• Computers and Concrete
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• 제5권3호
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• pp.261-277
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• 2008

This paper presents methodologies for remaining life prediction of plain concrete structural components considering tension softening effect. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. A methodology to account for tension softening effects in the computation of SIF and remaining life prediction of concrete structural components has been presented. The tension softening effects has been represented by using any one of the models mentioned above. Numerical studies have been conducted on three point bending concrete structural component under constant amplitude loading. Remaining life has been predicted for different loading cases and for various tension softening models. The predicted values have been compared with the corresponding experimental observations. It is observed that the predicted life using bi-linear model and power curve model is in close agreement with the experimental values. Parametric studies on remaining life prediction have also been conducted by using modified bilinear model. A suitable value for constant of modified bilinear model is suggested based on parametric studies.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.33-39
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• 2013

반응변수의 변환을 고려하는 부분선형모형에서 이상치 문제는 선형모형에서와 마찬가지로 반응변수 변환모수의 추정에 왜곡된 결과를 초래할 수 있다. 이를 해결하기 위해서는 부분선형모형에서 반응변수 변환 모수 추정과 이상치 탐지 과정이 수행되어야 하지만 모형에 포함된 비모수 함수의 비정형성에 따른 어려움이 크다. 본 연구에서는 부분선형모형의 비모수함수에 대한 추정과 순차적 검정, 최대절사우도추정 등과 같은 이상치 제거방법의 적용을 통하여 부분선형모형에서 이상치에 강건한 반응변수 변환 과정을 제안한다. 제안된 방법들은 모의실험과 예제를 통해 효과를 비교 검증한다.

• Asian-Australasian Journal of Animal Sciences
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• 제18권8호
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Imaging Science in Dentistry
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• 제32권1호
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• pp.27-33
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• 2002

Purpose: To evaluate the influence of threshold value of computed tomography on the accuracy of rapid prototyping (RP) medical model Material and Methods : CT datas of a human dry skull were transferred from CT scanner via compact disk to a personal computer (PC). 3-dimensional image reconstruction on PC by V-works/sup TM/ 3.0 (CyberMed. Inc.) software and RP models fabrication were followed. 2-RP models were produced by threshold value of 500 and 800 selected in surface rendering process. Linear measurements between arbitrary 12 anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared. Thus, the accuracy of 500 RP and 800RP models was respectively evaluated. Results: There was mean difference (% difference) in absolute value of 2.27 mm (2.73%) between linear measurements of dry skull and 500 RP model. There was mean difference (% difference) in absolute value of 1.94 mm (2.52%) between linear measurements of dry skull and 800 RP model. Conclusion: Slight difference of threshold value in rendering process of 3-D modelling made a influence on the accuracy of RP medical model.

• 응용통계연구
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• 제18권3호
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• pp.701-712
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• 2005

로지스틱 회귀모형에서 suppression의 논의는 선형회귀의 논의보다 많지 않은데 그 이유 중의 하나는 회귀제곱합 또는 결정계수의 정의가 유일하지 않고 다양하기 때문이다. 여러 종류의 결정계수들 중에서 선호되는 두 종류의 결정계수와 Liao와 McGee(2003)가 제안한 두 종류의 수정 결정계수의 정의로부터 회귀제곱합을 유도하여 로지스틱 회귀모형에서의 suppression을 설명하고자 한다. 모의실험을 통하여 자료를 생성하여 어떤 경우에 suppression이 발생하는지를 살펴보고 그 결과를 선형회귀모형에서의 suppression 결과와 비교한다.

• Computers and Concrete
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• 제21권5호
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• pp.559-567
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• 2018

In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures ($400^{\circ}C-800^{\circ}C$) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models' results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.

• 응용통계연구
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• 제28권2호
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• pp.335-342
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• 2015

경시적 자료는 각 환자마다 시간에 따라 반복 측정되는 코호트 연구 등에서 많이 쓰인다. 본 연구는 반응변수 간 상관성을 고려할 수 있는 결합 다단계 일반화 선형모형을 이용하여, 다변량 경시적 자료 분석을 수행하였다. 한국 유전체 역학 연구에서 실시한 코호트 자료를 적합하고 결과를 해석한다. 조건부 아카이케 정보 기준을 이용하여 모형 선택을 하고, 변량효과들의 추정치들을 설명한다.

• Communications for Statistical Applications and Methods
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• 제18권3호
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• pp.319-331
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• 2011

In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.

• 한국ITS학회 논문지
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• 제11권6호
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• pp.145-154
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• 2012

본 논문은 고속도로 터널구간을 대상으로 교통사고특성을 다각적으로 분석하여 다양한 독립변수를 선정하고 종속변수를 건, 건/km, 건/백만대km로 다양화하여 다중선형회귀모형을 개발하였다. 그리고 개발된 모형들은 상호 비교 검토하여 최종적으로 교통사고영향요인으로 구성된 신뢰성 있는 교통사고예측모형을 결정하였다. 교통사고예측모형은 모형의 $R^2$, F값 등 검정통계량 수준, 다중공선성, 잔차분석 등 모형검증과정이 수행되었고 터널구간의 교통사고특성 반영여부 등을 검토하여 최종적으로 터널길이에 따라 총 2개의 모형을 선정하였다. 선정된 종속변수는 ln(건/백만대km)이며, 독립 변수는 연평균일교통량(AADT), 종단구배, 터널높이로 구성되었다. 추정모형은 RMSE, MAE를 이용하여 예측한 값과 실제 관측값과의 차이를 분석하여 터널구간의 교통사고를 설명하는데 적합한 모형으로 파악되었다.

• 한국통신학회논문지
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• 제31권8C호
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• pp.795-800
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• 2006

In this paper, the minimum mean square error(MSE) convergence of the RBF equalizer is evaluated and compared with the linear equalizer based on the theoretical minimum MSE. The basic idea of comparing these two equalizers comes from the fact that the relationship between the hidden and output layers in the RBF equalizer is also linear. As extensive studies of this research, various channel models are selected, which include linearly separable channel, slightly distorted channel, and severely distorted channel models. In this work, the theoretical minimum MSE for both RBF and linear equalizers were computed, compared and the sensitivity of minimum MSE due to RBF center spreads was analyzed. It was found that RBF based equalizer always produced lower minimum MSE than linear equalizer, and that the minimum MSE value of RBF equalizer was obtained with the center spread which is relatively higher(approximately 2 to 10 times more) than variance of AWGN. This work provides an analytical framework for the practical training of RBF equalizer system.