• Journal of Korean Society for Quality Management
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• v.40 no.3
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• pp.278-285
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• 2012

Purpose: Weapon systems have the long life cycle unlike the consumer product. Thus, the reliability of weapon system is improved during the life cycle through the steady technical change. In this paper, we deal with the method of evaluating the reliability of weapon system with the field failure data. Methods: Especially, we present how to gather the field failure data and evaluate the reliability through the case of K-series weapon system. To evaluate reliability, the reliability growth model is used and the result is discussed. Results: It is steadily improved the reliability of K-series weapon system deployed from 2000 to 2004. The frequency of the failures that affect the mission is largely reduced and MTBMF(mean time between mission failure) is also improved. Conclusion: We can guess the trend of the reliability of weapon system with the field data through this study. Furthermore, it can be used to improve the reliability and make maintenance policy.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.2
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• pp.127-136
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• 1991

This paper is concerned with the development and application of a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations in urban area (Seoul). For this, the characteristics of the regression trend, periodicity and dependence of the daily $SO_2$ concentration are investigated by a statistisical analysis of the daily average $SO_2$ values measured in Seoul area during 1989 $\sim$ 1990. Based on these, nonlinear regression time series model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.41-50
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• 1995

In this paper, an iterative procedure, which detects the location of the outliers and the joint estimates of the outliers effects and the model parameters in the autoregressive moving average model with two types of outliers, is proposed. The performance of the procedure is compared with the one in Chen and Liu(1993) through the Monte Carlo simulation. The proposed procedure is very robust in the sense that applies the procedures to the stationary time series model with any types of outliers.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.69-73
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• 1996

Tool wear monitoring is very important aspect in metal cutting because tool wear effects quarity and precision of workpiece, tool life etc. In this study we detected force signal through tool dynamometer in turning and using it we conducted 6th AR modeling and fractal analysis. Finally the back-propagation model of the neural network is utilized to monitor tool wear and features are extracted through AR model and fractal analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.861-864
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• 1993

This research provides the results of a trial to generate the chaos by using nonlinear function constructed by fuzzy inference rules. The chaos generation function or chaotic behavior can be obtained by using Takagi-Sugeno fuzzy model with some constraint of the relationship of its parameters. Two examples are shown in this research. The first is simple example that construct of logistic image by fuzzy model. The second is more complicated one that provide the chaotic time series by non-linear autoregression based on fuzzy model. Simulated results are shown in these examples.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1029-1031
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• 1999

This paper presents an $H_{\infty}$ based robust STATCOM supplementary controller design to suppress the SSR in series-compensated line. This controller is designed to have the robust stability against the plant model uncertainty. Time domain simulations using a nonlinear system model show that the proposed STATCOM supplementary controller can suppress the SSR efficiently against the plant model uncertainty

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.163-167
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• 2003

Based on the transient finite difference solution of Richards' equation, an infiltration model is developed to analyze temporal variation of precipitation recharge in the region of monsoon climates. Simulation results obtained by using time series data of 20-year daily precipitation and pan evaporation indicate that a linear relationship between the annual precipitation and the annual recharge holds for the soils under the monsoon climates with varying degrees of the correlation coefficient depending on the soil types. A sensitivity analysis reveals that the water table depth has little effects on the recharge for the sandy soil, whereas, for the loamy and silty soils, rise of the water table at shallow depths causes increase of evaporation by approximately 100㎜/yr and a corresponding decrease in recharge. A series of simulations for two-layered soils illustrate that the amount of recharge is dominantly determined by the soil properties of the upper layer, although the temporal variation of recharge is affected by both layers.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.90-90
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• 2011

Long-term nonstationary oscillations (NSOs) are commonly observed in hydrological and climatological data series such as low-frequency climate oscillation indices and precipitation dataset. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD.