• Computers and Concrete
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• 제16권5호
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.715-731
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• 2021

Uncertainties are calculated for pressurized water reactor (PWR) spent nuclear fuel (SNF) characteristics. The deterministic code STREAM is currently being used as an SNF analysis tool to obtain isotopic inventory, radioactivity, decay heat, neutron and gamma source strengths. The SNF analysis capability of STREAM was recently validated. However, the uncertainty analysis is yet to be conducted. To estimate the uncertainty due to nuclear data, STREAM is used to perturb nuclear cross section (XS) and resonance integral (RI) libraries produced by NJOY99. The perturbation of XS and RI involves the stochastic sampling of ENDF/B-VII.1 covariance data. To estimate the uncertainty due to modeling parameters (fuel design and irradiation history), surrogate models are built based on polynomial chaos expansion (PCE) and variance-based sensitivity indices (i.e., Sobol' indices) are employed to perform global sensitivity analysis (GSA). The calculation results indicate that uncertainty of SNF due to modeling parameters are also very important and as a result can contribute significantly to the difference of uncertainties due to nuclear data and modeling parameters. In addition, the surrogate model offers a computationally efficient approach with significantly reduced computation time, to accurately evaluate uncertainties of SNF integral characteristics.

• 응용통계연구
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• 제33권3호
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• pp.281-296
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• 2020

같은 개체로부터 반복 측정한 자료를 경시적 자료(longitudinal data)라고 한다. 이러한 자료를 분석하려면 흔히 사용되는 횡단 자료 분석과는 다른 분석 방법이 필요하다. 즉, 경시적 자료에서 공변량의 효과를 추정할 때에는 반복 측정된 결과 간의 상관성을 고려해야 하며, 따라서 공분산행렬을 모형화 하는 것이 매우 중요하다. 그러나 추정해야 할 모수가 많고, 추정된 공분산행렬이 양정치성을 만족해야 하므로 공분산 행렬의 모형화는 쉽지 않다. 특히 다변량 경시적 자료분석을 위한 공분산행렬의 모형화는 더욱더 심층적인 방법론을 사용해야 한다. 본 논문은 다변량 경시적 자료분석을 위한 공분산행렬을 모형화하기 위해 두 가지 방법론을 고찰한다. 두 방법 모두 수정된 콜레스키 분해(modified Cholesky decomposition)를 이용하여 시간에 따른 응답변수들의 상관관계를 설명하고 있다. 하지만 같은 시간에서 관측된 응답변수들간의 상관관계를 설명하는 방법이 다르다. 첫 번째 방법론에서는 향상된 선형 공분산 모형(enhanced linear covariance models)을 사용하여 공분산행렬이 양정치성을 만족하도록 한다. 두 번째 방법론에서는 분산-공분산 분해(variance-correlation decomposition)와 초구분해(hypersphere decomposition)을 이용하여 공분산 행렬을 모형화 한다. 이 두 방법론의 성능을 비교하고자 모의실험을 진행한다.

• 대한예방한의학회지
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• 제23권2호
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• pp.43-51
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• 2019

Objectives : This study aimed to analyze price variance by year, region and company of raw herbal medicines to draw payment system for herbal medicine insurances in the National Health Insurance. Methods : To analyse price variance, we used 2015-2017 data of 'Quality test results of imported herbal medicines' provided by Korea Pharmaceutical Traders Association and 'Price data of 56 raw herbal medicines' that was surveyed by the Association of Korean Medicine. We analysed gap of highest price and lowest price those were compared with average price and coefficient of variation(CV) of prices by year, region and company of raw herbal medicines. Results : In analysing 3 years data, the highest price was 23.2% higher, and the lowest price was 19.1% lower than the average price. As of 2018, the average price of domestic produced herbal medicines was 1,8 times higher than that of imported herbal medicines. By companies, the highest price was 117.5% higher, and the lowest price was 57.3% lower than the average price. Conclusions : The price of herbal medicines varied by production year, region and company. This results suggest that comprehensive payment model needs to be considered in modeling the health insurance coverage for herbal medicine decoctions.

• Korean Journal of Hydrosciences
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• 제3권
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• pp.105-113
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• 1992

Various modeling approaches to study a long term behavior of streamflow or groundwater storage have been conducted. In this study, a Multivariate AR (1) Model has been applied to generate monthly flows of the one key station which has historical flows using monthly flows of the three subordinate stations. The Model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness. Also, the correlation coefficients (lag-zero, and lag-one) between the two monthly flows were compared. The results showed that the modeled generated flows were statistically similar to the historical flows.

• Journal of Mechanical Science and Technology
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• 제18권8호
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• pp.1470-1478
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• 2004

In this paper, a new 'presumed' Probability Density Function (PDF) approach coupled with a Lagrangian tracking method is proposed for turbulent combustion modeling. The test and the investigation of the model are conducted by comparing the model results with DNS data for a premixed flame subjected in a decaying turbulent field. The newly constructed PDF, which incorporates the instantaneous chemical reaction term, demonstrates consistent improvement over conventional assumed PDF models. It has been found that the time evolution of the mean scalar, the variance and the mean reaction rate are strongly influenced by a parameter deduced by a Lagrangian equation which takes into account explicitly the local reaction rate. Tests have been performed for a moderate Damkohler number, and it is expected the model may cover a broader range of Damkohler number. The comparison with the DNS data demonstrates that the proposed model may be promising and affordable for implementation in a moment-equation solver.

• 한국지구과학회지
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• 제31권5호
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• pp.437-447
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• 2010

Spatial estimation of environmental variables has been regarded as an important preliminary procedure for decision-making. A minimum variance criterion, which has often been adopted in traditional kriging algorithms, does not always guarantee the optimal estimates for subsequent decision-making processes. In this paper, a geostatistical framework is illustrated that consists of uncertainty modeling via stochastic simulation and risk modeling based on loss functions for the selection of optimal estimates. Loss functions that quantify the impact of choosing any estimate different from the unknown true value are linked to geostatistical simulation. A hybrid loss function is especially presented to account for the different impact of over- and underestimation of different land-use types. The loss function-specific estimates that minimize the expected loss are chosen as optimal estimates. The applicability of the geostatistical framework is demonstrated and discussed through a case study of copper mapping.

• 대한산업공학회지
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• 제25권1호
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Wind and Structures
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• 제22권4호
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• pp.393-408
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• 2016

Weibull distribution is a suitable distribution to use in modeling the life time data. It has been found to be a exact fit for the empirical distribution of the wind speed measurement samples. In brief this paper consist of important properties and characters of Weibull distribution. Also we discuss the application of Weibull distribution to wind speed measurements and derive an expression for the probability distribution of the power produced by a wind turbine at a fixed location, so that the modeling problem reduces to collecting data to estimate the three parameters of the Weibull distribution using Maximum likelihood Method.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2006년도 6th International Meeting on Information Display
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• pp.1442-1446
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• 2006

In this paper, the response surface modeling of the pretilt angle control in the nematic liquid crystal (NLC) on the homogeneous polyimide surface with different surface treatment is investigated The rubbing strength and the hard baking temperature are considered as input factors. After the design of experiments is performed, the process model is then explored using the response surface methodology. The analysis of variance is used to analyze the statistical significance and the effect plots are also investigated to examine the relationship between the process parameters and the response.