• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.383-392
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• 2007

In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.27-35
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• 2009

In this paper, we study the classification of steam generator tube defects using an improved feature extraction. We consider 4 axisymmetric defect patterns of tube: I-In type, I-Out type, V-In type, and V-Out type. Through numerical analysis program based on finite element modeling, 400 ECT signals are generated by varying width and depth of each defect type. From those generated ECT signals, we propose new feature vectors that include an angle between the two points where the Maximum impedance and half the Maximum impedance, and angles between Maximum impedance point and 10%, 20%, 30%, 40% of Maximum impedance points. Also, multi-layer perceptron with one hidden layer is used to classify the defect patterns. Through the computer simulation study, it is shown that the proposed method achieves an improved defect classification performance in terms of Maximum Error and mean square Error.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.8-20
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• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.575-587
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• 2012

The effect of a new treatment is proven through the comparison of a new treatment with placebo; however, the number of parent non-inferiority trials tends to grow proportionally to the number of active controls. In a non-inferiority trial a new treatment is approved by proof that the new treatment is not inferior to an active control; however, both additional assumptions and historical trials are needed to show (through the comparison of the new treatment with the active control in a non-inferiority trial) that the new treatment is more efficacious than a putative placebo. The two different methods of using the historical data: frequentist principle method and meta-analytic method. This paper discusses the statistical methods and different Type I error rates obtained through the different methods employed.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1399-1412
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• 2016

We compare many normality tests consisting of different sources of information extracted from the given data: Anderson-Darling test, Kolmogorov-Smirnov test, Cramervon Mises test, Shapiro-Wilk test, Shaprio-Francia test, Lilliefors, Jarque-Bera test, D'Agostino' D, Doornik-Hansen test, Energy test and Martinzez-Iglewicz test. For the purpose of comparison, those tests are applied to the various types of data generated from skewed distribution, unsymmetric distribution, and distribution with different length of support. We then summarize comparison results in terms of two things: type I error control and power. The selection of the best test depends on the shape of the distribution of the data, implying that there is no test which is the most powerful for all distributions.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1468-1472
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• 1996

In this study, the performance of the CART(Classification and Regression Tree) is compared with that of discriminant analysis method. In most experiments using bank data, discriminant analysis shows better performance in terms of the total cost. In contrast, most experiments using insurance data show that the CART is better than discriminant analysis in terms of the total cost. The contradictory result are analysed by using the characteristics of the data sets. The performances of both the Classification and Regression Tree and discriminant analysis depend on the parameters:failure prior probability, data used, type I error, type II error cost, and validation method.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1085-1096
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• 2009

Population stratification can cause spurious associations between genetic markers and disease locus. In order to handle this population stratification in haplotype-based case-control association studies, we added population indicators as covariates to the haplotype trend regression model proposed by Zaykin et al. (2002). We investigated through simulations how both population stratification and measurement error in the estimation of true population of each individual affect type I error probabilities of the association tests based on both Zaykin et al.'s (2002) model and the proposed model. Based on those results, in the situation that there exists population stratification but there is no error in population classification of each individual, our proposed model does satisfy a type I error probability whereas Zaykin et al.'s (2002) model does not. However, as the measurement error increases, a type I error probability of our model correspondingly becomes larger than a nominal significance level. It implies that as long as uncertainty in the estimation of true population of each individual still remains, it is nearly impossible to avoid false positive in case-control association studies based on haplotypes.

• Korean Journal of Computational Design and Engineering
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• v.14 no.1
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• pp.18-24
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• 2009

This paper proposes an analytical method of evaluating the maximum error by modeling the exact tool path for the tool traverse singular region in five-axis machining. It is known that the NC data from the inverse kinematics transformation of 5-axis machining can generate singular positions where incoherent movements of the rotary axes can appear. These lead to unexpected errors and abrupt operations, resulting in scoring on the machined surface. To resolve this problem, previous methods have calculated several tool positions during a singular operation, using inverse kinematics equations to predict tool trajectory and approximate the maximum error. This type of numerical approach, configuring the tool trajectory, requires much computation time to obtain a sufficient number of tool positions in a region. We have derived an analytical equation for the tool trajectory in a singular area by modeling the tool operation into a linear and a nonlinear part that is a general form of the tool trajectory in the singular area and that is suitable for all types of five-axis machine tools. In addition, we have evaluated the maximum tool-path error exactly, using our analytical model. Our algorithm can be used to modify NC data, making the operation smoother and bringing any errors to within tolerance.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.303-312
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• 1995

We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.