• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.509-512
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• 2003

Our country is serious difference of precipitation seasonally and about 66% of yearly mean rainfall is happening in concentration rainfall form between September on June. It requires consideration because of a lot of natural disasters by this downpour are produced. Slope failure is happened by artificial factor of creation of slope according to the land development, fill slope etc. and natural factor of rainfall, topography, nature of soil, soil quality, rock floor. Usually, Direct factor of failure slope is downpour. In this study, the Slope about among 55 places happened failure by downpour investigated occurrence position, geological etc and executed and inquire into character of rainfall connected with failure slope. Among character of rainfall, executed analysis about Max. hourly rainfall and cumulative rainfall of place that failure slope is situated and grasped the geological character of failure slope. Through this, inquire to character of failure slope by rainfall and take advantage of basis study for Hazard map creation.

• Journal of Aquaculture
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• v.8 no.4
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• pp.285-294
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• 1995

In Bouin polder, one of the oyster culture zone on landbase in France, artificial substrate was suspended every week to study the variation of the biomass of benthic microalgae as the chlorophyll a. The meteorological and physico-chemical factors in sea water were studied by analysing the correlationship, correlation circle and principal component of these factors. Among the meteorological factors such as insolation, precipitation and wind, insolation was one of the most prominant factors associated with the increase of water temperature, salinity, pH and biomass but with the decrease of turbidity, dissolved oxygen and nitrate. Nitrate was the main contributor for the variation of biomass among the other nutrient components, while phosphate and silicate increased in summer when the biomass increased.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1412-1416
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• 2009

최근의 극심한 기상이변으로 인하여 발생되는 이상호우의 예측에 관한 사항은 치수 이수는 물론 방재의 측면에서도 역시 매우 중요한 관심사로 부각되고 있다. 강우를 예측하기 위해 많은 방법들이 사용되고 있으나 강우의 메커니즘은 매우 복잡하여 수문순환과정에서 가장 예측하기 힘든 요소이며, 추계학적 예측모형이나 확정론적 예측모형 모두에 있어 상당한 불확실성을 내포하고 있다. 기상예측모형 등을 이용하여 강우예측에 대한 정도를 높여가고는 있으나 많은 수문학적 모형에서 요구하는 시공간적으로 정도가 높은 강우를 예측하기에는 힘들다. 인공신경망은 과거자료의 입 출력 패턴에서 정보를 추출하여 지식으로 보유하고, 이를 근거로 새로운 상황에 대한 해답을 제시하도록 하는 인공지능분야의 학습기법으로 인간이 과거의 경험과 훈련으로 지식을 축적하듯이 시스템의 입 출력에 의하여 연결강도를 최적화함으로서 모형의 구조를 스스로 조직화하기 때문에 모형의 구조에 적합한 최적 매개변수를 추정할 수 있다. 따라서 정확한 예측이 어려운 강우사상을 과거의 자료로부터 신경망의 수학적 알고리즘을 통해 강우의 예측에 적용할 수 있을 것이다. 따라서 본 연구에서는 이러한 인공신경망의 기법 중 오류 역전파 알고리즘을 통하여 과거의 강우사상들을 입 출력 자료로 이용하여 인공신경망을 학습시켜 강우의 예측에 대한 정도를 높이도록 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1027-1031
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• 2008

본 연구는 한반도 영역을 대상으로 2001년 7, 8월과 2002년 6월로 홍수기를 대상으로 RDAPS 모형, AWS, 상층기상관측(upper-air sounding)의 자료를 이용하였다. 또한 수치예보자료를 범주적 예측확률로 변환하고 인공신경망기법(ANN)을 이용하여 강수발생확률의 예측정확성을 향상시키는데 있다. 신경망의 예측인자로 사용된 대기변수는 500/ 750/ 1000hpa에서의 지위고도, 500-1000hpa에서의 층후(thickness), 500hpa에서의 X와 Y의 바람성분, 750hpa에서의 X와 Y의 바람성분, 표면풍속, 500/ 750hpa/ 표면에서의 온도, 평균해면기압, 3시간 누적 강수, AWS관측소에서 관측된 RDAPS모형 실행전의 6시간과 12시간동안의 누적강수, 가강수량, 상대습도이며, 예측변수로는 강수발생확률로 선택하였다. 강우는 다양한 대기변수들의 비선형 조합으로 발생되기 때문에 예측인자와 예측변수 사이의 복잡한 비선형성을 고려하는데 유용한 인공신경망을 사용하였다. 신경망의 구조는 전방향 다층퍼셉트론으로 구성하였으며 역전파알고리즘을 학습방법으로 사용하였다. 강수예측성과의 질을 평가하기 위해서 $2{\times}2$ 분할표를 이용하여 Hit rate, Threat score, Probability of detection, Kuipers Skill Score를 사용하였으며, 신경망 학습후의 강수발생확률은 학습전의 강수발생확률에 비하여 한반도영역에서 평균적으로 Kuipers Skill Score가 0.2231에서 0.4293로 92.39% 상승하였다.

• Transactions of Materials Processing
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• v.13 no.1
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• pp.53-58
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• 2004

The microstructural changes of Al-Zn-Mg-Cu alloy containing Sc during hot extrusion and post heat treatment were investigated. Two kinds of Al-Sc alloys with different alloying elements (B1, B2) were hot extruded to make T-shape bars at extrusion temperature of $380^{\circ}C$, then the bars were solution treated at $480^{\circ}C$ for 2hrs followed by artificial aging at $120^{\circ}C$ for 24hrs. The interior microstructure of as extruded bar consisted of elongated grains, however, fine equiaxed grains were also observed around surface. The microstructural gradient suggested that different restoration process could proceed during the hot extrusion. For B1 and B2, different grain growth behaviors were found around the surface during the post heat treatment. Rapid grain growth behavior was observed for B1 around the surface, however, it was not observed for B2. Orientation pinning, which was related with the evolution of preferred orientation, and precipitation were thought to be responsible for the rapid grain growth.

• Journal of Microbiology
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• v.43 no.6
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• pp.503-509
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• 2005

The production of manganese peroxidase (MnP) by Irpex lacteus, purified to electrophoretic homogeneity by acetone precipitation, HiPrep Q and HiPrep Sephacryl S-200 chromatography, was shown to correlate with the decolorization of textile industry wastewater. The MnP was purified 11.0-fold, with an overall yield of $24.3\%$. The molecular mass of the native enzyme, as determined by gel filtration chromatography, was about 53 kDa. The enzyme was shown to have a molecular mass of 53.2 and 38.3 kDa on SDS-PAGE and MALDI-TOF mass spectrometry, respectively, and an isoelectric point of about 3.7. The enzyme was optimally active at pH 6.0 and between 30 and $40^{\circ}C$. The enzyme efficiently catalyzed the decolorization of various artificial dyes and oxidized Mn (II) to Mn (III) in the presence of $H_2O_2$. The absorption spectrum of the enzyme exhibited maxima at 407, 500, and 640 nm. The amino acid sequence of the three tryptic peptides was analyzed by ESI Q- TOF MS/MS spectrometry, and showed low similarity to those of the extracellular peroxidases of other white-rot basidiomycetes.

• Journal of the Korean Ceramic Society
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• v.55 no.5
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• pp.480-491
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• 2018

Calcium phosphates (CaP) were prepared by a wet chemical method. Micro-crystalline dicalcium phosphate (DCPD) was precipitated at $37^{\circ}C$ and pH 5.0 using $Ca(OH)_2$ and $H_3PO_4$. The precipitated DCPD solution was kept at $37^{\circ}C$ for 96 h. Artificial bone cement was composed of DCPD, $Ca(H_2PO_4)_2{\cdot}H_2O$ (MCPM), and $CaSO_4{\cdot}1/2H_2O$, $H_2O$ and aqueous poly-phosphoric acid solution. The wet prepared CaP powder was used as a matrix for the bone cement recipe. With the addition of aqueous poly-phosphoric acid, the cement hardening reaction was started and the CaP bone cement blocks were fabricated for the mechanical strength measurement. For the tested blocks, the mechanical strength was measured using a universal testing machine, and the microstructure phase analysis was done by field emission scanning electron microscopy and X-ray diffraction. The cement hardening reaction occurred through the decomposition and recrystallization of MCPM and $CaSO_4{\cdot}1/2H_2O$ added on the surface of the wet prepared CaP, and this resulted in grain growth in the bone cement block.

• Environmental Engineering Research
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• v.10 no.6
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• pp.294-305
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• 2005

The effectiveness of fly ash-, quicklime-, and quicklime-fly ash-based stabilization/solidification(S/S) in chromium(Cr) contaminated soils was investigated using modified semi-dynamic leaching tests. Artificial soil samples composed of kaolinite or montmorillonite contaminated with chromium nitrate(4000 mg $Cr^{3+}\;kg^{-1}$ of solid) were prepared and then subjected to S/S treatment using quicklime, fly ash, or quick lime-fly ash. The effectiveness of the treatment was evaluated by assessing the cumulative fraction of leached $Cr^{3+}$ as well as, by computing the effective diffusivity ($D_e$) and the leachability index (LX) of the treated samples. The reduction in $Cr^{3+}$ release for the untreated samples was more pronounced in the presence of montmorillonite, which was attributed to sorption. Treatment with quicklime, fly ash, or quick lime-fly ash was significantly effective in reducing $Cr^{3+}$ release most probably due to the formation of pozzolanic reaction products and $Cr(OH)_3$ precipitation. The most effective treatment was observed in montmorillonite-sand soil samples treated with quicklime-fly ash (99.8% removal). The mean $D_e$ decreased significantly and the mean LX was greater than 9 for all treated samples, indicating that the treated soils were acceptable for "controlled utilization". The mechanism controlling $Cr^{3+}$ leaching from all treated samples during the first 5 days appeared to be diffusion.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.