• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.62.2-62.2
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• 2019

한국의 고천문 자료는 삼국시대 이후 근대 조선까지 다수가 존재하여 세계적으로 드문 기록 문화를 보유하고 있으나, 한문 번역이 많이 이루어지지 않아 학술적 활용이 활발하지 못한 상태이다. 고문헌의 한문 문장 번역은 전문인력의 수작업에 의존하는 만큼 소요 시간이 길기에 투자대비 효율성이 떨어지는 편이다. 이에 최근 여러 분야에서 응용되는 인공지능의 적용을 대안으로 삼을 수 있으며, 초벌 번역 수준일지라도 자동번역기의 개발은 유용한 학술도구가 될 수 있다. 한국천문연구원은 한국정보화진흥원이 주관하는 2019년도 Information and Communication Technology 기반 공공서비스 촉진사업에 한국고전번역원과 공동 참여하여 인공신경망 기계학습이 적용된 고문헌 자동번역모델을 개발하고자 한다. 이 연구는 고천문 도메인에 특화된 인공지능 기계학습 기법으로 자동번역모델을 개발하여 이를 서비스하는 것을 목적으로 한다. 연구 방법은 크게 4가지 개발을 진행하는 것으로 나누어 볼 수 있다. 첫째, 인공지능의 학습 데이터에 해당되는 '코퍼스'를 구축하는 것이다. 이는 고문헌의 한자 원문과 한글 번역문이 쌍을 이루도록 만들어 줌으로써 학습에 최적화한 데이터를 최소 6만 개 이상 추출하는 것이다. 둘째, 추출된 학습 데이터 코퍼스를 다양한 인공지능 기계학습 기법에 적용하여 천문 분야 특수고전 도메인에 특화된 자동번역 모델을 생성하는 것이다. 셋째, 클라우드 기반에서 참여 기관별로 소장한 고문헌을 자동 번역 모델에 기반하여 도메인 특화된 모델로 도출 및 활용할 수 있는 대기관 서비스 플랫폼 구축이다. 넷째, 개발된 자동 번역기의 대국민 개방을 위해 웹과 모바일 메신저를 통해 자동 번역 서비스를 클라우드 기반으로 구축하는 것이다. 이 연구는 시스템 요구사항 분석과 정의를 바탕으로 설계가 진행 또는 일부 완료되어 구현 중에 있다. 추후 이 연구의 성능 평가는 자동번역모델 평가와 응용시스템 시험으로 나누어 진행된다. 자동번역모델은 평가용 테스트셋에 의한 자동 평가와 전문가에 의한 휴먼 평가에 따라 모델의 품질을 수치로 측정할 수 있다. 또한 응용시스템 시험은 소프트웨어 방법론의 개발 단계별 테스트를 적용한다. 이 연구를 통해 고천문 분야가 인공지능 자동번역 확산 플랫폼 시범의 첫 케이스라는 점에서 의의가 있다. 즉, 클라우드 기반으로 시스템을 구축함으로써 상대적으로 적은 초기 비용을 투자하여 활용성이 높은 한문 문장 자동 번역기라는 연구 인프라를 확보하는 첫 적용 학문 분야이다. 향후 이를 활용한 고천문 분야 학술 활동이 더욱 활발해질 것을 기대해 볼 수 있다.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.6
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• pp.415-426
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• 1997

Some arguments have been about over the representativeness of government-run air quality monitoring stations among scholars and non-governmental organizations (NGOs). However, it is not a simple problem to move monitoring stations because of continuity of data and high cost. So it is necessary to complement the monitoring data if it do not represent the ambient air quality properly. The purpose of this study was to evaluate the representativeness of some monitoring stations using passive $NO_2$ samplers and to find a complementary method from linear regression. Two stations were chosen for the evaluation: Shinlim Station was one of the most controversial stations in Seoul and Banpo Station had the best reputation. Air qualities were surveyed at seven points around each monitoring station with consideration of land use and distance. The ratios of the average $NO_2$ levels of the areas to these at the monitoring stations were 1.59 for Shinlim Station and 1.03 for Banpo Station. The differences between the average $NO_2$ levels and those at the monitoring stations were 10.75 ppb for Shilim Station and 0.34 ppb for Banpo Station. The correlation coefficients between the two levels were 0.7668 for Shinlim and 0.7662 for Banpo. The average coefficients of determination $(R^2)$ were 0.61 for Shinlim and 0.61 for Banpo. The Shinlim Station could not represent the air quality of Shinlim-Dong good because it is located in a green area at an outskirt of Shinlim-Dong. But the Banpo Station located in a central residential area of Banpo-Dong showed a fair representativeness. However, air quality turned out to be different with land use such as residential area, green area or road: the air quality data from a monitoring station located at a certain land use should not be interpreted as representing the air quality at any sites around the station. Equations to predict the average $NO_2$ levels of each area from the data from the monitoring stations were presented based on linear regression.

• Journal of Environmental Science International
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• v.20 no.1
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• pp.81-91
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• 2011

Air quality monitoring networks are very important facilities to manage urban air pollution control and to set up an environmental policy. Since air quality monitoring network of Daegu was allocated from 1980s to mid-90s, there is need to reevaluate it and relocated its site. This study was evaluated the position of Daegu air quality monitoring station by unit environmental sensitivity index, grid emission rate, CAI (Comprehensive Air-quality Index) point. The investigation domain covered an area of 16 $\times$ 24 km centered at the metropolitan area of Daegu with grid spacing of 2 km. The location of alternative air quality monitoring networks was selected through optimization and quintiles analysis of total score. The result showed that all things considered, new air quality monitoring network need to install grid numbers 10, 28, 36, 37, 46. We also recommand three scenarios of alternative air quality monitoring network when considering unit environmental sensitivity index, emission rate and CAI point.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5601-5609
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• 2012

The fine particulate matter($PM_{10}$) concentrations and contents were measured to check the health and environment influential factors in Pohang Iron and Steel Industrial Complex and its vicinities. In addition, the $PM_{10}$ distribution for each year and season was surveyed using the regional air quality monitoring stations. The measuring on the $PM_{10}$ inside the industrial complex showed $61.3{\pm}12.1{\mu}g/m^3$ for average concentration of $PM_{10}$ which was measured by Dongil Industry and $44.3{\pm}8.1{\mu}g/m^3$ measured by steel manufacturing industry complex management office. Both of them satisfied the environmental air quality standard. The percentage of $SO_4{^2}$, $NO_3{^-}$, $NH_4{^+}$ which are the secondary ions created out of the $PM_{10}$ in Dongil Industry and steel manufacturing industry complex management office was checked and it was revealed that the percentage of ${SO_4}^{2-}$ was high and it is considered that the pollution source related with the sulfides exist at the industrial complex. They were in order of ${SO_4}^{2-}$ > $Cl^-$ > $NO_3{^-}$ > $F^-$ > $NH_4{^+}$ in Dongil Industry and ${SO_4}^{2-}$ > $Cl^-$ > $NO_3{^-}$ > $NH_4{^+}$ > $F^-$ in steel manufacturing industry complex management office.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.9
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• pp.644-655
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• 2011

VOCs (Volatile Organic Compounds) have adverse effects on human health and have caused serious global air pollution problems such as ozone depletion and cimate changes. The total of 56 target VOCs were selected to be monitored in this study for 4 years (2006~2009). The VOCs were measured every hour. The concentration of BTEX was higher than the other target compounds. Generally, the levels of VOCs measured in this study were higher than those measured by the other studies because Gamjeon and Jangrim monitering sites are located in industrial areas. The seasonal variations showed that the VOCs were the highest in winter. The temporal variations showed that the VOCs were high during commuting time on weekday. PMF model was used to resolve source types and source contributions of VOCs in this study. Identified sources and quantified contributions resolved by PMF were vehicle exhaust (15.22%), thinning solvent (29.83%), surface coating (17.13%), industries (13.95%), LPG vehicle (15.22%), combustion boiler (7.11%) and biogenic source (6.61%). Thinning solvent and Surface coating were the most contributed sources possibly due to manufactures and automobile garages in Gamjeon and solvent and paint manufactures in Sasang-Gu.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.549-560
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• 2019

The effects of the coal-fired power plant emissions, as the biggest point source of air pollutants, on spatiotemporal surface air pollution over the remote area are investigated in this study, based on a set of date selection and statistical technique to consider meteorological and geographical effects in the emission-concentration (source-receptor) relationship. We here proposed the sophisticated technique of data processing to separate and quantify the effects. The data technique comprises a set of data selection and statistical analysis procedure that include data selection criteria depending on meteorological conditions and statistical methods such as Kolmogorov-Zurbenko filter (K-Z filter) and empirical orthogonal function (EOF) analysis. The data selection procedure is important for filtering measurement data to consider the meteorological and geographical effects on the emission-concentration relationship. Together with meteorological data from the new high resolution ECMWF reanalysis 5 (ERA5) and the Korea Meteorological Administration automated surface observing system, air pollutant emission data from the telemonitoring system (TMS) of Dangjin and Taean power plants as well as spatio-temporal air pollutant concentrations from the air quality monitoring system are used for 4 years period of 2014-2017. Since all the data used in this study have the temporal resolution of 1 hour, the first EOF mode of spatio-temporal changes in air pollutant concentrations over the Seoul metropolitan area (SMA) due to power plant emission have been analyzed to explain over 97% of total variability under favorable meteorological conditions. It is concluded that SO₂, NO₂, and PM₁₀ concentrations over the SMA would be decreased by 0.468, 1.050 ppb, and 2.045 ㎍ m^-3 respectively if SO₂, NO₂, and TSP emissions from Dangjin power plant were reduced by 10%. In the same way, the 10% emission reduction in Taean power plant emissions would cause SO₂, NO₂, and PM₁₀ decreased by 0.284, 0.842 ppb, and 1.230 ㎍ m^-3 over the SMA respectively. Emissions from Dangjin power plant affect air pollution over the SMA in higher amount, but with lower R value, than those of Taean under the same meteorological condition.

• Journal of the Korean earth science society
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• v.23 no.8
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• pp.683-696
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• 2002

In this work. we investigated the ozone data sets that exceeded ambient air quality standards from 31 air quality monitoring stations dispersed across the Seoul metropolitan city during the period covering 1990 and 2000. To specifically describe spatial dependency of high level O$_3$ occurrence, we grouped our data into four different geographical ozone exceedance is much longer in SW than the other three sectors. When we compared the exceedance data in terms of occurrence frequency, the month of maximum frequency differed slightly among different sectors. Examination of long-term exceedance trend indicated that its frequency increased continuously from all sectors over the past years, although slightly opposite patterns existed in their absolute values. Most importantly, its peak occurrence frequency seemed to center in very recent years such as 1998 (NE sector) and 2000 (ail pattern sectors except NE). Consequently, we were able to describe the existence of certain patterns of ozone exceedance data sets in terms of both temporal and spatial scales.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.1
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• pp.38-48
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• 2004

Tropospheric ozone data in Korea for 1998-2002 were analyzed to assess the impact on vegetation. SUM06(sum of hourly concentrations at or above 0.06 ppm) and AOT40(accumulated exposure over a threshold of 40 ppb), widely used as ozone indices in the U.S. and Europe, were calculated based on hourly ozone concentration in 612 areas during 1998-2002 in Korea. SUM06 of the highest 30 areas were 5-12 ppm/hr which were almost the same levels of the U.S. average, and a crop loss of 5% would be expected. Ozone pollution in Seoul during 1998-2002 had decreased compared to that for 1990-97 except in the Northern area; however, ozone pollution in Kyunggi during 1998-2002 had been increased twice compare to the previous 5 years. Korea was divided into four regions: Seoul Metropolitan area, Jungbu, Honam, and Youngnam. Ozone pollution in the Seoul Metropolitan area was much higher during 1998-2000 than the other areas, but ozone pollution during 2001-2002 was almost the same in all four regions. Chunnam-Kwangyang na Kyungbuk-Gumi, famous industrial complexes in southern Korea, were significant ozone pollution areas. However, other industrial complexes, such as Incheon, Ulsan, and Kyunggi-Sihwa, were not polluted compared to their neighbors. Comparing all ozone indices, SUM06(yr), SUM06(3mon), AOT40(yr), AOT40(3mon), number of hours exceeding 100 ppb, 95 percentile, 99 percentile, and maximum concentration, it was determined reasonable to use SUM06(3mon), AOT40(3mon) and number of hours exceeding 100 ppb for evaluation of the chronic impact of ozone on vegetation.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.