• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.101-110
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• 1998

The mean residual life function is the expected remaining life of an item at age $\chi$. The problem of trend change in the mean residual life is great interest in the reliability and survival analysis. In this paper we develop a new test statistic for testing whether or not the mean residual life changes its trend based on a complete sample. Monte Carlo simulations are conducted to compare the perfor mance of our test statistic with that of previously known tests.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.789-798
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• 2000

The mean residual function is the expected remaining life of an item at age x. The problem of trend change in the mean residual life is great interest in the reliability and survival analysis. In this paper, we develop a family of test statistics for testing whether or not the mean residual life changes its trend. The asymptotic normality of the test statistics is established. Monte Carlo simulations are conducted to study the performance of our test statistics.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.79-86
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• 2022

Recently, the amount of maintenance is increasing due to the aging of power facilities, but the budget is constrained. Therefore, the importance of asset management that selects replacement priorities based on the failure probability and enhances investment effects is increasing. Because the number of distribution transformers is very large, the proportion of investment cost is very high. Therefore, it is important to select the investment priority by evaluating the reliable remaining life based on the failure probability. This paper evaluates the statistical expected life using the failure data of distribution transformers for the last 11 years and the current operation data. The hazard rate of distribution transformer and MV cable was compared with each other and the adequacy of early failure removal was reviewed and the statistical expected life corresponding to the cumulative failure probability B3% was calculated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• The Journal of Information Systems
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• v.29 no.1
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• pp.75-91
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• 2020

Purpose Retrofit, defined to be addition of new technologies or features to the old system to increase efficiency or to abate GHG emissions, is considered as an important alternative for the old coal-fired power plant. The purpose of this study is to propose mathematical method to model multiple alternative retrofit in Generation Capacity Expansion Planning(GCEP) problem, and to get insight to the retrofit patterns from realistic case studies. Design/methodology/approach This study made a multi-alternative retrofit GECP model by adopting some new variables and equations to the existing GECP model. Added variables and equations are to ensure the retrofit feature that the life time of retrofitted plant is the remaining life time of the old power plant. We formulated such that multiple retrofit alternatives are simultaneously compared and the best retrofit alternative can be selected. And we found that old approach to model retrofit has a problem that old plant with long remaining life time is retrofitted earlier than the one with short remaining life time, fixed the problem by some constraints with some binary variables. Therefore, the proposed model is formulated into a mixed binary programming problem, and coded and run using the GAMS/cplex. Findings According to the empirical analysis result, we found that approach to model the multiple alternative retrofit proposed in this study is comparing simultaneously multiple retrofit alternatives and select the best retrofit satisfying the retrofit features related to the life time. And we found that retrofit order problem is cleared. In addition, the model is expected to be very useful in evaluating and developing the national policies concerning coal-fired power plant retrofit.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.271-280
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• 2019

Smart factory, a critical part of digital transformation, enables data-driven decision making using monitoring, analysis and prediction. Predictive maintenance is a key element of smart factory and the need is increasing. The purpose of this study is to analyze the degradation characteristics of a galvanizing kettle for the steel plating process and to predict the remaining useful life(RUL) for predictive maintenance. Correlation analysis, multiple regression, principal component regression were used for analyzing factors of the process. To identify the trend of degradation, a proposed rolling window was used. It was observed the degradation trend was dependent on environmental temperature as well as production factors. It is expected that the proposed method in this study will be an example to identify the trend of degradation of the facility and enable more consistent predictive maintenance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.1-11
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• 2021

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

• KDI Journal of Economic Policy
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• v.39 no.2
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• pp.25-51
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• 2017

This paper employs an Overlapping Generations Model to quantify the impacts of Korea's demographic transition toward an older population on the total output growth rate. The model incorporates the projected population through 2060 according by Statistics Korea. The effects of the low fertility and increased life expectancy rates are studied. The model is considered suitable for analyzing the effects of demographic changes on the Korean economy. Under the assumption that the TFP growth rate will not slow considerably in the future, remaining at 1.3% per annum, the gross output growth rate of the Korean economy is projected to slow to 1.1% per annum in the 2050s, from 4.0% in the 2000s. The shrinking workforce due to the decline in fertility plays a significant role in the deceleration of the Korean economy. The increased life expectancy rate is expected to mitigate the negative effect, but the magnitude of its effect is found to be limited.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.11-21
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• 1997

The mean residual life(MRL) function gives the expected remaining life of a item at age t. In particular F is said to be an increasing intially then decreasing MRL(IDMRL) distribution if there exists a turing point $t^*\ge0$ such that m(s)$\le$ m(t) for 0$$\le s$\le$ t $t^*$, m(s)$\ge$ m(t) for $t^*\le$ s$\le$ t. If the preceding inequality is reversed, F is said to be a decreasing initially then increasing MRL(DIMRL) distribution. Hawkins, et al.(1992) proposed test of H0 : F is exponential versus$H_1$: F is IDMRL, and $H_0$ versus $H_1$' : F is DIMRL when turning point is unknown. Their test is based on a complete random sample $X_1$, …, $X_n$ from F. In this paper, we generalized Hawkins-Kochar-Loader test to random censored data.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.25 no.1
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• pp.13-19
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• 2015

Among the various possible factors affecting the Minority Carrier Life Time (MCLT) of the mc-Si crystal, dislocations formed during the cooling period after solidification were found to be a major element. It was confirmed that other defects such as grain boundary or twin boundary were not determinative defects affecting the MCLT because most of these defects seemed to be formed during the solidification period. With a measurement of total thickness variation (TTV) and bow of the silicon wafers, it was found that residual stress remaining in the mc-Si crystal might be another major factor affecting the MCLT. Thus, it is expected that better quality of mc-Si can be grown when the cooling process right after solidification is carried out as slow as possible.