• Resources Recycling
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• v.26 no.4
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• pp.42-49
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• 2017

As a fundamental study for the separation of vanadium and tungsten from the leaching solution obtained from the soda roasting and water leaching process of spent SCR (Selective Catalytic Reduction) catalyst was carried out. The precipitation behaviors of vanadium and tungsten using the artificial solution (V: $1g{\cdot}L^{-1}$, W: $10g{\cdot}L^{-1}$) was investigated depending on temperature, NaOH concentration and the amount of $CaCl_2$ (aq.) added. V (aq.) was selectively precipitated at lower temperature than 293 K while tungsten also was precipitated at higher temperature. Precipitation rate of V and W was decreased by the increasing concentration of NaOH. On the other hand, excess Ca addition induced the increase of precipitation rate for V and W due to the formation of $Ca(OH)_2$ following the pH decline. The response surface methodology was employed to optimize the selective precipitation. Vanadium of 99.5% and tungsten of 0.0% was precipitated at $0.5mol{\cdot}L^{-1}$ of aqueous NaOH and 1 equivalent ratio of $CaCl_2$ at 293 K.

• Economic and Environmental Geology
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• v.52 no.6
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• pp.529-539
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• 2019

In this study, heavy metal removal and precipitation characteristics with pH change were studied for artificial acid mine drainage. Artificial acid mine drainage was prepared using sulfates of iron, aluminum, copper, zinc, manganese which contained in acid mine drainage from abandoned mines. The single and mixed five heavy metal samples of Fe, Al, Cu, Zn, and Mn were prepared at initial concentrations of 30 and 70 mg/L. Fe and Al were mostly removed at pH 4.0 and 5.0, respectively, and other heavy metals gradually decreased with increasing pH. Concentration changes with increasing pH show generally similar trend for single and mixed heavy metal samples. The effect of removing heavy metals from aqueous solutions is not related to the initial concentration and depends on the pH change. XRD were used for mineral identification of precipitates and crystallinity of the mineral tended to increase with increasing pH. The precipitates that produced by decreasing the concentration of heavy metals in the aqueous solution composed of Fe-goethite(FeOOH), Al-basaluminite(Al₄(SO₄)(OH)₁₀·4H₂O), Cu-connellite(Cu₁₉(OH)₃₂(SO₄)Cl₄·3H₂O) and tenorite(CuO), Zn-zincite(ZnO), and Mn-hausmannite(Mn₃O₄).

• Resources Recycling
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• v.13 no.3
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• pp.3-11
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• 2004

Basic studies have been conducted regarding the crystal formation of hydroxyapatite which was produced in the treatment process of phosphate-containing wastewater using calcium ions as the precipitating agent for its employment as the material for artificial bones. The precipitation of hydroxyapatite were conducted in the synthetic solution which simulating human body fluid for its increased applicability. Ca($NO_3$)$_2$$.$$4H_2$O and ($NH_4$)$_2$$HPO_4$ were employed for the precipitation of hydroxyapatite and its composition was analyzed after drying at 80oC. The thermal behavior of precipitate was investigated by examining the change in its crystalline structure according to the sintering temperature. DTA/TG analysis showed that the escape of moisture from the precipitate occurred at ca. $100^{\circ}C$ and the decomposition of ammonia and the evaporation of lattice water were brought about at around $250^{\circ}C$. X-ray diffraction analysis indicated that the thermally treated precipitate consisted mainly of hydroxyapatite. For dried precipitate, the bonds in the component materials which used for the precipitate formation were observed by FT-IR, and after thermal treatment the major bonds in the precipitate were shown to be $OH^{-}$, $PO_4^{3-}$ , and $CO_3^{ 2-}$ , which were main comprising bonds of hydroxyapatite.

• Korean Journal of Metals and Materials
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• v.49 no.1
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• pp.9-16
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• 2011

To develop a 6061 aluminum alloy with low residual stress and high tensile strength, a cryogenic treatment process was investigated. Compared to the conventional heat treatment process for precipitation hardening with artificial aging, the cryogenic treatment process has two additional steps. The first step is cryogenic quenching of the sample into liquid nitrogen, the second step is up-hill quenching of the sample into boiling water. The residual stress for the sample was measured by the $sin^2{\psi}$ method with X-ray diffraction. The 6061 aluminum alloy sample showed 67% relief in stress at the cryogenic treatment process with artificial aging at $175^{\circ}C$. From this study, it was found that the optimum cryogenic treatment process for a sample with low residual stress and high tensile strength is relatively low cooling speed in the cryogenic quenching step and a very high heating speed in the up-hill quenching step.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.117-117
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• 2021

본 연구에서는 Sentinel-1A/B C-band SAR(Synthetic Aperture Radar) 위성영상을 기반으로 인공신경망(Artificial Neural Network, ANN) 모형을 활용해 금강 유역 상류 40×50 km² 면적에 대한 토양수분을 산정하였다. 10 m 공간 해상도의 Sentinel-1A/B SAR 영상은 8일 간격으로 2015년부터 2019년까지 5년 동안 구축하였고, SNAP(SentiNel Application Platform)을 통해 기하 보정, 방사 보정 및 잡음(Noise) 보정을 수행하고 VV 및 VH 편파 후방산란계수로 변환하였다. ANN 모형 검증자료로 TDR(Time Domain Reflectometry)로 측정된 9개 지점의 실측 토양수분 자료를 구축하였으며, 수문학적 개념인 선행강우를 고려하기 위해 동지점에 대한 강수량 자료를 구축하였다. ANN은 각 지점에 해당하는 토양 속성별로 모델링하고, 전체 기간 및 계절별로 나누어 모의하였으며, 전체 자료의 60%와 40%를 각각 훈련 및 테스트 데이터로 사용하였다. 산정된 토양수분은 상관계수(Correlation Coefficient, R)와 평균제곱근오차(Root Mean Square Error, RMSE)를 활용하여 검증을 수행할 예정이다.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.981-987
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• 2017

Jeju Island is a volcanic island which has a large permeability. Groundwater is a major water resources and its proper management is essential. Especially, there is a multilevel restriction due to the groundwater level decline during a drought period to protect sea water intrusion. Preliminary countermeasure using long-term groundwater level prediction is necessary to use agricultural groundwater properly. For this purpose, the monthly groundwater level prediction technique by Artificial Neural Network model was developed and applied to the representative monitoring wells. The monthly prediction model showed excellent results for training and test periods. The continuous groundwater level prediction model also developed, which used the monthly forecasted values adaptively as input data. The characteristics of groundwater declines were analyzed under extreme cases without precipitation for several months.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.626-628
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• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.19-31
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• 2021

Groundwater level (GWL) causes the stress state within soil and affects the bearing capacity and the settlement of foundation. In this study, the analyses of influencing factors on GWL fluctuation were performed. From the results, river stage and moving average of precipitation were main influence components for urban near large river and rural areas, respectively. In addition, the prediction performance of GWL using artificial neural network (ANN) was conducted with respect to the influence components. As a result, the effect of main component was significant on the prediction performance of GWL.