• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.815-824
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• 2013

Traditional Pearson chi-squared test is not appropriate for the data collected by the complex sample design. When one uses the traditional Pearson chi-squared test to the complex sample categorical data, it may give wrong test results, and the error may occur not only due to the biased variance estimators but also due to the biased point estimators of cell proportions. In this study, the design based consistent Wald test statistics was derived for k-population homogeneity test, and the traditional Pearson chi-squared test statistics was partitioned into three parts according to the causes of error; the error due to the bias of variance estimator, the error due to the bias of cell proportion estimator, and the unseparated error due to the both bias of variance estimator and bias of cell proportion estimator. An analysis was conducted for empirical results of the relative size of each error component to the Pearson chi-squared test statistics. The second year data from the fourth Korean national health and nutrition examination survey (KNHANES, IV-2) was used for the analysis. The empirical results show that the relative size of error from the bias of variance estimator was relatively larger than the size of error from the bias of cell proportion estimator, but its degrees were different variable by variable.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.287-288
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• 2006

This paper introduces an approach of method to compensate accuracy error of diagonal direction. The measurement of squareness error is an important parameter in performance test of two axis Linear Motor and this exerts influence on accuracy error of diagonal test. However, previous knowledge management approaches are limited in deviation measurement of optical axis or restrictive elements of diagonal measurements using laser interferometer. But this proposed method calculated diagonal accuracy error which was occurred by squareness error and compensated squareness error using orthogonal correction method of PMAC. From this result, diagonal accuracy error is significantly reduced. This experimental results show that geometric error of squareness error is easily corrected by dynamic coordinate correction.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.34-40
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• 2002

Machining accuracy is affected by quasi-static errors of machining center. Since machine errors have a direct influence upon both the surface finish and geometric shape of the finished workpiece, it is very important to measure the machine errors and to compensate these errors. The laser measurement method for identifying geometric errors of machine tool has the disadvantages such as high cost, long calibration time and usage of volumetric error synthesis model. Accordingly, this paper deals with analysis of the geometric errors of a machine tool using ball bar test without using complicated error synthesis model. Statistical analysis method was adopted in this paper for deriving geometric errors using hemispherical helix ball bar test. As a result of experiment, geometric errors of the vertical machining center are compensated by 88%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1033-1034
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• 2010

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.205-210
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• 2008

The research interprets the principles of sampling error design for quality statistics models such as hypothesis test, interval estimation, control charts and acceptance sampling. Introducing the proper discussions of the design of significance level according to the use of hypothesis test, then it presents two methods to interpret significance by Neyman-Pearson and Fisher. Second point of the study proposes the design of confidence level for interval estimation by Bayesian confidence set, frequentist confidential set and fiducial interval. Third, the content also indicates the design of type I error and type II error considering both productivity and customer claim for control chart. Finally, the study reflects the design of producer's risk with operating charistictics curve, screening and switch rules for the purpose of purchasing and subcontraction.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.1-14
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• 2003

In man cases, the measurement error variances may be functions of the unknown true values or related covariate. In some cases, the measurement error variances increase in proportion to the value of predictor. This paper develops estimators of the parameters of a linear measurement error variance function under stratified multistage random sampling design and additional conditions. Also, this paper evaluates and compares the power of an asymptotically unbiased test with that of an asymptotically biased test. The proposed method are applied to blood sample measurements from the U.S. Third National Health and Nutrition Examination Survey(NHANES III)

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.302-308
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• 2014

In this paper, a detailed investigation of the random vibration test is presented for strapdown inertial navigation systems (INS). The effect of the random vibration test has been studied from the point of view of navigation performance. The role of distribution functions and RMS value is represented to determine a feasible method to reject or reduce vibration related error in position and velocity estimation in inertial navigation. According to a survey conducted by the authors, this is the first time that the effect of the distribution function in vibration related error has been investigated in random vibration testing of INS. Recorded data of navigation grade INS is used in offline static navigation to examine the effect of different characteristics of random vibration tests on navigation error.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.501-504
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• 2001

The use of error compensation techniques has been recognized as an effective way in the improvement of the accuracy of a machine tool. The laser measurement method for identifying position errors of machine tool has the disadvantages such as high cost, long calibration time and usage of volumetric error synthesis model. Accordingly, this paper deals with analysis of the position errors of a machine tool using ball bar test without using complicated error synthesis model. Statistical analysis method was adopted in this paper for deriving position errors using hemispherical helix ball bar test.