• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.6
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• pp.48-53
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• 2002

품질보증 부분에서 대부분 통계적 수법들은 기지분포를 가정하여 데이터를 분석하고 있다. 예를 들면 공정능력에서는 정규분포를 가정하고 있고, 신뢰성 분석에서는 지수분포, 대수정규분포 또는 와이블분포 등을 가정하고 있다. 만약 이러한 것들이 가정하는 확률분포와 크게 편의될 때 도출된 결론은 그 유효성을 상실하게 된다. 따라서 공정이 정규분포를 하지 않는다면 정규분포에 기초한 공정능력지수의 사용은 부정확한 공정의 정보를 제시하게 된다. 이러한 문제를 해결하기 위하여 그 사례로 소표본일 경우 정규성의 유무를 Lilliefors 검정통계량으로 검정하였다. 그리고 최우추정량(MLE), 수정적률추정량(MME), 및 적률추정량(ME)을 사용하여 모수추정을 하여 그 각각의 경우에 공정능력지수로 평가하였다. 공정능력지수의 평가는 공정이 지수분포를 하는 경우에 적합한 공정능력지수($C_{pe}$)를 제안하고, 이것의 대안으로써 Pearson 시스템, Johnson 시스템 및 Burr 시스템과도 비교평가 하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.3
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• pp.183-189
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• 2013

In this paper, uncertainties and failure criteria of structure are mathematically expressed by random variables and a limit state equation. A limit state equation is approximated by Fleishman's 3rd order polynomials and the theoretical moments of an approximated limit state equation are calculated. Fleishman introduced a 3rd order polynomial in terms of only standard normal distiribution random variables. But, in this paper, Fleishman's polynomial is extended to various random variables including beta, gamma, uniform distributions. Cumulants and a normalized limit state equation are used to calculate a theoretical moments of a limit state equation. A cumulative distribution function of a normalized limit state equation is approximated by a Pearson system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.281-291
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• 1999

This paper is a brief review of the different procedures that are available for fitting theoretical distributions to data. The use of each technique is illustrated by reference to a distribution system which including the Pearson, Johnson and Burr functions. These functions can be used to calculate percent out of specification. The main objectives of this study are to propose a new methods for estimating a measure of process capability for Beta distributed variable data by using the percentage nonconforming. The comprehensive information for the process can be used to evaluate more accurately process capability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1682-1685
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• 2005

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolerance of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or estimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte-Carlo Method and got the probabilistic sensitivity. The sensitivity of structural response with respect to inconstant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.133-142
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• 1998

This paper is a brief review of the different procedures that are available for fitting theoretical distributions to data. The use of each technique is illustrated by reference to a distribution system which including the Pearson, Poission approximation of Gamma distribution and Burr functions. These functions can be used to calculate percent out of specification. Therefore, in this paper a new methods for estimating a measure of non-normal process capability for Gamma distributed variable data proposed using the percentage nonconforming. Process capability indices combines with the percentage nonconforming information can be used to evaluate more accurately process capability.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.6
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• pp.110-119
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• 2007

This study's objects are to predict distribution and to assess vulnerability of sub-alpine vegetations in the Korean peninsula for climate change using various climate models. This study validates relationship between sub-alpine vegetations and environmental factors using Pearson correlation analysis. Then, the future distribution of sub-alpine vegetations are predicted by a logistic regression. The major findings in this study are; First, spring mean temperature (March-May), total precipitation, elevation and warmth index are highly influencing factors to the distribution of sub-alpine vegetations. Second, the sub-alpine vegetations will be disappeared in South Korea and concentrated around Baekdu Mountain in North Korea. North Korea is predicted to have serious impact of climate change because temperature will be increased higher than in South Korea. The study findings concluded that the assessment of the future vulnerability of sub-alpine vegetations to climate change are significant.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.869-878
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• 2007

In this paper a new process capability index $C_{psks}$ is introduced for non-normal process. $C_{psks}$ that is proposed by transformation of the $C_{psks}$ incorporates an additional skewness correction factor in the denominator of $C_{psks}$. The use of each technique is illustrated by reference to a distribution system which includes the Pearson and Johnson functions. Accordingly, $C_{psks}$ is proposed as the process capability measure for non-normal process.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.937-943
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• 2007

In this paper a new process incapability index $C^*_{psks}$ is introduced for non-normal process. $C^*_{psks}$ is proposed by transformation of the $C^*_{psks}$. The use of each technique is illustrated by reference to a distribution system which includes the Pearson and Johnson functions. Accordingly, $C^*_{psks}$ is proposed as the process capability measures for non-normal process.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.18-34
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• 1999

This paper is a brief review of the different procedures that are available for fitting theoretical distributions to data. The use of each technique Is illustrated by reference to a distribution system which including the Pearson, Johnson and Burr functions. These functions can be used to calculate percent out of specification. Therefore, in this paper a new method for estimating a measure of process capability for Gamma distributed variable data proposed using the percentage nonconforming. Process capability indices combines with the percentage nonconforming Information can be used to evaluate more accurately process capability.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.989-998
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• 2018

Connectivity between water source and demand node can be served as a critical system performance indicator of the degree of water distribution network (WDN)' failure severity under abnormal conditions. Graph theory-based approaches have been widely applied to quantify the connectivity due to WDN's graph-like topological feature. However, most previous studies used undirected-unweighted graph theory which is not proper to WDN. In this study, the directed-weighted graph theory was applied for WDN connectivity analyses. We also proposed novel connectivity indicators, Source-to-Node Shortest Pathway (SNSP) and SNSP-Degree (SNSP-D) which is an inverse of the SNSP value, that does not require complicate hydraulic simulation of a WDN of interest. The proposed SNSP-D index was demonstrated in total 42 networks in J City, South Korea in which Pearson Correlation Coefficient (PCC) between the proposed SNSP-D and four other system performance indicators was computed: three resilience indexes and an energy efficiency metric. It was confirmed that a system representative value of the SNSP-D has strong correlation with all resilience and energy efficiency indexes (PCC = 0.87 on average). Especially, PCC was higher than 0.93 with modified resilience index (MRI) and energy efficiency indicator. In addition, a multiple linear regression analysis was performed to identify the system hydraulic characteristic factors that affect the correlation between SNSP-D and other system performance indicators. The proposed SNSP is expected to be served as a useful surrogate measure of resilience and/or energy efficiency indexes in practice.