was to review measures for the maintenance and recovery of usage in the event of re-proliferation of COVID-19.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.379-389
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• 2024

Purpose and Method: Crowed Events can lead to sudden accidents caused by unpredictable variables. Therefore, focusing on the '10.29 Itaewon accident' among the representative cases, we examined the accident as the process of occurrence. In addition, improvement measures were suggested through analysis of related legal systems. Result: In the Itaewon accident, a "colony wave phenomenon" occurred due to "ultra-high-density cluster stay". In addition, cluster destruction occurred from a weak location in the cluster due to clusters and pressures in different directions to avoid this. Looking at the laws related to the safety management of Crowed Events, the laws and regulations differ depending on the location and type. Due to the complementary nature of the approach to the legal blind spot, the legal system that uses similar terms of the same concept and is not systematic is causing uncertainty in the application and interpretation of the law. Conclusion: Crowd control and on-site management should be carried out for events when the cluster density is expected to reach 8 people/m² or reached. Consistency should be maintained through the unified application of legislation to related legislation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1937-1945
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• 1999

In paper, we propose a 3D polygonal mesh simplification technique based on vertex clustering. The proposed method differentiates the size of each cluster according to the local property of a 3D object. We determine the size of clusters by considering the normal vector of triangles and the vertex distribution. The subdivisions of cluster are represented by octree. In this paper, we use the Harsdorff distance between the original mesh and the simplified one as a meaningful error value. Because proposed method adaptively determine the size of cluster according to the local property of the mesh, it has smaller error as compared with the previous methods and represent the small regions on detail. Also it can generate a multiresolution model and selectively refine the local regions.

• Journal of the Korean Society of Tobacco Science
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• v.22 no.2
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• pp.184-189
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• 2000

This study was conducted to develop the objective evaluation method for tobacco smoke. The evaluation was carried out by using the data of cut or blended tobacco components, smoke components, electric nose system (ENS), and sensory test. By using the statistical methods, such as cluster analysis, discriminant analysis, factor analysis, correlation analysis, and multiple regression analysis, the relationship among the data of tobacco, smoke, ENS, and sensory evaluation was studied. By the results of cluster analysis, the data from smoke analysis by GC and ENS were able to select the difference of tobacco leaf characteristics. As the results of discriminant analysis, grouping by the components of tobacco leaves and smoke was possible and the results of GC analysis of smoke could be used for discrimination of tobacco leaves. In the results of factor analysis, nicotine, tar, CO, puff No and pH in the smoke were the factors effecting on the tobacco leaf characteristics. From the correlation analysis, aroma, taste, irritation, and smoke volume of sensory test had high relation to tar, p-cresol threonolatone, levoglucosane, and quinic acid- ${\gamma}$ -lactone of smoke. The ENS data showed high efficiency for discriminant analysis and cluster analysis, but it was not good for factor analysis, and correlation analysis. It was possible to estimate tobacco leaves and their blending characteristics by the analytical data of tobacco leaves, smoke, ENS, and sensory test results. By the multiple regression analysis, some correlation among selected chemical components and sensory evaluation were found. This study strongly indicated that the some chemical analysis data was available for the objective evaluation of tobacco sensory attributes.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.89-99
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• 2015

The World Economic Forum and the Korean Ministry of Knowledge Economy have selected big data as one of the top 10 in core information technology. The key of big data is to analyze effectively the properties that do have data. Clustering analysis method of big data techniques is a method of assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. Similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we studied upper and lower bounds for binary similarity measures with negative matches such as Russel and Rao measure, simple matching measure by Sokal and Michener, Rogers and Tanimoto measure, Sokal and Sneath measure, Hamann measure, and Baroni-Urbani and Buser mesures I, II. And the comparative studies with these measures were shown by real data and simulated experiment.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.67-80
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• 2008

Various document categorization methods have been studied to provide a user with an effective way of browsing a large scale of documents. They do compares set of documents into groups of semantically similar documents automatically. However, the automatic categorization method suffers from low accuracy. This thesis proposes a semi-automatic document categorization method based on the domains of documents. Each documents is belongs to its initial domain. All the documents in each domain are recursively clustered in a level-wise manner, so that the category tree of the documents can be founded. To find the clusters of documents, the stop-word of each document is removed on the document frequency of a word in the domain. For each cluster, its cluster keywords are extracted based on the common keywords among the documents, and are used as the category of the domain. Recursively, each cluster is regarded as a specified domain and the same procedure is repeated until it is terminated by a user. In each level of clustering, a user can adjust any incorrectly clustered documents to improve the accuracy of the document categorization.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.429-439
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• 2008

Canonical correlation biplot is 2-dimensional plot for investigating the relationship between two sets of variables and the relationship between observations and variables in canonical correlation analysis graphically. In general, biplot is useful for giving a graphical description of the data. However, this general biplot and also canonical correlation biplot do not give some concise interpretations between variables and observations when the number of observations are large. Recently, for overcoming this problem, Choi and Kim (2008) suggested a method to interpret the biplot analysis by applying the K-means clustering analysis. Therefore, in this study, we will apply their method for investigating the relationship between skill and competition score factors of KLPGA players using canonical correlation biplot and cluster analysis.