• 대기
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• 제29권3호
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• pp.241-255
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• 2019

Bias correction (BC) and quality control (QC) are essential steps for the proper use of satellite observations in data assimilation (DA) system. BC should be calculated over quality controlled observation. And also QC should be performed for bias corrected observation. In the Korea Institute of Atmospheric Prediction Systems (KIAPS) Package for Observation Processing (KPOP), we adopted an adaptive BC method that calculates the BC coefficients with background at the analysis time rather than using static BC coefficients. In this study, we have developed an iterative QC-BC method for Advanced Microwave Sounding Unit-A (AMSU-A) to reduce the negative feedback from the interaction between BC and QC. The new iterative QC-BC is evaluated in the KIAPS 3-dimensional variational (3DVAR) DA cycle for January 2016. The iterative QC-BC method for AMSU-A shows globally significant benefits for error reduction of the temperature. The positive impacts for the temperature were predominant at latitudes of $30^{\circ}{\sim}90^{\circ}$ of both hemispheres. Moreover, the background warm bias across the troposphere is decreased. Even though AMSU-A is mainly designed for atmospheric temperature sounding, the improvement of AMSU-A pre-processing module has a positive impact on the wind component over latitudes of $30^{\circ}S$ near upper-troposphere, respectively. Consequently, the 3-day-forecast-accuracy is improved about 1% for temperature and zonal wind in the troposphere.

• 한국항행학회논문지
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• 제28권1호
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• pp.149-154
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• 2024

미세먼지에 대한 연구는 실시간으로 발전하고 있으며, 예측 모델의 정확도를 향상시키기 위해 다양한 방법이 연구되고 있다. 또한 미세먼지의 정확한 원인과 영향을 파악하기 위해 이러한 다양한 요소들을 고려하는 연구들이 활발히 이루어지고 있다. 본 논문에서는 PM_2.5와 상관성이 있는 데이터를 계절을 기준으로 구분하여 학습하는 예측 모델과 특정 농도를 기준으로 저농도와 고농도를 구분하여 학습하는 모델을 통해 예측 성능의 비교 및 분석을 진행하였다. 기상데이터와 대기오염 물질 데이터를 사용하였으며 PM_2.5와 상관관계를 확인하여 학습 및 평가를 위한 데이터를 구성하였다. 계절별 예측 모델과 농도별 예측 모델은 LSTM으로 설계하였으며, 세부 파라미터는 하이퍼 파라미터 탐색을 통해 적용하였다. 예측 모델의 성능 평가는 정확도, RMSE, MAPE, 저농도와 고농도 구간에서의 정확도 그리고 AQI를 기준으로 4개의 범위에 대한 정확도로 진행하였다. 성능 평가 결과, 농도별 학습을 진행한 예측 모델이 AQI 기준 "나쁨" 구간의 정확도에서 91.02%의 정확도를 보였으며, 계절별 학습을 진행한 예측 모델보다 전반적으로 좋은 성능을 보였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.31-39
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• 2007

In this study we developed a turbidity management system to support the operation for effective turbid water management. The decision-making system includes various models for prediction of turbid water inflow, effective reservoir operation using the selective withdrawal facility, analysis of turbid water discharge in the downstream. The system is supported by the intensive monitoring devices installed in the upstream rivers, reservoirs, and downstream rivers. SWAT and HSPF models were constructed to predict turbid water flows in the Imha and Andong catchments. CE-QUAL-W2 models were constructed for turbid water behavior prediction, and various analyses were conducted to examine the effects of the selective withdrawal operation for efficient high turbid water discharge, turbid water distribution under differing amount and locations of turbid water discharge. A 1-dimensional dynamic water quality model was built using Ko-Riv1 for simulation of turbidity propagation in the downstream of the reservoirs, and 2-dimensional models were developed to investigate the mixing phenomena of two waters discharged from the Andong and Imha reservoirs with different temperature and turbidity conditions during joint dam operation for reducing the impacts of turbid water.

• International Journal of Advanced Culture Technology
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• 제11권2호
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• pp.249-255
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• 2023

This study aims to empirically confirm the effect and impact of community care design research centered on domestic space and environment on health promotion, diagnosis treatment, disease management, rehabilitation, and mitigation through the year of publication and perspective. To this end, based on 1,227 space and environment design studies from 2,144 community care design research data conducted for about 20 years from 2002 to 2022, when care services began in earnest through the long-term care system for the elderly, SPSS 26.0 was used to create a 'Multi-layer Perceptron' artificial neural network structure model was predicted and neural network analysis was performed. Research Results First, as a result of checking studies in each field of health care by year, there is a significant difference with the number of studies related to health promotion being the highest. Second, the five perspectives are region, time, dimension, function, and content perspective. As a result of inputting these variables as independent variables and analyzing their importance in the artificial neural network, the function perspective had the most influence, followed by the region > content > dimension > time perspective.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권5호
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• pp.437-444
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• 2015

In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter ${\gamma}$ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher ${\gamma}$ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low ${\gamma}$ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by $10{\times}$.

• 한국농공학회논문집
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• 제61권1호
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• 한국대기환경학회지
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• 제13권5호
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• pp.383-396
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• 1997

Numerical prediction of the pollutant dispersion over a two-dimensional hilly terrain is presented. The dispersion model used in the present work is based on the gradient diffusion theory and the finite-volume method on a non-orthogonal boundary-fitted grid system. The numerical model is validated by comparing the results with the available experimental data for the flat-floor dispersion within a turbulent boundary-layer. The numerical error analysis is performed based on the guideline of Kasibhatla et al.(1988) for the elevated-source dispersion in the flat-floor boundary layer having a power-law velocity and linear eddy-diffusivity profile. The influences of the two-dimensional hilly terrain on the dispersion from a continuously released source are numerically investigated by changing the emission locations and heights. It is found that the distributions of ground-level concentration are strongly influenced by the source location and the emission height. Hence, the terrain amplification factor is greatly enhanced when the pollutant source is located within a flow separation region. Dispersion from a source of short duration is also simulated and the duration time of the pollutant is compared at several downstream locations on a hilly terrain. The results of the numerical prediction are applied to the evaluation of environmental impacts due to the automobile exhausts at the seashore highway with a parallel mountain range.

• 화약ㆍ발파
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• 제33권2호
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• pp.25-39
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• 2015

수중 암반 제거 방법은 화약을 사용한 수중발파와 크레인에 장착된 쇄암봉 낙하 충격을 이용하는 방법 등이 널리 이용된다. 이와 같은 암반 제거 방법은 환경적인 요인에서 지반 진동과 수중 소음을 유발하게 된다. 본 연구 대상 현장은 하역 부두의 접안능력을 향상시키기 위해 기 설치된 잔교식 돌핀 구조물에 근접한 지역의 수중 기반암을 쇄암봉 낙하에 의해 제거하도록 설계되어 있다. 시험시공을 통하여 쇄암봉 낙하 충격으로 유발되는 진동에 대한 계측, 평가를 거쳐 진동 추정식을 획득하였고, 이를 본 공사에 반영하여 구조물에 대한 안전성을 확보하였다.

• 대한산업공학회지
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• 제38권1호
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• pp.46-56
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• 2012

The purpose of virtual metrology (VM) in semiconductor manufacturing is to predict every wafer's metrological values based on its process equipment data without an actual metrology. In this paper, we propose novelty detection-based reliability estimation models for VM in order to support flexible utilization of VM results. Because the proposed model can not only estimate the reliability of VM, but also identify suspicious process variables lowering the reliability, quality control actions can be taken selectively based on the reliance level and its causes. Based on the preliminary experimental results with actual semiconductor manufacturing process data, our models can successfully give a high reliance level to the wafers with small prediction errors and a low reliance level to the wafers with large prediction errors. In addition, our proposed model can give more detailed information by identifying the critical process variables and their relative impacts on the low reliability.