• Information Systems Review
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• v.24 no.1
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• pp.21-37
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• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Korean Journal of Plant Resources
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• v.36 no.4
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• pp.299-313
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• 2023

Stellaria pallida (Dumort.) Crép., S. ruderalis M. Lepší, P. Lepší, Z. Kaplan & P. Koutecký, and Cerastium pumilum Curtis are unrecorded alien species of Caryophyllaceae found in Seoul and Gyeonggi-do in the Republic of Korea. Stellaria pallida is readily distinguished from other taxa in the same genus by its petals, as it's considerably smaller than the sepals or absent. In contrast, S. ruderalis is difficult to identify as it has intermediate traits between S. media L. and S. neglecta (Lej.) Weihe. However, S. ruderlais clearly identified by seed morphology, and S. ruderalis, unlike S. media and S. neglecta, has conical shape of outer periclinal wall and papillate on the basely surface of that. Cerastium pumilum is similar to C. glomeratum Thuill. and belongs to the same subgenus (Sub gen. Fugacia); however, it has some distinct characteristics, including stamens that are primarily 8 (5-10), upper bracts with membranous edges, and the length of the pedicels and petals being longer than that of the sepals. The three of alien plants was recorded for the first time in this study, and information of their habitat, distribution map, description and photographs are presented.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.53-62
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• 2013

Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.

• Journal of Korean Society of Forest Science
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• v.87 no.3
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• pp.358-365
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• 1998

To offer the basic information for sustainable production of forest resources and conservation of the global environment, change in potential natural vegetation (PNV) associated with climate change due to doubling atmospheric carbon dioxide ($2{\times}CO_2$) was estimated with the global natural vegetation mapping system based an K${\ddot{o}}$ppen scheme. The system interpolates climate data spherically to each grid cell, determines the vegetation types onto the grid cell, and produces potential vegetation map and area on the globe and continents. The climate data consist of the current, ($1{\times}CO_2$) climate prior to AD 1958 observed at some 2,000 stations and the doubling ($2{\times}CO_2$) climate estimated from Meteorological Research Institute of Japan. The vegetation zone under the $2{\times}CO_2$ climate scenario expanded mainly toward the poles due to the rise in temperature. The changed PNV area on the globe amounts to 1/3 (4.91 billion (G) ha) of the total land area (15.04 Gha). Kappa statistic for judging agreement between the patterns of vegetation distribution under $1{\times}CO_2$ climate and $2{\times}CO_2$ climates shows good agreement (0.63) for the globe as a whole. The most stable areas are desert and ice. The potential forest area (PFA) was estimated at 6.82 Gha of the land area in $2{\times}CO_2$ climate scenario. In terms of continental changes in PFA, North America and Asis are increased under the $2{\times}CO_2$ climate. However, the potential forest arms of the other continents are decreased by the climate. Europe has no change in the PFA. Especially, the expansion of desert area in Oceania would be accelerated by the $2{\times}CO_2$ climate.

• Spatial Information Research
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• v.15 no.2
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• pp.135-146
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• 2007

The purpose of this study was to ascertain and analyze environmental factors of subgenus Lycoctonum in Korea for conservation and management of rare high land plant species by GIS. We derived the habitat model of Lycoctonum from GPS coordination, habitat factors and digital topology maps. Suitable altitude fur the subgenus Lycoctonum is from 470m to 1320m, and northern slopes(between 15.5 and 36 degrees) are ideal for the Lycoctonum populations. In addition to altitude, slope and aspect, deciduous forest and approximation to water source were found as important factor. Using GIS and the Lycoctonum habitat model, we overlaid elevation, aspect, slope and land cover layers and analyzed buffer from the water source on two topology maps, Yang-Soo and Mock-Dong. After making prediction map for Lycoctonum habitat, we verified the existence of Lycoctonum populations on the predicted sites through field survey. Through this research, we're convinced that GIS software is powerful tool for plants conservation, such as finding unknown habitat or selecting alternative habitat.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.617-623
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• 2019

Numerous water disasters have recently occurred all over the world, including South Korea, due to global climate change in recent years. As water-related disasters occur extensively and their sites are difficult for people to access, it is necessary to monitor them using satellites. The Ministry of Environment and K-water plan to launch the next-generation medium satellite No. 5 (water resource/water disaster satellite) equipped with C-band synthetic aperture radar (SAR) in 2025. C-band SAR has the advantage of being able to observe water resources twice a day at a high resolution both day and night, regardless of weather conditions. Currently, RADARSAT-2 and Sentinel-1 equipped with C-band SAR achieve the purpose of their launch and are used in various environmental fields such as forest structure detection and coastline change monitoring, as well as for unique purposes including the detection of flooding, drought and soil moisture change, utilizing the advantages of SAR. As such, this study aimed to analyze the characteristics of the next-generation medium satellite No. 5 and its application in environmental fields. Our findings showed that it can be used to improve the degree of precision of existing environmental spatial information such as the classification accuracy of land cover map in environmental field works. It also enables us to observe forests and water resources in North Korea that are difficult to access geographically. It is ultimately expected that this will enable the monitoring of the whole Korean Peninsula in various environmental fields, and help in relevant responses and policy supports.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.19-32
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• 2024

This study examined the feasibility of image-based surveys by detecting objects in facilities and agricultural land using the YOLO algorithm based on drone images and comparing them with the land category by law. As a result of detecting objects through the YOLO algorithm, buildings showed a performance of detecting objects corresponding to 96.3% of the buildings provided in the existing digital map. In addition, the YOLO algorithm developed in this study detected 136 additional buildings that were not located in the digital map. Plastic greenhouses detected a total of 297 objects, but the detection rate was low for some plastic greenhouses for fruit trees. Also, agricultural land had the lowest detection rate. This result is because agricultural land has a larger area and irregular shape than buildings, so the accuracy is lower than buildings due to the inconsistency of training data. Therefore, segmentation detection, rather than box-shaped detection, is likely to be more effective for agricultural fields. Comparing the detected objects with the land category by law, it was analyzed that some buildings exist in agricultural and forest areas where it is difficult to locate buildings. It seems that it is necessary to link with administrative information to understand that these buildings are used illegally. Therefore, at the current level, it is possible to objectively determine the existence of buildings in fields where it is difficult to locate buildings.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.125-137
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• 2008

This research aims to produce basic data for developing habitat suitability models on the breeding sites of Oriental White Storks(Ciconia boyciana) which will be reintroduced to the wild in the future. The habitat characteristics of ten historical nesting sites of the Oriental White Storks at Gyeonggi and Chungcheong provinces in South Korea were analyzed with 1970's land use maps and Landsat MSS. The range of altitude on nesting sites was 40~116.38m. The mean distance from nesting sites to rice fields, to 30m wider river, and to reservoirs was $54.8{\pm}84.48m$, $869.8{\pm}708.01m$, and $1721.2{\pm}906.05m$ respectively. Historical nesting sites were located close to human settlements, and the mean distance of nesting sites to human settlements was $144.1{\pm}182.97m$. The land types within 5km radius from ten historical nesting sites consisted of 53.7% forest, 28.3% rice fields, 16.7% grasslands, 0.8% water bodies, and 0.6% human settlements. The composition of four land types(forest, rice fields, grasslands, and human settlements) was significantly differed between 93 random points and 10 historical nesting sites.

• Spatial Information Research
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• v.10 no.2
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• pp.247-261
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• 2002

There are much damage of people and property because of heavy rain every year. Especially, there are problem to major facility such as dam, bridge, road, tunnel, and industrial complex in the ground stability. So the counter plan for landslide or ground failure must be necessary In the study, the technique of regional landslide susceptibility assessment near the Ulsan petrochemical complex and Kumgang railway bridge was developed and applied using GIS. For the assessment, the geological structures such as bedding and fault were surveyed and the geological structure, topographic, soil, forest, and land use spatial database were constructed using CIS. Using the spatial database, the factors that influence landslide occurrence, such as slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of forest, and land use were calculated or extracted from the spatial database. For application of geological structure, the geological structure line and fault density were calculated. Landslide susceptibility was analyzed using the landslide-occurrence factors by probability method that is summation of landslide occurrence probability values per each factors range or type. The landslide susceptibility map can be used to assess ground stability to protect major facility.