• Journal of dental hygiene science
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• v.13 no.3
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• pp.254-263
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• 2013

This study is to develop an evaluation index for infection control and to verify its validity by examining each set of weighted data collected from 121 infection control personnel at dental hospitals who agreed to the preliminary survey and advisory. The study was conducted from 14th December, 2010 to 31st January, 2011, and PASW Statistics 18.0 and AMOS 5.0 had been used for the statistical data analysis. As a result of the study, five evaluation factors with 21 sub-indicators have been identified at structural level, eight evaluation factors with 32 sub-indicators at processing level, and one evaluation fact with five sub-indicators at resulting level, total 14 evaluation factors with 58 sub-indicators throughout all levels. The path analysis added on the result that 'standard precautions ($x_1$)', 'infection control support system ($x_2$)', 'internal and external characteristics ($x_3$)' are exogenous variables that affect on other variables, and 'standard infection control ($y_1$)','Organization equipment management handwashing ($y_2$)', 'environmental infection control ($y_3$), 'personal protective equipment ($y_4$)', 'waste and laundry management ($y_5$)' are endogenous variables that are infulenced by others. The standardized metrics are more needed than anything else when examining on infection control. This study attempts to develop proper dental infection control metrics adequately adjusted for domestic circumstances, and therefore to contribute to effective systematic management and decision-making in infection control.

• Korean Journal of Heritage: History & Science
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• v.39
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• pp.219-242
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• 2006

Quantitative analysis and provenance interpretation of the raw materials for the jade (amazonite) excavated from the Asan Sijeonri site were studied. Geology of the Sijeonri site composed mainly of Precambrian metasedimentary rocks and the alluvium ranges extensively. In the site, amazonite jade was excavated in the Bronze Age No. 4 circular-shaped resident site. The jade has a comma-shaped and shows light green color with so much cracks. The jade is silicate mineral of columnar habits that is shown white streak, and has fine cleavages with vitreous luster. As the analytical results, this jade was identified as a feldspar-group mineral gemologically called amazonite that is mineralogically microcline formed to intergrowth of albite and orthoclase. Internal textures of the amazonite present Na-end member of albite coexisting with K-end member of orthoclase that are replaced each other along the cleavages and twin planes with several ${\mu}m$ scales. Therefore, the amazonite is one mineral phase combined with albite and orthoclase by substitution of $Na_2O$ and $K_2O$, respectively. The Danyang are is an unique producing site of amazonite in South Korea, and Gongju Janggimyeon was known as microcline provenance to the utmost area from the Sijeonri site. In the marginal area of southern coast in Korean Peninsula, Bronze Age amazonite has been excavated in several sites, where original provenance of the raw amazonite is not identified. The Sijeonri site does not show any facilities of producing and processing traces for amazonite jade. Also, only one jade was collected in the Sijeonri site. Therefore, there is not possibility that the provenance of raw jade is the Sijeonri area. To explain original provenance of the amazonite jade, migration path, manufacturing process and archaeological interpretation are required.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.19-33
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• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.