• Journal of Oral Medicine and Pain
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• v.33 no.2
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• pp.121-132
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• 2008

To establish the diagnosis and treatment plan for skeletal Class II malocclusion, patient's skeletal morphology, prognosis as well as the treatment effect is one of the important factor to consider. Therefore, the present study classified analyzed the difference between initial(T1) and after use of activator(T2), and after finish of direct multi-bonding system treatment(T3) for Class II malocclusion during growth period according to the treatment result(effective body length) and morphology of vertical skeletal type. The experimental group was classified into two groups(1 group, 2 group) according to the effective body length change between before and after use of activator, showed good treatment effect of activator for patient with small mandible and large differential between maxilla and mandible, and short anterior facial height. And the difference between 1 and 2 group in the experimental group before treatment(T1) disappeared in the finished treatment(T3). But in contrast, the initial difference of T1 stage between a and b group in the control group did not disappear in the finished treatment(T3). In short, experimental group's treatment effect was much better than contrast group and the treatment effect was maintained and got stable results at comparison experimental group with contrast group. Through this study, we can find activator's treatment effect and stable retention of that in growing Class II malocclusion patients. By estimate of activator treatment effect through these results, we can establish the correct diagnosis and treatment plan for adolescent Class II malocclusion estimate of activator treatment effect and lead the ideal facial growth pattern.

• Journal of the Korean earth science society
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• v.28 no.4
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• pp.478-490
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• 2007

With groundwater data of seawater intrusion monitoring network in coastal areas of Korea's main land, we analyzed types of seawater intrusion through the coastal aquifer. The data including groundwater level, temperature and electrical conductivity obtained from 45 monitoring wells at 25 watershed regions were evaluated. Based on statistical analysis, correlation analysis and variation type analysis, groundwater levels were mainly affected by rainfall and artificial pumping. About 78% of the monitoring wells showed average temperature higher than $15^{\circ}C$ and about 58% of them showed minimum variations less than $0.2^{\circ}C$. Electrical conductivities showed a large magnitude of variation and irregular characteristics compared with groundwater levels and temperatures. Average electrical conductivities lower than $2,000\;{\mu}S/cm$ were observed at 28 monitoring wells while those of higher than $10,000\;{\mu}S/cm$ were done at 9 monitoring wells. From the cross-correlation analysis, groundwater levels were mostly affected by precipitation while temperature and electrical conductivity showed very low correlation. Meanwhile tidal variations strongly affected the groundwater levels comparing to precipitation. We classified the long-term monitoring data according to variation types such as constant process, linear trend, cyclic variation, impulse, step function and ramp. Impulse type was dominant for variations of groundwater level, which was largely affected by rainfall or artificial pumping, the constant process was dominant for temperature. Compared with groundwater level and temperature, electrical conductivities showed various types like linear trend, step function and ramp. According to the discrepancy of variation characteristics for monitoring data at each well in the same region, periodical analysis of monitoring data is essentially required.

• The Journal of the Petrological Society of Korea
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• v.16 no.4
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• pp.208-216
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• 2007

Based on digital geologic and geomorphic maps of 1 : 250,000 scale, distributive ratios of rock types were obtained by ArcGIS 9.0 program in the Gyeonggi, Seoul and Incheon areas of the Gyeonggi province. In the Gyeonggi area, 37 rock types are developed, and their geologic ages can be classified into Precambrian, Age-unknown, Triassic, Jurassic, Cretaceous and Quatemary. Among them, distributive ratios are decreasing in the order of Jurassic Daebo granites, Precambrian banded gneiss of Gyeonggi gneiss complex and Quatemary alluvium, all of which comprise about 83.7% of the rock types in the area. In the Seoul and Incheon areas, 10 and 15 rock types are developed, respectively., with the firmer being classified into Precambrian, Jurassic and Quatemary, and the latter into Precambrian, Jurassic, Cretaceous and Quatemary. In the Seoul area, distributive ratios are decreasing in the order of banded gneiss of Gyeonggi gneiss complex, Daebo granites and alluvium, which consist of 95.5% of the rocks in the area. In the Incheon area, distributive ratios are decreasing in the order of alluvium, Daebo granites, banded gneiss of Gyeonggi gneiss complex, reclaimed land, and schists of Gyeonggi gneiss complex, which occupy about 96.2% of the rocks in the area. The ratio of alluvium in the Incheon area is greater than that of Gyeonggi and Seoul areas, and the ratio of reclaimed land in the Incheon area is greater that of the Seoul, which can be attributed to the recent reclamation of the land for the industrial results such as new town development along the coastline of the Gyeonggi Bay.

• The Korean Journal of Quaternary Research
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• v.18 no.1 s.22
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• pp.21-30
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• 2004

The Ilgwang Fault is NNE-striking, elongated 40 Km between Ulsan and Haeundae-ku, Busan in southeastern part of the Korean Peninsula. This paper si mainly concerned about the ages of the fault activities especially in the Quaternary, inferred from classification of geomorphic surfaces and trench excavation for the construction of Singori nuclear power plant. The geomorphic surfaces are classified into Beach and the Alluvial plain, the 10 m a.s.l. Marine terrace(MIS 5a), the 20 m a.s.l. Marine terrace(MIS 5e), the Reworked surface of 45 m a.s.l. Marine terrace(MIS 7 or 9) and the Low relief erosional surface. The Low relief erosional surface is distributed coastal side, the Reworked surface of 45m a.s.l. Marine terrace inland side by the Ilgwang Fault Line as the boundary line. But the former is above 10 m higher in relative height than the latter. The 20 m a.s.l. Marine terrace on the elongation line of the Ilgwang Fault reveals no dislocation. A site was trenched on the straight contact line with $N30^{\circ}E$-striking between the 10 m a.s.l. Marine terrace and the 20 m a.s.l. Marine terrace. Fault line or dislocation was not observable in the trench excavation. Accordingly, the straight contact line is inferred as the ancient shore line of the 10 m a.s.l. Marine terrace. The Ages of the Fault activities are inferred after the formation of the Ichonri formation - before the formation of the 45 m a.s.l. Marine terrace(220 Ka. y. B.P. or 320. Ka. y. B.P.). The Low relief erosional surface was an island above the sea-level during the formation of the 45 m a.s.l. marine terrace in the paleogeography.

• Korean Journal of Environment and Ecology
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• v.37 no.4
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• pp.259-272
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• 2023

This study set up 49 survey areas with an area of about 400 square meters in Abies koreana natural habitat to identify the species composition and vegetation structure of the A. koreana forest in the Mt. Jiri Nation Park, conducted field surveys using phytosociological methods, and performed the cluster analysis using the Two-Way Indicator Species Analysis (TWINSPAN) and Table manipulation. Subsequently, species composition analysis using the importance value, species diversity analysis, DBH analysis, sapling analysis, and similarity analysis was conducted by each cluster type. The cluster analysis classified the A. koreana forest in Mt. Jiri into five clusters, A, B, C, D, and E. The forest was divided into two clusters, Magnolia sieboldii-Dryopteris crassirhizoma-Sasa borealis and Betula ermanii-Solidago virgaurea-Calamagrostis arundinacea. The former was classified as type A and B by Cornus controversa-Hydrangea macrophylla, and the latter was classified as type E, a typical community, and a Sorbus commixta-Rhododendron mucronulatum cluster. And the S. commixta-R. mucronulatum cluster was divided into C type and D type by Picea jezoensis-Ligularia fischeri and Ainsliaea acerifolia. Through vegetation analysis, the importance value of A. koreana, Quercus mongolica, Acer pseudosieboldianum, Fraxinus sieboldiana, and B. ermanii was highly expressed in the A. koreana forest in Mt. Jiri. Regarding species diversity, the results were similar to those reported in other studies of A. koreana forests in Mt. Jiri. The analysis of diameter at breast height (DBH) showed that A. koreana dominated all layers, and the growth of saplings was also good, indicating that the dominance of A. koreana is expected to continue for a while. However, when considering the value of biodiversity that is expected to increase and threats caused by climate change, systematic preservation and management are required to respond to various threats based on continuous monitoring.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.119-135
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• 2021

Korea has stepped up efforts to investigate and catalog its flora and fauna to conserve the biodiversity of the Korean Peninsula and secure biological resources since the ratification of the Convention on Biological Diversity (CBD) in 1992 and the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits (ABS) in 2010. Thus, after its establishment in 2007, the National Institute of Biological Resources (NIBR) of the Ministry of Environment of Korea initiated a project called the Korean Indigenous Species Investigation Project to investigate indigenous species on the Korean Peninsula. For 15 years since its beginning in 2006, this project has been carried out in five phases, Phase 1 from 2006-2008, Phase 2 from 2009-2011, Phase 3 from 2012-2014, Phase 4 from 2015-2017, and Phase 5 from 2018-2020. Before this project, in 2006, the number of indigenous species surveyed was 29,916. The figure was cumulatively aggregated at the end of each phase as 33,253 species for Phase 1 (2008), 38,011 species for Phase 2 (2011), 42,756 species for Phase 3 (2014), 49,027 species for Phase 4 (2017), and 54,428 species for Phase 5(2020). The number of indigenous species surveyed grew rapidly, showing an approximately 1.8-fold increase as the project progressed. These statistics showed an annual average of 2,320 newly recorded species during the project period. Among the recorded species, a total of 5,242 new species were reported in scientific publications, a great scientific achievement. During this project period, newly recorded species on the Korean Peninsula were identified using the recent taxonomic classifications as follows: 4,440 insect species (including 988 new species), 4,333 invertebrate species except for insects (including 1,492 new species), 98 vertebrate species (fish) (including nine new species), 309 plant species (including 176 vascular plant species, 133 bryophyte species, and 39 new species), 1,916 algae species (including 178 new species), 1,716 fungi and lichen species(including 309 new species), and 4,812 prokaryotic species (including 2,226 new species). The number of collected biological specimens in each phase was aggregated as follows: 247,226 for Phase 1 (2008), 207,827 for Phase 2 (2011), 287,133 for Phase 3 (2014), 244,920 for Phase 4(2017), and 144,333 for Phase 5(2020). A total of 1,131,439 specimens were obtained with an annual average of 75,429. More specifically, 281,054 insect specimens, 194,667 invertebrate specimens (except for insects), 40,100 fish specimens, 378,251 plant specimens, 140,490 algae specimens, 61,695 fungi specimens, and 35,182 prokaryotic specimens were collected. The cumulative number of researchers, which were nearly all professional taxonomists and graduate students majoring in taxonomy across the country, involved in this project was around 5,000, with an annual average of 395. The number of researchers/assistant researchers or mainly graduate students participating in Phase 1 was 597/268; 522/191 in Phase 2; 939/292 in Phase 3; 575/852 in Phase 4; and 601/1,097 in Phase 5. During this project period, 3,488 papers were published in major scientific journals. Of these, 2,320 papers were published in domestic journals and 1,168 papers were published in Science Citation Index(SCI) journals. During the project period, a total of 83.3 billion won (annual average of 5.5 billion won) or approximately US $75 million (annual average of US $5 million) was invested in investigating indigenous species and collecting specimens. This project was a large-scale research study led by the Korean government. It is considered to be a successful example of Korea's compressed development as it attracted almost all of the taxonomists in Korea and made remarkable achievements with a massive budget in a short time. The results from this project led to the National List of Species of Korea, where all species were organized by taxonomic classification. Information regarding the National List of Species of Korea is available to experts, students, and the general public (https://species.nibr.go.kr/index.do). The information, including descriptions, DNA sequences, habitats, distributions, ecological aspects, images, and multimedia, has been digitized, making contributions to scientific advancement in research fields such as phylogenetics and evolution. The species information also serves as a basis for projects aimed at species distribution and biological monitoring such as climate-sensitive biological indicator species. Moreover, the species information helps bio-industries search for useful biological resources. The most meaningful achievement of this project can be in providing support for nurturing young taxonomists like graduate students. This project has continued for the past 15 years and is still ongoing. Efforts to address issues, including species misidentification and invalid synonyms, still have to be made to enhance taxonomic research. Research needs to be conducted to investigate another 50,000 species out of the estimated 100,000 indigenous species on the Korean Peninsula.

• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Life Science
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• v.20 no.4
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• pp.555-560
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• 2010

The objective of this study was to investigate levels of serum creatinine, CuSOD and MnSOD protein expression in the kidney after renal ischemic reperfusion with pre-exercise using heat shock protein 70.1 in knock-out mice (KO). The C57/BL6 strain (Wild type: WT) and KO were divided into 4 groups as follows: Sham control group (Sham), pre-exercise group (Ex), pre-exercise +ischemia group (Ex+IR), and ischemia group (IR). CuSOD and MnSOD expression were significantly decreased (p<0.01, p<0.05) and blood creatinine concentration was significantly increased (p<0.01) in the IR group of KO. In contrast, CuSOD and MnSOD expression in the Ex+IR group of KO were higher than the IR group, while creatinine concentration was significantly lower. These results suggest that Hsp70 is directly correlated to renal ischemic reperfusion injury. Pre-exercise in renal ischemia might prevent or inhibit positive oxidative stress inhibitory effects by increasing anti-oxidative enzymes (CuSOD, MnSOD) within the kidney and improve to prevent renal function. Thus, pre-exercise may have a protective role against renal injury after renal ischemia.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.33 no.4
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• pp.87-98
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• 2015

The natural monument forests (no.374) located at Pyeongdae-ri in Jeju island are described and classified by using phytosociological methods and numerical analysis. The purpose of this paper is to identify the ecological character of Torreya nucifera forests between natural habitat and artificial habitat, as well as their spatial and phytogeographical distribution in the Korea. The comparison of forests between Pyeongdae-ri and other regions was analyzed by using a non-metric multidimensional scaling analysis (NMDS) and hierarchical clustering. On the basis of the 12 phytosociological $relev{\acute{e}}s$, the vegetation of T. nucifera dominant forest in Jeju island was arranged in one syntaxon (Alangium platanifolium-Torreya nucifera community included typicum and one subcommunity) within Camellietea. The community of T. nucifera dominant forests were characterized floristically and ecologically. We discussed diagnostic species with references, and proposed a few important diagnostic species (Ilex crenata for. microphylla, Acer palmatum, Zingiber mioga, Mercurialis leiocarpa, Osmorhiza aristata, Mecodium wrightii etc.) to explain condition of the habitat and synecological character. The communities were described by concerning their edaphical and syndynamical niche; we discussed their total distribution in Korea. In most forests they are widespread in Korean peninsular and their distribution is primarily determined by artificial plantation and periodical management. The forests consisted of T. nucifera have developed from natural environment element and artificial management. As a result they have very unique characters with the floristic, structural characterization and distribution. Furthermore, we identified that they need to apposite management for sustainability.