• Journal of the Korean Vacuum Society
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• v.6 no.3
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• pp.181-186
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• 1997

Time-of-flight impact-collision ion scattering spectroscopy (TOF-ICISS) was applied to study the geometrical structure of epitaxially grown MgO layers on a TiC(001). The hetero-epitaxial MgO layer was able to be deposited by thermal evaporation of magnesium onto the TiC(001) surface and subsequent exposure of oxygen at room temperature. A slight heating of the substrate at around $300^{\circ}C$ was necessary to overcome a thermal barrier for the ordering. The well-ordered MgO structure was confirmed with the 1$\times$1 LEED pattern. TOF-ICISS was useful in studying interface structure between oxide and substrate. The results revealed that the MgO layer is formed at the on-top sites of the TiC(001) substrate and the lateral lattice constant of MgO layer is the same as that of the TiC substrate. The MgO was deposited within two layers on the most parts of the surface.

• Tunnel and Underground Space
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• v.25 no.3
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• pp.255-263
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• 2015

Explosive blasting is a very useful tool for mining and civil engineering applications. It, however, may cause severe environmental hazards on adjacent structures due to blasting impact. Blast engineers try to make optimum blast design to provide efficient performance and to minimize the environmental impact as well. It requires a pre-assessment of the impacts resulting from the blasting operation in design stage. One of the common procedures is to evaluate the proposed blast pattern through a series of test blasting in the field. Another approach is to evaluate the possible environmental effects using the numerical methods. There are a number of input parameters to be prepared for the numerical analysis. Some of them are well understood, while some are not. This paper presents some results of sensitivity analysis of the basic input parameters in numerical modelling of blasting problems so as to provide sound understanding of the parameters and some guidelines for input preparation.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.321-333
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• 2003

Self-organizing map (SOM), a unsupervised learning neural network, has been developed by T. Kohonen since 1980's. Main application areas were pattern recognition and text retrieval. Because of that, it has not been spread to statisticians until late. Recently, SOM's are frequently drawn in data mining fields. Kohonen's SOM, however, needs improvements to become a statistician's standard tool. First, there should be a good guideline as for the size of map. Second, an enhanced visualization mode is wanted. In this study, principal components self-organizing map (PC-SOM), a modification of Kohonen's SOM, is proposed to meet such needs. PC-SOM performs one-dimensional SOM during the first stage to decompose input units into node weights and residuals. At the second stage, another one-dimensional SOM is applied to the residuals of the first stage. Finally, by putting together two stages, one obtains two-dimensional SOM. Such procedure can be easily expanded to construct three or more dimensional maps. The number of grid lines along the second axis is determined automatically, once that of the first axis is given by the data analyst. Furthermore, PC-SOM provides easily interpretable map axes. Such merits of PC-SOM are demonstrated with well-known Fisher's iris data and a simulated data set.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.243-255
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• 2016

The developed composite index has limits to estimate and predict economic status due to economic pattern change and the response change of explanatory variables. A higher precedence individual indicators should be selected to predict the future accurately. In this study, effectiveness of Jeju Island precedence indicators consists of constituents in the area, the consumer price index, services production index, mining and manufacturing production index. The average temperature of Seogwipo and credit card purchase amount is reviewed as an economic turning point consideration and time lag correlation analysis with real data. In addition, we suggest the proper reference cycle in Jeju composite precedence index and evaluate the configuration in leading indicators for Jeju by comparing national economic indicators. Based on the derived results, the current problems of Jeju Island precedence indicators will be illustrated and the improvement methods to estimate a regional composite index will be suggested.

• Economic and Environmental Geology
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• v.31 no.5
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• pp.399-413
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• 1998

Daegang granite is located around the Namwon-gun, Cheolabuk-do, and is an elongate stock $(80 km^{2})$ in the NNE-SSW direction. Daegang granite has the very same mineralogical and geochemical characteristics as those of the typical A-type granites; (1) it is a one feldspar hypersolvus granite, and is classified as an alkali feldspar granite in the lUGS scheme, (2) has small amounts of Fe-rich biotite (annite) and alkali amphibole (ribeckite) that are late in the crystallization sequence of the granitic magma, (3) always contains opaque oxides, fluorite and zircon, (4) shows high and quite homogeneous $SiO_2$, content (mostly 72~77 wt.%) and $(Na_{2}O+K_{2}O)/Al_{2}O_{3}$ ratio (0.90~0.98), (5) contains high Ga, lOOOO*Ga/Ai, $K_{2}O+Na_{2}O$, $(K_{2}O+Na_{2}O)/CaO$, $K_{2}O/MgO$, FeO/MgO, agpaitic index, Zr, Nb, Ce, Y, Zn value or ratio that resemble to those of the Australian A-type granites (Whalen et al., 1987), and (6) has enriched LREE and HREE that show flat variation pattern with slightly depleted in HREE and profound Eu anomalies (Eu/Eu*=0.04~0.l4). In the tectonic discrimination diagrams of Pearce et al. (1984) and Eby (1992), Daegang granite is classified as a within plate granite and $A_{2}-type$.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.59-72
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• 2012

Since a protein displays its specific functions when disorder region of protein sequence transits to order region with provoking a biological reaction, the separation of disorder region and order region from the sequence data is urgently necessary for predicting three dimensional structure and characteristics of the protein. To classify the disorder and order region efficiently, this paper proposes a classification/prediction method using sequence data while acquiring a non-biased result on a specific characteristics of protein and improving the classification speed. The emerging patterns based EPs-TFP methods utilizes only the essential emerging pattern in which the redundant emerging patterns are removed. This classification method finds the sequence patterns of disorder region, such sequence patterns are frequently shown in disorder region but relatively not frequently in the order region. We expand P-tree and T-tree conceptualized TFP method into a classification/prediction method in order to improve the performance of the proposed algorithm. We used Disprot 4.9 and CASP 7 data to evaluate EPs-TFP technique, the results of order/disorder classification show sensitivity 73.6, specificity 69.51 and accuracy 74.2.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• Journal of Environmental Science International
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• v.29 no.11
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• pp.1125-1139
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• 2020

This study investigated the distributional pattern of meiobenthos associated with future deep-sea mining in the Korea Deep Ocean Study area present in the Clarion-Clipperton Fracture Zone (CCFZ) located in the southeastern part of the North Pacific Ocean. Standing stocks of meiobenthos were investigated in benthic impact experiment sites (BIS) and Korea Institute of Ocean Science & Technology long-term monitoring (KOMO) sites during the 2008-2014 annual field survey. A total of 14 taxa of meiobenthos were identified. Nematodes were the most abundant taxon (60-86%). Harpacticoid copepods (5-26%) and benthic foraminifera (1-12%) were also dominant at all sites. The total meiobenthic densities varied from 4 to 150 ind./10 cm2. The mean value of total meiobenthic abundance was higher at BIS than at KOMO sites, but there was no significant difference between the two sites. The mean values of the number of taxa and biomass at BIS and KOMO sites were similar. The mean abundance of nematodes that were the most dominant taxa was also higher at BIS than at KOMO sites. The standing stocks in our study sites were relatively lower than those previously reported at other CCFZ sites. These results seem to reflect a low organic concentration in the study area.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.857-866
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• 2003

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques in order to analyze an audit data set. However. since they mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In this paper, a new clustering algorithm for modeling the normal pattern of a user's activities is proposed. Since clustering can identify an arbitrary number of dense ranges in an analysis domain, it can eliminate the inaccuracy caused by statistical analysis. Also, clustering can be used to model common knowledge occurring frequently in a set of transactions. Consequently, the common activities of a user can be found more accurately. The common knowledge is represented by the occurrence frequency of similar data objects by the unit of a transaction as veil as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a concise profile. As a result, the profile can be used to detect any anomalous behavior In an online transaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.1-15
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• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.