• Journal of Mechanical Science and Technology
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• v.16 no.1
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• pp.60-74
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• 2002

In this study, normal, log-normal, triangular, uniform. Weibull, Erlang and unit beta probability density functions are tried to represent the behaviour of frequency distributions of workpiece dimensions collected from various manufacturing firms. Among the distribution functions, the unit beta distribution function is found to be the best fit using the chi-square test of fit. An attempt is made for the adoption of the unit beta model to x-bar charts of quality control in manufacturing. In this direction, upper and lower control limits (UCL and LCL) of x-bar control charts of dimension measurements are estimated for the beta model, and the observed differences between the beta and normal model control limits are discussed for the measurement sets.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.117-129
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• 1996

This Study intends to analysis what 3 factors, which are indices of Capital, Labor and Distribution, really affect to Value-Added Productivity through Statistical Analysis. For this, We selected 12 indices of Value-Added from the edition of 'Annual report of Korean companies' published in 'Korea Investors Service., Inc', especially in parts of Chemicals and Chemical products of total 85 companies. Using this data, Multivariate Statistical Analysis such as Principal Component Analysis, Factor Analysis, Covariance Structure Analysis is taken for modeling the effect of 3 factor(Labor Productivity, Capital Productivity and the Index of Distribution) on Value-Added Productivity.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.73-85
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• 1996

In this paper, we consider the distribution-free confidence intervals for the reliability of the stress-strength model when the stress X and strength Y depend linearly on some explanatory variables z and w, respectively. We apply these confidence intervals to the Rocket-Motor data and compare the results to those of Guttman et al. (1988). Some simulation results show that the distribution-free confidence intervals have better performance for nonnormal errors compared to those of Guttman et al. (1988), which are designed for normal random variables in respect that the former yield the coverage levels closer to the nominal coverage level than the latter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1307-1329
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• 2022

This paper proposes an improved Pseudorandom Sequence Generator (PRSG) based on the concept of modular arithmetic systems with non-integral numbers. The generated random sequence use in various cryptographic applications due to its unpredictability. Here the mathematical model is designed to solve the problem of the non-uniform distribution of the sequences. In addition, PRSG has passed the standard statistical and empirical tests, which shows that the proposed generator has good statistical characteristics. Finally, image encryption has been performed based on the sort-index method and diffusion processing to obtain the encrypted image. After a thorough evaluation of encryption performance, there has been no direct association between the original and encrypted images. The results show that the proposed PRSG has good statistical characteristics and security performance in cryptographic applications.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.67-80
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• 2017

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

• Nuclear Engineering and Technology
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• v.31 no.3
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• pp.257-266
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• 1999

A simplified statistical methodology is developed in order to both reduce over-conservatism of deterministic methodologies employed for PWR fuel rod internal pressure (RIP) calculation and simplify the complicated calculation procedure of the widely used statistical methodology which employs the response surface method and Monte Carlo simulation. The simplified statistical methodology employs the system moment method with a deterministic approach in determining the maximum variance of RIP The maximum RIP variance is determined with the square sum of each maximum value of a mean RIP value times a RIP sensitivity factor for all input variables considered. This approach makes this simplified statistical methodology much more efficient in the routine reload core design analysis since it eliminates the numerous calculations required for the power history-dependent RIP variance determination. This simplified statistical methodology is shown to be more conservative in generating RIP distribution than the widely used statistical methodology. Comparison of the significances of each input variable to RIP indicates that fission gas release model is the most significant input variable.

• Nuclear Engineering and Technology
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• v.50 no.1
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• pp.107-115
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• 2018

To ensure the structural integrity of nuclear power plants, it is essential to predict the lifetime of Alloy 182 weld, which is used for welding in nuclear reactors. The lifetime of Alloy 182 weld is directly related to the crack initiation time. Owing to the large time scatter in most crack initiation tests, a probabilistic model, such as the Weibull distribution, has mainly been adopted for prediction. However, since statistically more advanced methods than current typical methods may be applied, we suggest a statistical procedure for parameter estimation of the crack initiation time of Alloy 182 weld, considering right-censored data and the covariate effect. Furthermore, we suggest a procedure for uncertainty evaluation of the estimators based on the bootstrap method. The suggested statistical procedure can be applied not only to Alloy 182 weld but also to any material degradation data set including right-censored data with covariate effect.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.262-267
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• 2001

In this study, the Reliability of degraded steam turbine blade was evaluated using the limited fatigue data. The statistical estimation of limited fatigue data implies that some unknown uncertainties which may be involved in fatigue reliability analysis. Therefore, an appropriate distribution in the fatigue strength was determined by the characteristic distribution - linear correlation coefficient, fatigue physics, error parameter. 3-parameter Weibull distribution is the most appropriate distribution to assume for infinite region. The load applied on the blade is mainly tensile. The maximum Von-Mises stress is 219.4 MPa at the steady state service condition. The failure probability($F_p$) derived from the strength-stress interference model using Monte carlo simulation under variable service condition is 0.25% at the 99.99% confidence level.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.987-998
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• 2013

This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.