• 응용통계연구
• /
• 제24권5호
• /
• pp.941-949
• /
• 2011

본 논문에서는 세 집단만을 판별분석 할 경우에 계산되는 오분류확률에 영향을 미치는 이상치 판별을 목적으로 하며, 쉽게 응용 가능한 간단한 영향함수식을 제시하였다. 그리고 제시된 수식을 이용하여 안면 데이터로 세 가지 사상체질을 분류해보고 각 관찰값들의 오분류확률에 대한 영향함수를 계산하였다. 이상치를 제거하고 재 판별분석을 하는 데 있어, 오분류확률에 대한 영향함수를 이용하는 것이 효율적인 방법임을 확인하였다.

• Journal of applied mathematics & informatics
• /
• 제28권1_2호
• /
• pp.257-273
• /
• 2010

One can construct a theory of probability of fractional order in which the exponential function is replaced by the Mittag-Leffler function. In this framework, it seems of interest to generalize some useful classical mathematical tools, so that they are more suitable in fractional calculus. After a short background on fractional calculus based on modified Riemann Liouville derivative, one summarizes some definitions on probability density of fractional order (for the motive), and then one introduces successively fractional Euler's integrals (first and second kind) and fractional Hermite polynomials. Some properties of the Gaussian density of fractional order are exhibited. The fractional probability so introduced exhibits some relations with quantum probability.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2003년도 춘계학술대회
• /
• pp.310-315
• /
• 2003

This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

• 한국신뢰성학회지:신뢰성응용연구
• /
• 제18권2호
• /
• pp.130-142
• /
• 2018

Purpose: The purpose of this study is to introduce regression method in the presence of competing risks and to show how you can use the method with hypothetical data. Methods: Survival analysis has been widely used in biostatistics division. But the same method has not been utilized in reliability division. Especially competing risks, where more than a couple of causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not utilized in the area of reliability or they are analysed in the wrong way. Specifically Kaplan-Meier method is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced. In addition, sample competing risks data are analysed using cumulative incidence function along with some graphs. Lastly we compare cumulative incidence functions with regression type analysis briefly. Results: We used cumulative incidence function to calculate the survival probability or failure probability in the presence of competing risks. We also drew some useful graphs depicting the failure trend over the lifetime. Conclusion: This research shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime in the presence of competing risks. Cumulative incidence function is shown to be useful in stead. Some graphs using the cumulative incidence functions are also shown to be informative.

• 한국해안해양공학회지
• /
• 제10권4호
• /
• pp.204-210
• /
• 1998

최대 엔트로피 방법을 이용하여 강한 비정규분포과정의 특성을 갖는 비선형 불규칙 파고의 확률밀도 함수를 유도하였다. 파랑의 파고가 쇄파고(또는 수심)에 의해 제한되고 파고의 1, 2차 모멘트만 주어졌을 경우, 유도된 확률밀도함수는 $H_{b}$ (쇄파고), $H_{m}$(평균파고), $H_{rms}$(파고의 제곱평균평방근)의 매개변수로 폐합형(closed form)으로 표시된다. 파고의 3차 이상의 모멘트가 주어진 경우에는 최대 엔트로피를 갖는 확률밀도함수의 매개변수를 구하기 위해서 비선형 적분 방정식 계를 Newton-Raphson 방법을 이용하여 수치적으로 구하였다. 최대 엔트로피 방법을 이용하여 유도된 파고의 확률밀도함수를 비정규분포의 특성이 강한 실측자료와 비교하였다. 실측자료는 폭풍시 중간수심과 천해에서 측정된 쇄파고에 가까운 자료로서 강한 비선형 불규칙 파랑의 특성을 지니며, 이 경우에도 유도된 확률밀도함수가 측정된 파고의 막대그래프와 잘 일치하였다. 강한 비선형 불규칙파의 특성을 갖는 파랑의 파고일 경우에도 파고의 1, 2차 모멘트만으로도 파고의 분포를 잘 나타낼 수 있었다. 최대 엔트로피 방법을 이용하여 구해진 파고의 확률분포함수는 해안구조물의 설계파를 결정하는 극치파고분포와 파고의 통계적인 특성을 추정하는데 매우 유용하게 이용될 수 있다.

• 대한기계학회논문집B
• /
• 제22권5호
• /
• pp.607-617
• /
• 1998

Comparisons of joint probability density distribution obtained from the raw data of measured droplet sizes and velocities in a transient diesel fuel spray with computed joint probability density function were made. Simultaneous droplet sizes and velocities were obtained using PDPA. Mathematical probability density functions which can fit the experimental distributions were extracted using the principle of maximum likelihood. Through the statistical process of functions, mean droplet diameters, non-dimensional mass, momentum and kinetic energy were estimated and compared with the experimental ones. A joint log-hyperbolic density function presents quite well the experimental joint density distribution which were extracted from experimental data.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2006년도 정기 학술대회 논문집
• /
• pp.857-864
• /
• 2006

Due to the difficulties in numerical generation of random fields that satisfy not only the probabilistic distribution but the spectral characteristics as well. it is relatively hard to find an exact response variability of a structural response with a specific random field which has its features in the spatial and spectral domains. In this study. focusing on the fact that the random field assumes a constant over the domain under consideration when the correlation distance tends to infinity, a semi-theoretical solution of response variability is proposed for in-plane and plate bending structures. In this procedure, the probability density function is used directly resulting in a semi-exact solution for the random field in the state of random variable. It is particularly noteworthy that the proposed methodology provides response variability for virtually any type of probability density functions.

• 한국농공학회지
• /
• 제45권2호
• /
• pp.35-44
• /
• 2003

A system reliability method is proposed to decide reliable serviceability of agricultural irrigation system. Even though reliability method is applied to real engineering situations involving actual life environments and maintaining costs, a number of Issues arise as a modeling and analysis level. This article use concepts that can be described the probability of failure with time variant and series-parallel system reliability analysis model. A proposed method use survivor function that can simulate a time-variant performance function for a lifetime before it is required essential maintenance or replacement to define a target probability of failure in agricultural irrigation canal. In the further study, it is required a relationship between a state of probability of failure and current serviceability to make the optimum repair strategy to maintain appropriate serviceability of an irrigation system.

• 상하수도학회지
• /
• 제34권4호
• /
• pp.251-258
• /
• 2020

Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.

• Structural Engineering and Mechanics
• /
• 제49권1호
• /
• pp.111-128
• /
• 2014

The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.