• Bulletin of the Korean Chemical Society
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• v.22 no.9
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• pp.1023-1029
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• 2001

The positions of Kr atoms encapsulated in the molecular-dimensioned cavities of fully dehydrated zeolite A of unit-cell composition Cs3Na8HSi12Al12O48 (Cs3-A) have been determined. Cs3-A was exposed to 1025 atm of krypton gas at 400 $^{\circ}C$ for four days, followed by cooling at pressure to encapsulate Kr atoms. The resulting crystal structure of Cs3-A(6Kr) (a = $12.247(2)\AA$, R1 = 0.078, and R2 = 0.085) has been determined by single-crystal X-ray diffraction techniques in the cubic space group Pm3m at $21(1)^{\circ}C$ and 1 atm. In the crystal structure of Cs3-A(6Kr), six Kr atoms per unit cell are distributed over three crystallographically distinct positions: each unit cell contains one Kr atom at Kr(1) on a threefold axis in the sodalite unit, three at Kr(2) opposite four-rings in the large cavity, and two at Kr(3) on threefold axes in the large cavity. Relatively strong interactions of Kr atoms at Kr(1) and Kr(3) with Na+ ions of six-rings are observed: Na-Kr(1) = 3.6(1) $\AA$ and Na-Kr(3) = $3.08(5)\AA.$ In each sodalite unit, one Kr atom at Kr(1) was displaced $0.74\AA$ from the center of the sodalite unit toward a Na+ ion, where it can be polarized by the electrostatic field of the zeolite, avoiding the center of the sodalite unit which by symmetry has no electrostatic field. In each large cavity, five Kr atoms were found, forming a trigonal-bipyramid arrangement with three Kr(2) atoms at equatorial positions and two Kr(3) atoms at axial positions. With various reasonable distances and angles, the existence of Kr5 cluster was proposed (Kr(2)-Kr(3) = $4.78(6)\AA$ and Kr(2)-Kr(2) = $5.94(7)\AA$, Kr(2)-Kr(3)-Kr(2) = 76.9(3), Kr(3)-Kr(2)-Kr(3) = 88(1), and Kr(2)-Kr(2)-Kr(2) = $60^{\circ}).$ These arrangements of the encapsulated Kr atoms in the large cavity are stabilized by alternating dipoles induced on Kr(2) by four-ring oxygens and Kr(3) by six-ring Na+ ions, respectively.

• Journal of the Korean Chemical Society
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• v.26 no.4
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• pp.211-217
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• 1982

Assuming krypton molecules adsorbed on the graphite surface as a two-dimensional (2D) gas, 4th virial coefficient of the virial equation is calculated by the use of cluster integrals. The Henry's law constant, and 2nd and 3rd virial coefficients are also calculated. Adsorption isotherms calculated from this virial equation agree very satisfactorily with experimental results. The interaction energy of Kr-graphite surface is calculated assuming the pairwise additivity of Lennard-Jones(12,6) potential, and parametars therein are taken as; ${\varepsilon}_{gs}$/k = 71.1 K, ${\varepsilon}_{gg}$/k = 170 K, ${\sigma}_{gs}$ = 354 pm, and ${\sigma}_{gg}$ = 368 pm.

• Journal of the Korean Society of Food Science and Nutrition
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• v.30 no.5
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• pp.765-769
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• 2001

Starches of flavored glutinous rice were analyzed by using scanning electron microscope (SEM) and differential scanning calorimetry (DSC) and tested on the starch granule susceptibility to 15% H$_2$SO$_4$, glucoamylase and $\alpha$-amylase. The shape of starch granules form flavored glutinous rice varieties was polygonal and the size was 4~6 $\mu$m in diameter. According to DSC, glutinous rice starch showed onset temperature (T$_{o}$) range 59.8~62.5$^{\circ}C$ and KR92021-B-B-42-3-B and KR92021-B-B-165-1-B showed higher enthalpy ($\Delta$H) on gelatinization than others. The starches from KR92021-B-B-5-2-B and KR92021-B-B-42-3-B showed lower hydrolysis rate using 15% H$_2$SO$_4$ than KR92021-B-B-165-1-B. KR92021-B-B-5-2-B showed higher degree of hydrolysis of glucoamylase and $\alpha$-amylase than the others.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.517-528
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• 2018

We provide R scripts to detect outliers in multivariate data and visualization. Detecting outliers is provided using three approaches 1) Robust Mahalanobis distance, 2) High Dimensional data, 3) density-based approach methods. We use the following techniques to visualize detected potential outliers 1) multidimensional scaling (MDS) and minimal spanning tree (MST) with k-means clustering, 2) MDS with fviz cluster, 3) principal component analysis (PCA) with fviz cluster. For real data sets, we use MLB pitching data including Ryu, Hyun-jin in 2013 and 2014. The developed R scripts can be downloaded at "http://www.knou.ac.kr/~sskim/ddpoutlier.html" (R scripts and also R package can be downloaded here).

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.47.3-47.3
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• 2019

We discovered 722 open clusters within 1 kpc using Gaia DR2 data. These clusters are detected in the proper motion space and confirmed on the spatial distribution with parallax information. We divided the 3628 regions and visually searched using python program. Among 722 open clusters, 430 clusters are previously unknown clusters. Catalogue of discovered clusters is unloaded on the online catalogue at https://radio.kasi.re.kr/project/shlee/. Owing to the good membership criteria, we could see the halo structure of the clusters. In that reason, the average size of the discovered cluster is about 9 times than that of previously known clusters.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.142-143
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• 2003

Ammi majus (white lace flower, Unbelliferae) is an ornamental plant used for cut-flower arrangements worldwide. A potyvirus was isolated from its leaves with mosaic and chlorotic symptoms in the cultivated field of Chiba, Japan. Compared with Japanese homuort mosaic virus (JHMV) previously isolated from Cryptotaenia japonica, it showed similar characteristics in host reactions and molecular properties. The nucleotide sequences of coat protein and 3'- nontranslated region were highly homologous and shared 87％ and 91％ identities with those of JHMV, respectively. This virus was thus supposed to be an isolate of JHMV and designated as JHMV-Am. Phylogenetic tree was constructed using CP nucleotide sequences of the two isolates and other potyviruses previously reported. JHMV-Am and JHMV fell into a cluster with Korean strain of Zantedeschia mosaic virus (ZaMV-KR). However, low identity in amino acid sequences was found in the termini of CP genes between the two isolates of JHMV and ZaMV-KR.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.33-39
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• 2004

In this paper. we design the learning algorithm of LVQ which is used Forward Counter Propagation Networks to improve classification performance of LVQ networks. The weights of Forward Counter Propagation Networks which is between input layer and cluster layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm. Finally. pattern vectors is classified into subclasses by neurons which is being in the cluster layer, and the weights of Forward Counter Propagation Networks which is between cluster layer and output layer is learned to classify the classified subclass, which is enclosed a class. Also. kr the number of classes is determined, the number of neurons which is being in the input layer, cluster layer and output layer can be determined. To prove the performance of the proposed learning algorithm. the simulation is performed by using training vectors and test vectors that ate Fisher's Iris data, and classification performance of the proposed learning method is compared with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional classification.

• 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.