• Geomechanics and Engineering
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• 제6권1호
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• pp.1-15
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• 2014

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety ($F_s$) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of $F_s$ in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) $F_s$ for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical $F_s$ of the homogeneous finite slopes subjected to earthquake forces.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.224-228
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• 1994

This paper deals with a Batch processor application to determine orbit trajectories from satellite tracking data. The purpose of this paper is to find the initial state vectors. In order to determine the better estimation process, several different cases are compared. Here we adapt a minimum variance concept to develop estimation and prediction techniques. These results are compared with by SEP, Spherical Error Probable, values.

• 대한전기학회논문지
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• 제35권11호
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• pp.503-510
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• 1986

A new algorithm based on the concept of prediction error minimization is suggested to estimate the time varying delay in discrete processes. In spite of the existence of the stochastic noise, this algorithm can estimate time varying delay accurately. Computation time of this algorithm is far less than that of the previous extended parameter methods. With the use of this algorithm, generalized minimum variance control shows good control behavior in simulations.

• Computers and Concrete
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• 제25권6호
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• 한국방재안전학회논문집
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• 제15권2호
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• pp.45-56
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• 2022

가뭄 및 갈수시에 용수부족 현상이 발생하나 회귀수량을 고려한 대응이나 대책 마련이 진행되지 않고 있다. 이에 본 연구에서 자료기반의 기계학습 모형(LSTM)을 통해 회귀수량 중 하수종말처리장의 방류량을 예측하였다. 입력자료로 방류량, 유입량, 강수량, 수위를 사용하였고 예측 결과의 정확도를 개선하기 위하여 추가적으로 입력변수의 변동성 분포를 고려하였다. 방류량 자료의 변동성을 확인하기 위해서 관측값과 분포 사이의 잔차를 복합삼각함수 형태로 가정하여 이론적인 확률분포와 함께 방류량 최적의 분포 형태로 나타내었다. 변동성 분포를 고려한 입력자료를 이용한 결과와 그렇지 않는 결과를 비교한 결과, 오차정도가 감소함을 보였으며 이는 변동성 분포가 계절성을 상대적으로 잘 재현하였기 때문이라 판단된다. 따라서 본 연구에서 구축한 하수종말장처리장의 방류량 예측 모형을 활용할 경우 보다 정확한 회귀수량 예측이 가능하여 효율적인 하천수 관리 체계를 수립하는데 기초자료로 활용될 수 있을 것으로 기대된다.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.57-76
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• 2007

Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

• 한국산림과학회지
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• 제102권4호
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• pp.467-476
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• 2013

산림입지도의 지위지수 정보는 조사지점에만 존재하므로 미조사 지역에 대한 지위지수는 별도의 추정이 필요하다. 미조사 지역의 지위지수 추정을 위해 본 연구에서는 점자료로부터 연속표면을 생성하는 공간 내삽법인 크리깅 기법을 적용하였다. Chapman-Richards 생장모델을 이용하여 표준지별 지위지수 추정치를 구한 뒤 가우시안, 구형 및 지수형 베리오그램 모델별로 정규크리깅을 이용하여 단양 전역의 소나무림 지위지수를 격자단위($30m{\times}30m$)로 추정하였다. 교차검증을 위해 평균오차(ME), 평균표준오차(ASE) 및 평균제곱근오차(RMSE)를 계산하였다. 베리오그램 모델 적합 결과, 상대 너깃이 가장 큰 가우시안 모델(37.40%)이 제외되었으며 구형 모델(16.80%)과 지수형 베리오그램 모델(8.77%)이 선택되었다. 크리깅에 의한 지위지수 추정치는 지수형 모델을 적용한 경우 4.39~19.53, 구형모델을 적용한 경우 4.54~19.23의 분포를 보였다. 교차 검증 결과, RMSE는 두 모델에서 큰 차이가 없는 것으로 나타났으나 구형모델의 ME와 ASE가 지수형 모델보다 작기 때문에 구형 베리오그램 모델 기반 지위지수 지도를 최종적으로 선정하였다. 지위지수 지도로부터 산출된 단양지역 소나무림 평균 지위지수는 10.78로 추정되었다. 공간이질성이 큰 우리나라 산림의 바이오매스 추정 시 지위지수 지도를 통해 지역적 변이를 고려할 수 있으며 궁극적으로는 탄소저장량 분포 추정의 정확도 제고에 기여할 수 있을 것으로 기대된다.

• Journal of Crop Science and Biotechnology
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• 제10권3호
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• pp.185-192
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• 2007

Near-infrared spectroscopy(NIRS) was used to develop a rapid and efficient method to determine lignan glucosides in intact seeds of sesame(Sesamum indicum L.) germplasm accessions in Korea. A total of 93 samples(about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for lignan glucosides contents were measured by high performance liquid chromatography. Calibration equations for sesaminol triglucoside, sesaminol($1{\rightarrow}2$) diglucoside, sesamolinol diglucoside, sesaminol($1{\rightarrow}6$) diglucoside, and total amount of lignan glucosides were developed using modified partial least square regression with internal cross validation(n=63), which exhibited lower SECV(standard errors of cross-validation), higher $R^2$(coefficient of determination in calibration), and higher 1-VR(ratio of unexplained variance divided by variance) values. Prediction of an external validation set(n=30) showed a significant correlation between reference values and NIRS estimated values based on the SEP(standard error of prediction), $r^2$(coefficient of determination in prediction), and the ratio of standard deviation(SD) of reference data to SEP, as factors used to evaluate the accuracy of equations. The models for each glucoside content had relatively higher values of SD/SEP(C) and $r^2$(more than 2.0 and 0.80, respectively), thereby characterizing those equations as having good quantitative information, while those of sesaminol($1{\rightarrow}2$) diglucoside showing a minor quantity had the lowest SD/SEP(C) and $r^2$ values(1.7 and 0.74, respectively), indicating a poor correlation between reference values and NIRS estimated values. The results indicated that NIRS could be used to rapidly determine lignan glucosides content in sesame seeds in the breeding programs for high quality sesame varieties.

• 대기
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• 제30권2호
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• pp.103-113
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• 2020

In this study, we analyzed and developed the monitoring system in order to confirm the effect of observations on forecast sensitivity on ensemble-based data assimilation. For this purpose, we developed the Ensemble Forecast Sensitivity to observation (EFSO) monitoring system based on Local Ensemble Transform Kalman Filter (LETKF) system coupled with Korean Integrated Model (KIM). We calculated 24 h error variance of each of observations and then classified as beneficial or detrimental effects. In details, the relative rankings were according to their magnitude and analyzed the forecast sensitivity by region for north, south hemisphere and tropics. We performed cycle experiment in order to confirm the EFSO result whether reliable or not. According to the evaluation of the EFSO monitoring, GPSRO was classified as detrimental observation during the specified period and reanalyzed by data-denial experiment. Data-denial experiment means that we detect detrimental observation using the EFSO and then repeat the analysis and forecast without using the detrimental observations. The accuracy of forecast in the denial of detrimental GPSRO observation is better than that in the default experiment using all of the GPSRO observation. It means that forecast skill score can be improved by not assimilating observation classified as detrimental one by the EFSO monitoring system.