• 제목/요약/키워드: Normal Probability Plot

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2-수준 요인실험에서 주효과 및 교호작용에 대한 효율적인 분석방법 연구 (A Study on Effective Identification Method for Influential Main Effects and Interactions in the 2-level Factorial Designs)

  • 김상익
    • 품질경영학회지
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    • 제34권1호
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    • pp.27-33
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    • 2006
  • In this paper, an effective method for identifying influential main effects and interactions in the 2-level factorial designs is suggested by exploiting the resolution V designs developed by Kim(1992). For analysis of such designs, we employ the Bayesian approach for easy and clear identification of influential effects in the half normal probability plot.

고장 데이터의 플롯을 이용한 소프트웨어 신뢰도 성장 모델의 성능평가 (Performance estimation for Software Reliability Growth Model that Use Plot of Failure Data)

  • 정혜정;양해술;박인수
    • 정보처리학회논문지D
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    • 제10D권5호
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    • pp.829-836
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    • 2003
  • 소프트웨어 신뢰도 성장 모델은 다양하게 연구되어져 있다. 그러나 이러한 모델에서 정확한 모수를 측정하는 것은 그리 쉽지 않다. 특히 고장 데이터에 대하여 소프트웨어 신뢰도 성장 모델의 추정이 정확히 이루어져야만 모델을 설명하는 모수의 추정도 정확하게 이루어질 수 있다. 이러한 측면에서 테스팅을 통해서 얻어진 소프트웨어의 고장 데이터의 정규확률점수를 구해서 두 개의 값에 대한 플롯을 그려보고 그려진 결과를 이용해서 분포를 예측하여 예측된 분포에 적합한 소프트웨어 신뢰도 성장 모델을 적용한다면 상당히 정확한 테스팅 결과론 얻을 수 있을 것이다. 본 논문에서는 고장 테이터의 플롯을 통한 결과를 통해서 분포를 예측하고 모델을 성능평가 척도에 따라서 모의실험을 하여 그 결과를 통해서 소프트웨어 신뢰도 성장 모델의 적합성을 검정하는 연구이다. 연구결과 고장데이터의 정규점수를 이용한 플롯을 보고 소프트웨어 신뢰도 성장 모델을 예측할 수 있었고 이러한 예측을 통해서 모델 선정한다면 모델의 성능평가에서도 우수함을 확인할 수 있다.

$K^n$ 요인배치법에서 포화실험에 의한 요인효과의 검정 (Tests of Factor Effect Using Saturated Design in $K^n$ Factorial Design)

  • 최성운
    • 대한안전경영과학회:학술대회논문집
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    • 대한안전경영과학회 2008년도 춘계학술대회
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    • pp.295-299
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    • 2008
  • This paper discusses tests of factor effect or contrast by the use of saturated design $k^n$ factorial design. The nine nonparametric rank measures in normality test using normal probability pot are proposed. Length's PSE(Pseduo Standard Error) test [4] which relies on the concept of effect sparsity is also introduced and extended to the margin of error(ME) and Simultaneous margin of error(SME).

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빈도 분석법을 이용한 논벼의 한발 기준 10년 빈도 작물 증발산량 산정 (Estimating Paddy Rice Evapotranspiration of 10-Year Return Period Drought Using Frequency Analysis)

  • 유승환;최진용;장민원
    • 한국농공학회논문집
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    • 제49권3호
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    • pp.11-20
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    • 2007
  • Estimation of crop consumptive use is a key term of agricultural water resource systems design and operation. The 10-year return period drought has special aspects as a reference period in design process of irrigation systems in terms of agricultural water demand analysis so that crop evapotranspiration (ETc) about the return period also has to be analyzed to assist understanding of crop water requirement of paddy rice. In this study, The ETc of 10-year return period drought was computed using frequency analysis by 54 meteorological stations. To find an optimal probability distribution, 8 types of probability distribution function were tested by three the goodness of fit tests including ${\chi}^2$(Chi-Square), K-S (Kolmogorov-Smirnov) and PPCC (Probability Plot Correlation Coefficient). Optimal probability distribution function was selected the 2-parameter Log-Normal (LN2) distribution function among 8 distribution functions. Using the two selected distribution functions, the ETc of 10-year return period drought was estimated for 54 meteorological stations and compared with prior study results suggested by other researchers.

와이블 분포함수를 이용한 하수관로 노후도 추정 (Estimation of sewer deterioration by Weibull distribution function)

  • 강병준;유순유;박규홍
    • 상하수도학회지
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    • 제34권4호
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    • pp.251-258
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    • 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.

AISI 304강의 상온/고온 인장특성에 관한 연구 (A Study on Tensile Characteristics of AISI 304 Stainless Steel under Room and Elevated Temperatures)

  • 박성호;박노석;김재훈
    • 한국추진공학회지
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    • 제12권5호
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    • pp.35-42
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    • 2008
  • 본 연구는 AISI 304강의 상온 및 고온 인장실험을 수행한 결과이다. 항공구조재료로 널리 사용되고 있는 AISI 304강의 인장실험을 ASTM 규정에 따라 상온 및 고온에서 수행하였다. A Basis와 B Basis 인장강도를 평가하기 위하여 정규확률지를 사용하였다. 응력과 소성변형률과의 관계를 지수함수로 가정하는 Ramberg-Osgood 파라미터는 시험데이터의 최소제곱추정을 이용하여 구하였다. 인장실험 후 시험편의 표면을 SEM 영상과 EDX를 사용하여 관찰하였다.

배열을 이용한 효과적인 일부실시법의 설계 및 분석방법에 관한 연구 (A Study on the Construction and Analysis of Fractional Designs by Using Arrays for Factorial Experiments)

  • 김상익
    • 품질경영학회지
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    • 제40권1호
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    • pp.15-24
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    • 2012
  • For the construction of fractional factorial designs, the various arrays can be widely used. In this paper we review the statistical properties of fractional designs constructed by two arrays such as orthogonal array and partially balanced array, and develop a quick and easy method for analyzing unreplicated saturated designs. The proposed method can be characterized that we control the error rate by experiment-wise way and exploit the multivariate Student $t$-distribution. Especially the proposed method can be used efficiently together with some exploratory analysis methods, such as half normal probability plot method.

대구지방(大邱地方)의 확률일우량(確率日雨量)에 관(關)한 연구(硏究) (A Study on the Daily Probability of Rainfall in the Taegu Area according to the Theory of Probaility)

  • 김영기;나인엽
    • 자원환경지질
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    • 제4권4호
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    • pp.225-234
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    • 1971
  • With the advance of civilization and steadily increasing population rivalry and competition for the use of the sewage, culverts, farm irrigation and control of various types of flood discharge have developed and will be come more and more keen in the future. The author has tried to calculated a formula that could adjust these conflicts and bring about proper solutions for many problems arising in connection with these conditions. The purpose of this study is to find out effective sewage, culvert, drainage, farm irrigation, flood discharge and other engineering needs in the Taegu area. If demands expand further a new formula will have to be calculated. For the above the author estimated methods of control for the probable expected rainfall using a formula based on data collected over a long period of time. The formula is determined on the basis of the maximum daily rainfall data from 1921 to 1971 in the Taegu area. 1. Iwai methods shows a highly significant correlation among the variations of Hazen, Thomas, Gumbel methods and logarithmic normal distribution. 2. This study obtained the following major formula: ${\log}(x-2.6)=0.241{\xi}+1.92049{\cdots}{\cdots}$(I.M) by using the relation $F(x)=\frac{1}{\sqrt{\pi}}{\int}_{-{\infty}}^{\xi}e^{-{\xi}^2}d{\xi}$. ${\xi}=a{\log}_{10}\(\frac{x+b}{x_0+b}\)$ ($-b<x<{\infty}$) ${\log}(x_0+b)=2.0448$ $\frac{1}{a}=\sqrt{\frac{2N}{N-1}}S_x=0.1954$. $b=\frac{1}{m}\sum\limits_{i=1}^{m}b_s=-2.6$ $S_x=\sqrt{\frac{1}{N}\sum\limits^N_{i=1}\{{\log}(x_i+b)\}^2-\{{\log}(x_0+b)\}^2}=0.169$ This formule may be advantageously applicable to the estimation of flood discharge, sewage, culverts and drainage in the Taegu area. Notation for general terms has been denoted by the following. Other notations for general terms was used as needed. $W_{(x)}$ : probability of occurranec, $W_{(x)}=\int_{x}^{\infty}f_{(n)}dx$ $S_{(x)}$ : probability of noneoccurrance. $S_{(x)}=\int_{-\infty}^{x}f_(x)dx=1-W_{(x)}$ T : Return period $T=\frac{1}{nW_{(x)}}$ or $T=\frac{1}{nS_{(x)}}$ $W_n$ : Hazen plot $W_n=\frac{2n-1}{2N}$ $F_n=1-W_x=1-\(\frac{2n-1}{2N}\)$ n : Number of observation (annual maximum series) P : Probability $P=\frac{N!}{{t!}(N-t)}F{_i}^{N-t}(1-F_i)^t$ $F_n$ : Thomas plot $F_n=\(1-\frac{n}{N+1}\)$ N : Total number of sample size $X_l$ : $X_s$ : maximum, minumum value of total number of sample size.

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