• Journal of Sericultural and Entomological Science
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• v.41 no.2
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• pp.108-115
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• 1999

An improvement of methods in fibroin hydrolysates preparat significantly enlarges their applications to practical use. Acidic hydrolysis by hydrohloric acid is one of the methods for silk fibroin depolymeriza6tion. A low yield of final product and long time of the process are the demerits of this method. Possibility of preparation of water-soluble silk hydrolysates with more yield and less expenses is investigated in this study. Such possibility is occurred with the increasing tratment temperature and simultaneously decreasing treatment time, concentration of hydrochoric acid respectively, the concentration of sodium hydroxide used for neutraliza6tion of hydolysates after hydrolysis. Colour is decreased in this case and a small amount of activated, too. Protection of hydrolysates against precipitation after neutraliza6tion, and separation and during concentrating process is the other merit of this method. Creamy-coloured insoluble silk powder is the remainder of hydrolyzed fibroin. This is the only the immobiliza6tion of enzymes and other physiological active substances. Fine particles of this powder can be used as additives for artificial diets and cultural media, as well as raw materials for polymer membranes, etc.

• Microbiology and Biotechnology Letters
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• v.38 no.2
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• pp.216-221
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• 2010

This study shows an application of microbiologically induced carbonate precipitate for strength improvement and crack remediation of cement-sand mortar. Seven calcium carbonate-forming bacteria (CFB) were isolated from Dok-do and partially identified by DNA sequence analysis of the 16s rRNA gene. Crystal aggregates were apparent around the bacterial colonies grown on an agar medium. These strains showed strain specific $CaCO_3$ precipitation on urea-$CaCl_2$ medium. Among 7 isolates, Arthrobacter nicotinovorans KNUC601, Microbacterium resistens KNUC602, Agrobacterium tumefaciens KNUC603, Exiguobacterium acetylicum KNUC604, and Bacillus thuringiensis KNUC606 showed a repairing of artificial forced cracks in cement-sand mortar. Compressive strength of cement-sand mortar consolidated with Stenotrophomonas maltophilia KNUC605 was increased around 14.07% compared with that of negative control.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.771-775
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• 2010

This study was carried out to investigate soil and environment characteristics of Schizandra Chinensis Baillon habitat as a part of establishment of conservation plan for important medicinal crop resources in Jangsu-gun, Jeollabuk-do. These habitats are mostly located on the slopes of mountains facing northwest at an altitude of 500 m to 700 m with angles of inclination ranging from 17.6 to 36.4%. Average yearly precipitation and temperature of Schizandra Chinensis Baillon habitat were 1,493.4 mm and $16.9^{\circ}C$, respectively. The number of plant species growing in Schizandra Chinensis Baillon habitat was found 55 species. Salix gracilistyla, Lespedeza bicolor, and Artemisia princeps var. were shown higher density and coverage. Chemical quality of Schizandra Chinensis Baillon habitats soils was relatively higher than artificial cultivation soil.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.495-504
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• 2022

With the recent development of artificial intelligence, a Long Short-Term Memory (LSTM) model that is efficient with time-series analysis is being used to increase the accuracy of predicting the inflow of dams. In this study, we predict the inflow of the Soyang River dam, using the LSTM model with the Sequence-to-Sequence (LSTM-s2s) and attention mechanism (LSTM-s2s with attention) that can further improve the LSTM performance. Hourly inflow, temperature, and precipitation data from 2013 to 2020 were used to train the model, and validate and test for evaluating the performance of the models. As a result, the LSTM-s2s with attention showed better performance than the LSTM-s2s in general as well as in predicting a peak value. Both models captured the inflow pattern during the peaks but detailed hourly variability is limitedly simulated. We conclude that the proposed LSTM-s2s with attention can improve inflow forecasting despite its limits in hourly prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.320-320
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• 2023

강우 데이터는 수문기상, 환경, 농업, 자연재해, 그리고 수자원 시스템 분야에서 가장 필수적인 기본 요소 중 하나이다. 또한 강우 데이터는 수문학적 분석에서 활용되는 필수 입력 자료 중 하나로 관측 데이터의 품질에 따라 수문 모형을 이용한 모의 결과물의 정확도가 결정된다고 할 수 있다. 따라서, 강우 관측소별로 강우 데이터의 품질을 어떻게 관리하느냐에 따라 수문 모형의 활용 범위 및 수자원 관리의 효율성이 결정될 수 있다. 강우의 시공간적 변동성은 수 많은 인자들과 직간접적으로 연계되어 있기 때문에 미계측 강우 자료에 대해 직접 관측이 아닌 수치 모형을 이용하여 강우의 발생과 강우량을 산정하는 것은 매우 복잡한 과제 중 하나이다. 현재 국내에서 운용되고 있는 강우 관측소의 경우에도 미계측 된 강우 데이터가 존재함으로써 강우 데이터의 활용에 제한이 생기는 경우가 있다. 따라서, 이러한 미계측 데이터의 추정 및 보완은 보다 효과적인 수재해 방지, 수자원 관리를 위한 필수 과제 중 하나이다. 일반적으로, 미계측 강우를 산정하기 위해서 Kriging, Thiessen, 등우선법, 그리고 역거리 관측법 등 다양한 수문학적 방법들이 적용되고 있다. 이러한 방법들은 산악효과나 강우 관측소의 분포 상태 등을 고려하지 못하기 때문에 측정하는 지역에 따라 강우 추정 오차가 커질 수 있다는 한계가 있다. 최근에는 데이터 관측 시스템과 빅데이터 기술의 발전과 활용 가능한 데이터의 양이 증가함에 따라 머신러닝을 활용한 사례가 증가하고 있다. 머신러닝은 데이터 사이의 관계를 기반으로 분류, 회귀, 그리고 예측 문제에 주로 사용되는 기법 중 하나이다. 따라서, 본 연구에서는 광주광역시 지역에 위치한 주요 강우 관측 지점들을 대상으로 미계측 된 시강우 데이터를 추정 및 복원하고자 한다. 여기서 데이터 추정 기술이란 미계측 강우의 발생 유무 및 강우량을 추정할 수 있는 기술을 의미한다. 이를 위해 대표적인 머신러닝 알고리즘인 인공신경망(Artificial Neural Network) 및 랜덤포레스트(Random Forest)를 적용하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.292-292
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• 2023

본 연구에서는 인공신경망을 이용한 통계적 모형을 구성하여 금강권역의 봄철(3~5월) 강수량 예측을 수행하였다. 통계적 모형의 예측인자로서는 NOAA 등에서 제공하는 AAO, AMM, AO 등 36종의 기후지수와 대상권역인 금강권역의 강수량, 기온 등의 기상인자 8종 등 총 44종의 기후지수를 활용하였다. 예측대상기간을 기준으로 선행기간(1~18개월)에 따른 상관성을 분석하여 상관도가 높은 10개의 기후지수를 예측인자로 선정하였다. 예측모형 형태는 10개의 입력층과 1개의 은닉층으로 되어 있는 인공신경망모형을 구성하였다. 모형 구성과정에서의 불확실성을 최소화하고 예측모형의 적합도를 높이기 위해 예측대상기간을 기준으로 과거 40년간의 자료에 대해 임의로 20년간 자료를 선별하여 모형을 구성하고, 너머지 기간에 대해 검증하는 무작위 교차검증을 반복하여, 예측대상기간 및 예측시점에 따라 각각 적합도가 높은 1000개의 예측모형을 선별하였다. 과거기간(1991~2022년)을 대상으로 예측시점에 따라 각 연도별 1000개의 예측결과를 도출하여, 실제 해당년도의 관측값과의 비교를 통해 예측성을 분석하였다. 예측성은 크게 예측치의 최대값과 최소값 범위 및 예측치의 25%~75% 범위 안에 관측치가 포함될 확률, 그리고 과거 관측값의 3분위 구간을 기준으로 한 예측확률 등을 평가하였다. 관측치가 예측치의 범위 안에 포함될 확률은 평균 87.5%, 예측치의 25~75% 범위 안에 포함될 확률은 30.2%로 나타났으며, 3분위 예측확률은 35.6%로 분석되었다. 관측값과의 일대일 비교는 정확도가 떨어지지만 3분위 예측확률이 33.3% 이상인 점으로 볼 때 예측성은 확보된다고 볼 수 있다. 다만, 우리나라 강수량의 불규칙성과 통계적 모형 특성상 과거 관측되지 않은 패턴에 대해서는 예측이 어려운 문제가 있어, 특정년도의 예측결과가 관측치를 크게 벗어나는 경우도 종종 나타나고 있다.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of the Korean earth science society
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• v.28 no.4
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• pp.478-490
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• 2007

With groundwater data of seawater intrusion monitoring network in coastal areas of Korea's main land, we analyzed types of seawater intrusion through the coastal aquifer. The data including groundwater level, temperature and electrical conductivity obtained from 45 monitoring wells at 25 watershed regions were evaluated. Based on statistical analysis, correlation analysis and variation type analysis, groundwater levels were mainly affected by rainfall and artificial pumping. About 78% of the monitoring wells showed average temperature higher than $15^{\circ}C$ and about 58% of them showed minimum variations less than $0.2^{\circ}C$. Electrical conductivities showed a large magnitude of variation and irregular characteristics compared with groundwater levels and temperatures. Average electrical conductivities lower than $2,000\;{\mu}S/cm$ were observed at 28 monitoring wells while those of higher than $10,000\;{\mu}S/cm$ were done at 9 monitoring wells. From the cross-correlation analysis, groundwater levels were mostly affected by precipitation while temperature and electrical conductivity showed very low correlation. Meanwhile tidal variations strongly affected the groundwater levels comparing to precipitation. We classified the long-term monitoring data according to variation types such as constant process, linear trend, cyclic variation, impulse, step function and ramp. Impulse type was dominant for variations of groundwater level, which was largely affected by rainfall or artificial pumping, the constant process was dominant for temperature. Compared with groundwater level and temperature, electrical conductivities showed various types like linear trend, step function and ramp. According to the discrepancy of variation characteristics for monitoring data at each well in the same region, periodical analysis of monitoring data is essentially required.