• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.51-65
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• 2024

Phyto-sociological methods were used in this study to assess the vegetation structure of a forest stand at Mt. Goehwa in Sejong-Si with the aim of providing vegetation information for urban forest utilization and management plans. The actual forest vegetation was classified into two types of community groups (Quercus serrata-Lindera obtusiloba and Coreopsis lanceolata community groups) at the highest hierarchical level. The Q. serrata-L. obtusiloba community group was classified into six units, which included artificial forest and natural forest vegetation. Artificial forests were classified into three communities (Pinus rigida, Castanea crenata, and Robinia pseudoacacia), whereas natural forests were classified into three communities (Quercus variabilis, Quercus acutissimaa, and Pinus densiflora). The Coreopsis lanceolata community group, which exhibited vegetative characteristics of urban forest edge areas, was categorized into four units. The urban forest edges were classified into four communities (Indigofera bungeana, Lespedeza bicolor, Amorpha fruticosa, and Lespedeza cuneata). Accordingly, the vegetation structure of Mt. Goehwa was categorized into 10 vegetation unit systems. An importance value analysis showed the highest importance value for C. crenata at 6.7%, followed by P. rigida at 6.4%, and R. pseudoacacia at 6.3%, indicating that the ecological impact of plantation species can be significant on Mt. Goehwa. A community coefficient of similarity analysis revealed that the artificial and natural forests had similar species compositions; however, both forests differed from the urban forest edge. This variation was further confirmed by Detrended correspondence analysis(DCA), with similar results. Canonical correspondence analysis(CCA) showed that the artificial forest and natural forest community types were positively correlated with altitude, bare rock, and the present species. By contrast, the urban forest edge community types were negatively correlated with these factors.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.160-170
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• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.116-136
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• 2009

Since there are differences in the content, structure and functions of interpersonal communication during the practice of school science classes, it needs to articulate the difference of the modality of pedagogical practice in order to understand science teaching in detail. These characteristics of science teaching can be investigated by further insightful analysis on language in the science classroom. In this study, classroom discourse language codes using Bernstein's code theory were analyzed in the case of a middle school science class on the unit of minerals. The discourse language code was identified by the value of classification, which revealed power relations to the contexts of discourse and participants of discourse. It was also identified by the value of framing, which showed hierarchical relation between teacher and students as discourse subjects, and discursive control on the initiative of discourse. The results addressed that six types of discourse language codes were constructed and that those language codes reflected diverse modalities of science teaching from student-centered instruction to teacher-centered instruction in relation to classroom discourse. The modality of science teaching according to the transition tendencies of discourse language code showed dynamic variations of 'controlled student-centeredness inducing teaching' - 'positional student-centeredness permissive teaching' - 'controlled students' participation permissive teaching' - 'controlled student-centeredness facilitative teaching' - 'student-centeredness enhancing teaching'. In addition, results released that discursively and hierarchically weak control of discourse is necessary for enhancing student-centeredness of science teaching. Moreover, teaching practice enhancing student-centeredness can be accomplished by the harmony of a teacher's perception of discourse language code and his/her orientation to constructivist teaching and student-centered teaching.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.124-131
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• 2009

A reliable detection of regions in radiography is one of the most important task before the evaluation of defects on welded joints. The extracted features is to be classified into distinctive clusters for each segmented region. But conventional segmentation techniques give unsatisfactory results for this task due to the spatial superposition of intensity and low signal-to-ratio(SNR) in radiographic images. The usage of global or local processes not only provide the necessary noise resistance but also fail in classification of regions. In this paper, we presents an appropriate approach for segmentation of region-based indications in industrial Computed Radiography(CR) images. The geometric differences between welded and non-welded area which is generated on radiography as the representative regions(background, thickness, middle and welded region in steel tube image) have constructed the hierarchical structure. Although this structure is contaminated by noise, the scheme between regions can be selected by the help of local clustering based on distinctive geometric property of each region. Because of the geometric nature of the considered region and so that the region is selected layer by layer, and that the real class represents the boundary between regions, the vertical and horizontal clustering process in each layer must be judicious. In order to show the effectiveness of this approach, a comparative experiment of various segmentation method is performed on industrial steel tube CR images.

• Journal of the Korean Chemical Society
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• v.56 no.6
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• pp.739-750
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• 2012

The purpose of this study is to analyze the cognitive demands level of the description about 'pure substance and mixture compound', 'ionic compound', 'molecule' on the 'science2' textbooks by the 2007 revised curriculum. The three types of Curriculum Analysis Taxonomy have been used to analyze the cognitive demands level of those contents on the 6 kinds of 'science2' textbooks. The first, the cognitive demand level about 'pure substance and mixture compound' on many textbooks is a late concrete operational stage because of class inclusion and hierarchical classification. And the descriptions as 'pure substance is conserved even when mixed with other pure substance' is a early formal operational stage. The second, the cognitive demand level about 'ionic compound' and 'molecule' is a early formal operational stage, because of "Formal modeling is the indirect interpretation of reality by deductive comparison from a postulated system with its own rules" and "Atoms have a structure". The third, the terms as 'ionic bonding', 'ionic compound', 'chemical formula', 'covalent bonding', 'covalent compound', and 'molecular formula' have been used on many 'science2' textbooks. Those terms would be used later on 'chemistry I' and 'chemistry II' in senior high school but not even 'science3' and 'science'.

• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.351-358
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• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.1
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• pp.113-118
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• 2010

Due to diverse soil-forming environments and different purposes of the soil classification, numerous soil classification systems have been developed worldwide. The World Reference Base for Soil Resources (WRB) and the Soil Taxonomy of the United States are well-known in Korea. However, the German Soil Systematics based on somewhat different principles from the two former systems is little-known. The objective of this paper is therefore to give a short overview of the principles of the German Soil Systematics. The German Soil Systematics consists of a six-level hierarchical structure which comprises soil divisions, soil classes, soil types, soil subtypes, soil varieties, and soil subvarieties. Soils in Germany are firstly classified into one of four soil divisions according to the soil moist regime: terrestrial soils, semi-terrestrial soils, semi-subhydric/subhydric soils, and peats. Terrestrial soils are subdivided into 13 soil classes based on the stage of soil formation and the horizon differentiation. Semi-terrestrial soils are differentiated into four classes regarding the source of soil moist: groundwater, freshwater, saltwater, and seaside. Semi-subhydric/subhydric soils are subdivided into two classes: semi-subhydric and subhydric soils. Peats are classified into two classes of natural and anthropogenic origins. Classes can be compared to orders of the U.S. Taxonomy. Classes are subdivided into 29 soil types with regard to soil forming-processes for terrestrial soils, into 17 types with regard to the soil formation for semi-terrestrial soils, into five types with regard to the content of organic matter for semi-subhydric/subhydric soils, and also into five types with regard to peat-forming processes for peats. The soil mapping units in Germany are types, which can be additionally subdivided into ca. 220 subtypes, several thousands of varieties and subvarieties using detailed nuances of morphologic features of soil profile. Soil types can be compared to great groups of the U.S. Taxonomy.