• Journal of the Korea Safety Management & Science
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• v.9 no.2
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• pp.9-17
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• 2007

There is no universally agreed classification of human error, nor is there one in prospect. Thus, a taxonomy is usually made for a specific purpose. To seek the types of human errors in the environment of man-machine interface under the railway industry, we develop a cognitive information processing model incorporating the human's mental states. Using the model, this study investigates the types of human errors about the railway workers. Thus, a survey is conducted for railway safety personnel-locomotive engineers, station employees, and train commanders- in Korean railway company. Through the survey that is designed to investigate four types of human errors from the Questionnaires composed of thirty Questions, we analyze the types of human errors related to railway safety according to affiliated offices, operation shifts, age, and working years. Finally, from the insights of the results some guidelines for the railway safety management are presented.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.11
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• pp.67-75
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• 1998

Geometric and thermal errors are key contributors to the errors of a computer numerically controlled turning center. A planar error synthesis model is obtained by synthesizing 11 geometric and thermal error components of a turning center with homogeneous coordinate transformation method. This paper shows the sensitivity analysis on the temperature change, the confidence evaluation on the uncertainty Of measurement systems, and the error contribution analysis from the planar error synthesis model. Planar error in the z direction was very sensitive to the temperature change. and planar errors in the x and z directions were not affected by the uncertainty of measurement systems. The error contribution analysis ,which is applicable to designing a new turning center, was helpful to find the large error components which affect planar errors of the turning center.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.187-200
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• 1995

This paper is concerned with a regression model with nonlinear time series errors. Testing procedures for linearity of error terms are studied. To this end, large-sample properties of estimators of regression parameters and autoregression parameter are obtained. These results are then used to develop test statistics for testing linearity of errors. Some simulation studies are shown.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.691-694
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• 1999

This paper presents a performance improvement technique of 2-D towed array shape estimation using Kalman filters. The proposed algorithm by linear model approximation corrects the position errors caused by the Kalman filter results. However, since the assumed linear model makes errors at bending parts, the spline interpolation algorithm based on curve is proposed to reduce the errors.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.793-798
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• 2012

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.451-459
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• 2011

In this paper, a software release model is presented to determine the optimum testing time with consideration of software error type. The software errors are classified into two types, major and minor errors. The software testing is continued until the Nth major error is discovered and corrected. The total cost needed before and after testing time is modeled under nonhomogeneous Poisson error correction model. Numerical examples are presented to demonstrate the results.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.885-893
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• 2013

This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1111-1114
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• 2000

In this paper, a method of estimating second-order Volterra model with ARCH errors is presented. Then we use an ECLMS algorithm for noise canceling of nonlinear time series. The validity of the proposed method is demonstrated for estimating second-order Volterra model with ARCH errors, using computer simulations.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.143-151
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• 2001

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. A real-time error compensation system has been developed based on volumetric error synthesis model which is composed of machine tool errors. This paper deals with new algorithm about verification of machine tool errors. This new algorithm uses a simplified volumetric error synthesis model. This simplified model is constructed with only main components among the error components of the machine. The main error components are analyzed by ball bar test of hemispherical helix. The novel measurement method using ball bar system has many advantages which are more efficient, easier to use than conventional measurement system.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.157-161
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• 2023

Reliability is one of the computable quality features of the software. To assess the reliability the software reliability growth models(SRGMS) are used at different test times based on statistical learning models. In all situations, Tradational time-based SRGMS may not be enough, and such models cannot recognize errors in small and medium sized applications.Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into consideration in real time in order to decide the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to eradicate the problem of software reliability modeling. In the suggested model, a new distribution model is suggested to improve the reliability estimation method. We compute the model developed and stabilize its efficiency with other popular software reliability growth models from the research publication. Our assessment results show that the proposed Model is worthier to S-shaped Yamada, Generalized Poisson, NHPP.