• Spatial Information Research
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• v.20 no.3
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• pp.57-66
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• 2012

Gangwon-do has been suffering extensive landslide dam age, because its geography consists mainly of mountains. Analyzing the related factors is crucial for landslide prediction. We digitized the landslide and non-landslide spots on an aerial photo obtained right after a disaster in Inje, Gangwon-do. Three landslide factors-topographic, forest type, and soil factors-w ere statistically analyzed through GIS overlap analysis between topographic map, forest type map, and soil map. The analysis showed that landslides occurred mainly between the inclination of $20^{\circ}$ and $35^{\circ}$, and needleleaf tree area is more vulnerable to a landslide. About soil properties, an area with shallow effective soil depth and parent material of acidic rock has a greater chance of landslide.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.981-992
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• 2023

This study, the effect of organizational culture perceived by millennial Naval Personnel working on naval ships on organizational citizenship behavior was empirically analyzed. First, it was found that OC(group/hierarchy) had a positive effect on OCB. Second, affective commitment plays a mediating role in the relationship between OC(group/hierarchy) and OCB. It was confirmed that organizational culture has a direct effect on OCB, but also indirectly through affective commitment. Third, Coworker social loafing had a negative moderating effect in the relationship. Therefore, in order to improve organizational citizenship behavior, it is necessary to improve a balanced organizational culture suitable for changes in the organizational environment, Strengthen the key influencers for organizational members to affective commitment themselves in the organization, and promote improvement in social loafing.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.51-65
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• 2024

Landslides are natural disasters that causes significant property damage worldwide every year. In Korea, damage due to landslides is increasing owing to the effects of climate change, and it is important to identify the factors that increase the prevalence of landslides in order to reduce the damage they cause. Therefore, this study used a random forest model to analyze the importance of 14 factors in influencing landslide damage in a specific area of Chungju, Chungcheongbuk-do province, Korea. The random forest model performed accurately with an AUC of 0.87 and the most-important factors were ranked in the order of aspect, slope, distance to valley, and elevation, suggesting that topographic factors such as aspect and slope more greatly influence landslide damage than geological or soil factors such as rock type and soil thickness. The results of this study are expected to provide a basis for mapping and predicting landslide damage, and for research focused on reducing landslide damage.

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.2
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• pp.91-97
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• 1979

1. Nitrogenous compounds of-organic materials as nitrogen sources for Korean ginseng were characterized according to their solubility and chemical forms. 2. The extractable fractionation was as follows : Yakto group : non-extractable N > acid hydrolyzable N > acid nonhydrolyzable N > water sol. N, Litter group : acid hydrolyzable N > non-extractable N > water sol. N > acid non-hydrolyzable N, Bone meal : acid hydrolyzable N > water sol. N > acid non-hydrolyzable N. 3. Nitrogenous compounds in the water sol. fraction were : Yakto group and Litter group : humus N > amino acid N > nitrate N (recognized only in Yakto group) > ammonia N > hexosamine N > amide N, Bone meal : amino acid N > humus N > ammonia N > amide N. And nitrogenous compounds in the acid hydrolyzable fraction were : amino acid N > humus N ${\simeq}$ ammonia N > hexosamine N. 4. Availability was discussed about the major nitrogenous compounds (amino acid, humus and inorganic N) and the solubility.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.23-29
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• 2015

Due to the characteristics of landslide disasters including debris flow, the rapid speed to downward and difficulty to respond or evacuate from them, it is imperative to identify their potential hazards and prepare the reduction plans. However, the current landslide hazards generated by a variety of methods has been raised its accuracy because of the complexity of input data and their analyses, and the simplification of the landslide model. The main objective of this study is, therefore, to evaluate the preliminary landslide hazard based on the identification of geomorphological features. Especially, two methodologies based on the statistics of the directional data, Vector dispersion and Planarity analyses, are used to find some relationships between geomorphological characteristics and the landslide hazard. Results show that both methods well discriminate geomorphological features between stable and unstable domains in the landslide areas. Geomorphological features are closely related to the landslide hazard and it is imperative to maximize their characteristics by adapting multiple models rather than individual model only. In conclusions, the mechanism of landslide is not determined solely by a simple cause but the complex natural phenomenon caused by the interactions of the numerous factors and it is of primary importance to require additional researches for the outbreaking mechanism that are based on various methodologies.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.191-196
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• 2008

This study is a case study for application of the RTI warning model to Korea which was previously developed to predict landslide potential and occurrence time during a rainfall event. The rainfall triggering index (RTI) is defined as the product of the rainfall intensity I (mm/hr) and the effective accumulated rainfall $R_t$ (mm). This index is used to evaluate the landslide and debris-flow occurrence potential at time t during a rainfall event. The upper critical value ($RTI_{UC}$) of RTI and the lower critical value ($RTI_{LC}$) of RTI can be determined by historical rainfall data of a certain area. When the rainfall intensity exceeds the upper critical value, there are high potential to occur land-slides. The analysis result can predict landslide occurrence time of an area during a rainfall event as well as land-slide potential. The result can also be used as an important data to issue early-warning of landslides. In order to apply the RTI warning model to Korea this study analyzed rainfall data and landslides data in Inje county, Gangwon province, Korea from July 13 to July 19, 2006. According to the analysis result, the rainfall intensity exceeded the upper critical value 23 hours ago, 11 hours ago, and 9 hours ago from 11:00 in the morning, July 16. Therefore, landslide warnings would be issued three times for people evacuation for avoiding or reducing hurts and dam-ages from landslides in mountainous areas of Inje.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.27-39
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• 2008

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In summer, landslides frequently occur due to local torrential rains and storms. It is critical to predict the potential areas of landslides in advance and to take preventive measures to minimize consequences and to protect property and human life. The previous study on landslides mostly focused on identifying the causes of landslides in the areas where they occurred, and on analyzing landslide vulnerability around the areas without considering rainfall conditions. Thus there were not enough evaluations of the direct risk of landslides to human life. In this study, potentially risky areas for landslides were identified using the GIS data in order to evaluate direct risk on farmlands, roads, and artificial structures that were closely connected to human life. A map of landslide risk was made taking into account rainfall conditions, and a land use map was also drawn with satellite images and digital maps. Both maps were used to identify potentially risky areas for landslides.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.177-180
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• 2007

품사 태거를 구축할 때 어휘사전 증축이나 변환을 통해 성능 개선을 시도하지만 적당한 품사 태깅 코퍼스의 부재와 태그셋 불일치로 인한 변환 과정에 어려움을 겪고 있다. 본 논문에서는 세종 말뭉치 품사 태깅 코퍼스를 이용하여 품사 태깅용 어휘사전을 증축하고 품사 태거에 적용하여 성능을 개선하는 과정을 기술하였다. 품사 태거의 성능을 개선하기 위하여 세종 코퍼스를 태거의 태그셋에 적합하게 변환하고, 변환된 코퍼스에서 추출된 통계 정보를 품사 태거에서 활용하였다. 세종 코퍼스를 이용하여 품사 태거를 위한 어휘사전을 보강함으로써 품사 태거의 성능을 향상시킬 수 있었다.