• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.248-248
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• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.33-36
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• 2007

In recent years the convective-dispersive equation has been often discredited in predicting subsurface solute transport under field conditions due to presence of preferential flow paths. Kim et al. (2005) proposed a simple equation that can predict the breakthrough of solutes without excessive data requirements. In their Generalized Preferential Flow Model (GPFM), the soil is conceptually divided in a saturated "distribution layer" near the surface and a "conveyance zone" with preferential flow paths below. In this study, we test the model with previously published data, and compare it with a classical convective-dispersive model (CDM). With three parameters required-apparent water content of the distribution zone, and solute velocity and dispersion in the conveyance zone-GPFM was able to describe the breakthrough of solutes both through silty and sandy loam soils. Although both GPFM and CDM fitted the data well in visual, variables for GPFM were more realistic. The most sensitive parameter was the apparent water content, indicating that it is the determining factor to apply GPFM to various soil types, while Kim et al. (2005) reported that changing the velocity of GPFM reproduced solute transport when same soils were used. Overall, it seems that the GPFM has a great potential to predict solute leaching under field conditions with a wide range of generality.

• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.179-191
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• 2020

The aim of this study was to investigate the predictors of major adverse cardiac events (MACE) in patients with drug-eluting balloon (DEB) for in-stent restenosis (ISR) lesion. Total of 257 patients who developed ISR on follow-up coronary angiography (66.1 ± 10.1years, 172 males) in Chonnam National University Hospital between October 2012 and January 2017 were enrolled. We divided the patients into two groups; group I (MACE group; n= 35) and group II (No MACE group; n= 222). A multivariate logistic regression analysis revealed that type IV ISR (HR=4.179, 95% C.I.=1.851-9.437 p= 0.001), lesion length > 25 mm (HR=8.773, 95% C.I.=1.898-40.546 p=0.005), number of ISR recurrence > 2 (HR=4.693, 95% C.I.=1.259-17.490 p= 0.021) were independent factors for MACE after DEB in ISR lesions.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of the Korean GEO-environmental Society
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• v.3 no.4
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• pp.11-17
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• 2002

The purpose of this study is the prediction of pollutant loads into Seomjin river watershed using neural networks model. The pollutant loads into river watershed depend upon the water quantity of inflow from the upstream as well as the water quality of the inflow into the river. For the estimation of pollutants into river, a neural networks model which has the features of multi-layered structure and parallel multi-connections is used. The used water quality parameters are BOD, COD and SS into Seomjin river. The results of calibration are satisfactory, and proved the availability of a proposed neural networks model to estimate short-term water quality pollutants into river system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1107-1114
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• 2011

In this paper, the fatigue life of a laser peened structure was predicted. In order to calculate residual stress induced by laser peening finite element simulation was carried out. Modified Goodman equation was used to consider the effect of compressive residual stress induced by laser peening in fatigue analysis. In addition, additive adjustment factor approach was applied to consider S-N curve model uncertainty. Consequently, the reliable bounds of the predicted fatigue life of the laser peened structure was determined.

• Tunnel and Underground Space
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• v.19 no.3
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• pp.203-212
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• 2009

The prediction of near field behavior around an underground high-level radioactive waste repository is important for the repository design as well as the safety assessment. In this study, a sensitivity analysis for seven parameters consisted of design parameters and material properties was carried out using a three-dimensional finite difference code. From the sensitivity analysis, it was found that the effects of borehole spacing, tunnel spacing, cooling time and rock thermal conductivity were more significant than the other parameters. For getting a statistical distribution of buffer and rock temperatures around the repository, an artificial neural network, backpropagation, was applied. The reliability of the trained neural network was tested with the cases with randomly chosen input parameters. When the parameter variation is within ${\pm}10%$, the prediction from the network was found to be reliable with about a 1% error. It was possible to calculate the temperature distribution for many cases quickly with the trained neural network. The buffer and rock temperatures showed a normal distribution with means of $98^{\circ}C$ and $83.9^{\circ}C$ standard deviations of $3.82^{\circ}C$ and $3.67^{\circ}C$, respectively. Using the neural network, it was also possible to estimate the required change in design parameters for reducing the buffer and rock temperatures for $1^{\circ}C$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.4364-4374
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• 2015

In this study, heat transfer and friction characteristics of compound enhanced fin-and-tube heat exchangers were experimentally investigated. Louver-finned heat exchangers were also tested for comparison purpose. The effect of fin pitch on j and f factor was negligible. The j factor decreased as number of tube row increased. However, f factor was independent of number of tube row. Louver fin samples yielded higher j and f factors than compound enhanced fin samples. For one row, j and f factors of louver fin were 23% and 27% higher than those of compound enhanced fin. For two row, those were 11% and 8%, and for three row, those were 10% and 9%. However, heat transfer capacities at the same pressure drop of the compound enhanced fins were 6.4% for one row, 11.1% for two row and 13.6% for three row larger than those of louver fins, Existing louver fin correlation overpredicted the present j factors and underpredicted the present f factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.993-1000
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• 1992

Recently, many researchers have found that the speaker recognition rate is high when they perform the speaker recognition using statistical processing method of orthogonal parameter, which are derived from the analysis of speech signal and contain much of the speaker's identity. This method, however, has problems caused by vocalization speed or time varying feature of speed. Thus, to solve these problems, this paper proposes two methods of speaker recognition which combine DTW algorithm with the method using orthogonal parameters extracted from $Karthumem-Lo\'{e}ve$ Transform method which applies orthogonal parameters as feature vector to ETW algorithm and the other is the method which applies orthogonal parameters to the optimal path. In addition, we compare speaker recognition rate obtained from the proposed two method with that from the conventional method of statistical process of orthogonal parameters. Orthogonal parameters used in this paper are derived from both linear prediction coefficients and partial correlation coefficients of speech signal.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1881-1890
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• 2021

Particulate matter (PM) affects the human, ecosystems, and weather. Motorized vehicles and combustion generate fine particulate matter (PM_2.5), which can contain toxic substances and, therefore, requires systematic management. Consequently, it is important to monitor and predict PM_2.5 concentrations, especially in large cities with dense populations and infrastructures. This study aimed to predict PM_2.5 concentrations in large cities using meteorological and chemical variables as well as satellite-based aerosol optical depth. For PM_2.5 concentrations prediction, a random forest (RF) model showing excellent performance in PM concentrations prediction among machine learning models was selected. Based on the performance indicators R², RMSE, MAE, and MAPE with training accuracies of 0.97, 3.09, 2.18, and 13.31 and testing accuracies of 0.82, 6.03, 4.36, and 25.79 for R², RMSE, MAE, and MAPE, respectively. The variables used in this study showed high correlation to PM_2.5 concentrations. Therefore, we conclude that these variables can be used in a random forest model to generate reliable PM_2.5 concentrations predictions, which can then be used to assess the vulnerability of schools to PM_2.5.