• The Journal of the Petrological Society of Korea
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• v.9 no.4
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• pp.254-270
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• 2000

To clarify the geological structure of North Sobaegsan Massif in the Sangunmyeon area, Bonghwagun, Korea, where the Yecheon Shear Zone passes and the NE-SW and E-W trending structural lineaments are developed, the rock-structures of its main constituent rocks(Precambrian Won-nam Formation and Mesozoic Hornblende Granite) were examined. In this area, the geological structure was formed at least by four phases of deformation after the formation of gneissosity or schistosity of the Wonnam Formation: one deformation before D2 ductile shearing related to the for-mation of the Yecheon Shear Zone and two deformations after that. The NE-SW and E-W trending structural lineaments were formed by a giant open or gentle type of F4 fold, and their trends before D4 deformation are interpreted to be parallel to the orientation(ENE-WSW trend) of folded surface in the F4 hinge zone. The structural features of Dl-D3 deformations and their relative occurrence times are as follows. Dl deformation is formative period of the boudin structures and ENE-WSW trending isoclinal folds with sub-horizontal hinge lines and steeply inclined axial surfaces. D2 deformation is that of the mylonite foliation, stretching lineation and Z-shaped asymmetric folds related to top-to-the ENE dextral strike-slip shearing on the distinct foliations of Wonnam Formation(after intrusion of Mesozoic Hornblende Granite). D3 deformation is that of the ENE trending S-shaped asymmetric folds with sub-horizontal hinge lines and axial surfaces related to normal-slip shearing on the distinct foliations. It is expected that the result will be contributed to as valuable data for interpreting the tectonic evolution of the North Sobaegsan Massif and the Northeast Ogcheon Belt whose tectonic lineaments are changed from NE-SW to E-W trends at the Sindong-Bonghwa line.

• Investigative Magnetic Resonance Imaging
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• v.10 no.1
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• pp.1-7
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• 2006

Purpose : Recent development of diffusion tensor imaging enables the evaluation of the microstructural characteristics of the brain white matter. However, optimal imaging parameters for diffusion tensor imaging, particularly concerning the number of diffusion gradient direction, have not been studied thoroughly yet. The purpose of this study was to evaluate the influence of the number of diffusion gradient direction on the fiber tracking of the white matter. Materials and methods : 13 healthy volunteers (ten men and three women, mean age 30 years, age range 23-37 years) were included in this study. Diffusion tensor imaging was performed with different numbers of diffusion gradient direction as 6, 15, and 32, keeping the other imaging parameters constant. The imaging field ranged from 1 cm below the pons to 2-3 cm above the lateral ventricle, parallel to the anterior commissure-posterior commissure line. FA (fractional anisotropy) maps were created via image postprocessing, and then FA and its standard deviation were calculated in the genu and the splenium of the corpus callosum on each of FA maps. Fiber tracking of the corticospinal tract in the brain was performed and the number of the reconstructed fibers of the tract was measured. FA, standard deviation of FA and the number of the reconstructed fibers were compared statistically between the different diffusion gradient directions. Results : FA is not statistically significantly different between the different diffusion gradient directions. By increasing the number of diffusion gradient direction, standard deviation of FA decreased significantly, and the number of the reconstructed fibers increased significantly. Conclusion : The higher number of diffusion gradient direction provided better quality of fiber tracking.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.164-180
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• 2021

Plants Social Network(PSN) analysis combines the plant sociological method and the social network analysis to understand plant society focusing on environmental-to-plant and plant-to-plant relationships. PSN is at an early stage of research and require comparing plant society analyses in various environments and existing interspecies binding analysis. This study conducted a vegetation structural analysis of Mudeungsan National Park and compared the existing interspecies connection analysis with the PSN. A total of 60 plots were established for a survey on the Old Trail. The TWINSPAN and DCA analysis showed that the 60 survey plots were divided into the Quercus serrata-Pinus densiflora community (Community I) and the Quercus mongolica community (Community II) based on an altitude of 800 meters. We performed the interspecies correlation with more than 30% emergence frequency and the DCA analysis and compared the results with a focus on the major species in each colony. The results showed that Quercus serrata had a correlation of -0.450^** and -0.375^** with Pinus densiflora and Quercus mongolica, respectively. The DCA analysis also confirmed that Quercus serrata was located close to Pinus densiflora and far from Quercus mongolica along one axis. For the PSN analysis of PSN, 40 survey plots were added to investigate the species appearing in a total of 100 survey plots. The network structural analysis showed 378 links and a species having an average of 6 interspecies bindings. The density was 0.097, the diameter was 7, and the average path distance was 2.788, similar to the PSN analysis results of the Busan Metropolitan City. The plant social network analysis showed similar results to the existing interspecies combination analysis, enabling analyzing more data than the existing methods and observing the structure of plant society.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.1
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• pp.89-104
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• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.254-264
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• 2022

The Korean Society of Radiology and Medical Guidelines Committee amended the existing 2016 guidelines to publish the "Korean Clinical Practice Guidelines for Adverse Reactions to Iodide Contrast for Injection and Gadolinium Contrast for MRI: The Revised Clinical Consensus and Recommendations (2022 Third Edition)." Expert members recommended and approved by the Korean Society of Radiology, the Korean Academy of Asthma, Allergy and Clinical Immunology, and the Korean Nephrology Society participated together. According to the expert consensus or systematic literature review, the description of the autoinjector and connection line for the infection control while using contrast medium, the acute adverse reaction, and renal toxicity to iodized contrast medium were modified and added. We would like to introduce the revised contents.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.