• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.95-118
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• 2010

This study aims to investigate the spatial distribution of major soil types in Korea, and to assess the ability to predict soil distribution using environmental variables. A classification tree method was used to assess soil predictability. While the great soil groups can give more intuitive understandings on their spatial distributions, its predictability using environmental factors is much lower than that of the great groups. The most important factor to determine the spatial distribution of major soil types is the geomorphological characteristic of Korea that shows distinctive morphological difference between mountains and plains. Spatial distribution of climatic variables and catenary soil sequence along slopes play additional roles in determining the distribution of soil types. The classification tree models resulted in 35-75% of prediction accuracy, depends on the combination of different environmental variables brought in the models. While geomorphological variables are the best predictors for the great groups, climatic variables perform better for the great soil groups.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.509-516
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• 2012

In this research, it was intended to examine the vulnerability of Pinus rigida to climate changes, a major planting species in Korea. For this purpose, the distribution of Pinus rigida and its changes caused by climate changes were estimated based on the 'A1B' climate change scenario suggested by IPCC. Current distribution of Pinus rigida was analyzed by using the $4^{th}$Forest Type Map and its potential distribution in the recent year (2000), the near future (2050) and the further future (2100) were estimated by analyzing the optimized ranges of three climate indices - warmth index(WI), minimum temperature index of the coldest month (MTCI) and precipitation effectiveness index(PEI). The results showed that the estimated potential distribution of Pinus rigida declines to 56% in the near future(2050) and 15% in the further future (2100). This significant decline was found in most provinces in Korea. However, in Kangwon province where the average elevation is higher than other provinces, the area of potential distribution of Pinus rigida increases in the near future and the further future. Also the result indicated that the potential distribution of Pinus rigida migrates to higher elevation. The potential distributions estimated in this research have relatively high accuracy with consideration of classification accuracy (44.75%) and prediction probability (62.56%).

• Spatial Information Research
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• v.22 no.5
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• pp.19-26
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• 2014

International attention on the Northern Sea Route has been increased as the decreased sea-ice extents in Northern Sea raise the possibility to develop new sea routes and natural resources. However, to protect ships' safety and pristine environments in polar waters, International Maritime Organization(IMO) has been developing the Polar Code to regulate polar shipping. The marine navigation supporting system is essential for ships traveling long distance in the Northern Sea as they are affected by ocean weather and sea-ice. Therefore, to cope with the IMO Polar Code, this research proposes the functional requirements to develop the marine navigation supporting system for the Northern Sea Route. The functional requirements derived from the IMO Polar code consist of arctic voyage risk map, arctic voyage planning and MSI(Marine Safety Information) methods, based on which the navigation supporting system is able to provide dynamic and safe-economical sea route service using the sea-ice observation and prediction technologies. Also, a requirement of the system application is derived to apply the marine navigation supporting system for authorizing ships operating in the Northern Sea. To reflect the proposed system in the Polar Code, continual international exchange and policy proposals are necessary along with the development of sea-ice observation and prediction technologies.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.221-239
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• 2022

Unknown geotechnical characteristics are key challenges in the design of piles for the plant, civil and building works. Although the N-values which were read through the standard penetration test are important, those N-values of the whole area are not likely acquired in common practice. In this study, the N-value is predicted by means of regression analysis with artificial intelligence (AI). Big data is important to improve learning performance of AI, so circular augmentation method is applied to build up the big data at the current study. The optimal model was chosen among applied AI algorithms, such as artificial neural network, decision tree and auto machine learning. To select optimal model among the above three AI algorithms is to minimize the margin of error. To evaluate the method, actual data and predicted data of six performed projects in Poland, Indonesia and Malaysia were compared. As a result of this study, the AI prediction of this method is proven to be reliable. Therefore, it is realized that the geotechnical characteristics of non-boring points were predictable and the optimal arrangement of structure could be achieved utilizing three dimensional N-value distribution map.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Korean Geotechnical Society
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• v.25 no.1
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• pp.5-19
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• 2009

Most of earthquake-induced geotechnical hazards have been caused by the site effects relating to the amplification of ground motion, which is strongly influenced by the local geologic conditions such as soil thickness or bedrock depth and soil stiffness. In this study, an integrated GIS-based information system for geotechnical data, called geotechnical information system (GTIS), was constructed to establish a regional counterplan against earthquake-induced hazards at an urban area of Daejeon, which is represented as a hub of research and development in Korea. To build the GTIS for the area concerned, pre-existing geotechnical data collections were performed across the extended area including the study area and site visits were additionally carried out to acquire surface geo-knowledge data. For practical application of the GTIS used to estimate the site effects at the area concerned, seismic zoning map of the site period was created and presented as regional synthetic strategy for earthquake-induced hazards prediction. In addition, seismic zonation for site classification according to the spatial distribution of the site period was also performed to determine the site amplification coefficients for seismic design and seismic performance evaluation at any site in the study area. Based on this case study on seismic zonations in Daejeon, it was verified that the GIS-based GTIS was very useful for the regional prediction of seismic hazards and also the decision support for seismic hazard mitigation.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.105-121
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• 2009

The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (${\ge}$14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.667-675
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• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.81-90
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• 2005

Based on the landslide hazard scoring system of Korea Forest Research Institute, a GIS model for predicting landslide hazards was developed. The risk of landslide hazards was analyzed as the function of 7 environmental site factors for the terrain, vegetation, and geological characteristics of the corresponding forest stand sites. Among the environmental factors, slope distance, relative height and shapes of slopes were interpreted using the forestland slope interpretation module developed by Chung et al. (2002). The program consists of three modules for managing spatial data, analyzing landslide hazard and report-writing, A performance test of the model showed that 72% of the total landslides in Youngin-Ansung landslides area took place in the highly vulnerable zones of grade 1 or 2 of the landslide hazard scoring map.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.133-148
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• 2016

Voices demanding a revision of the aviation law on the operating drones are continuously rising high with the increase of their applicability in various industry fields. According to the current regulations, drones are permitted to fly under very strict conditions, which include limited places and the line-of-sight visibility from pilots. Because of the strict regulations, it is almost impossible for drones to be used in many industries such as parcel delivery services. To improve the business value of drones, we have to improve the accuracy of drones' positions and provide the proper protection levels in order to detect and avoid any risks including the collisions with the other drones. SBAS(Satellite Based Augmentation System) can support the aviation requirements with the accuracy and integrity so as to reduce the position errors and to calculate the protection levels of drones. In this paper, we assign the flight heights of drones according to the decision heights as per LAAS(Local Area Augmentation System) landing categories and conduct a simulation to predict the SBAS available time of the day.