• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.155-166
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• 2008

The quantitative bivariate spatial pattern analysis was applied for the water quality and nutrients data of Masan Bay, and for this analysis Pearson's r as aspatial correlation measurement, Moran's I as spatial association measurement and L index as integration of aspatial and spatial measurement methods were used. To understand the aspatial and spatial characteristics implicated in L index, Pearson's r as well as Moran's I were classified into 3 types respectively, and Pearson's r and Moran's I were combined with 9 types, and also quantile of L index value was used for each of those 9 types. Finally, these types were defined as 5 groups having not overlapped L index range. According to the application result of L index groups, bivariate water quality and nutrients showed no aspatial correlation regardless of spatial association in February and July, but they showed aspatial correlation having clustered spatial pattern in May and November. The result of this study providing the guideline for the interpretation of aspatial correlation and spatial association using L index is expected to be helpful for the marine environment pattern analysis using quantitative index for further study.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.170.1-170.1
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• 2005

• Tribology and Lubricants
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• v.18 no.4
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• pp.285-290
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• 2002

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Journal of Nutrition and Health
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• v.26 no.3
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• pp.357-366
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• 1993

The purposes of this study were to investigate the operational affecting productivity in hospital foodservice, and to examine the relationships between operational factors affecting productivity. The 28 hospitals over 400 beds in Seoul were mailed questionnaires assessing the factors that affect productivity in hospital foodservice(23 hospitals responded). Data analyses included descriptive statistics. Pearson product moment correlation analysis, and stepwise multiple regression analysis. The result of Pearson product moment correlation analysis indicated that the percentage of patient meals was significantly correlated to the productivity (r=.5560, p<.01). Stepwise multiple regression analysis indicated that the percentage of patient meals and the average work hours of employees were significant predictors of the operational factors at productivity.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.252-259
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• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.690-693
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• 2005

In this paper, probabilistic distribution of fatigue life of chassis component is determined statistically by applying the design of experiments and the Pearson system. To construct $p-\varepsilon-N$ curve, the case that fatigue data are random variables is attempted. Probabilistic density function(p.d.f) for fatigue life is obtained by design of experiment and using this p.d.f fatigue reliability about any aimed fatigue life can be calculated. Lower control arm and rear torsion bar of chassis component are selected as examples for analysis. Component load histories, which are obtained by multi-body dynamic simulation for Belsian load history, are used. Finite element analysis are performed using commercial software MSC Nastran and fatigue analysis are performed using FE Fatigue. When strain-life curve itself is random variable, probability density function of fatigue life has very little difference from log-normal distribution. And the case of fatigue data are random variables, probability density functions are approximated to Beta distribution. Each p.d.f is verified by Monte-Carlo simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.700-703
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• 2004

A reliability analysis and design procedure based on the design of experiment (DOE) is combined with the response surface method (RSM) for numerical efficiency. The procedure established is based on a 3$^n$ full factorial DOE for numerical quadrature using explicit formula of optimum levels and weights derived for general distributions. The full factorial moment method (FFMM) shows good performance in terms of accuracy and ability to treat non-normally distributed random variables. But, the FFMM becomes very inefficient because the number of function evaluation required increases exponentially as the number of random variables considered increases. To enhance the efficiency, the response surface moment method (RSMM) is proposed. In RSMM, experiments only with high probability are conducted and the rest of data are complemented by a quadratic response surface approximation without mixed terms. The response surface is updated by conducting experiments one by one until the value of failure probability is converged. It is calculated using the Pearson system and the four statistical moments obtained from the experimental data. A measure for checking the relative importance of an experimental point is proposed and named as influence index. During the update of response surface, mixed terms can be added into the formulation.

• Journal of Power System Engineering
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• v.19 no.1
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• pp.56-63
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• 2015

In this study, the exhaust characteristics of the diesel engine for the change of the mixing ratio of biodiesel fuel were quantitatively analyzed by using the numerical analysis method. As the fuel used in the experiment, the diesel and biodiesel(waste oil, soybean oil), the mixed fuel BD2(Diesel only), BD3, BD5, BD20, BD50 and BD100 were used. The injection pressure($p_{inj}$) was set to 400bar, 600bar, 800bar, 1000bar and 1200bar as the experimental variable. Also the concept of the standard deviation, Pearson's correlation coefficient and Spearman rank-order correlation coefficient based on the statistics was introduced in order to analyze the exhaust characteristics of the quantitative NOx and Soot according to the injection pressure and the mixing ratio variation of biodiesel blending fuel. It is considered that as a result of studies, for the waste oil, NOx and Soot can be simultaneously reduced through control of the mixing ratio at the regions of $p_{inj}=400bar$ and $p_{inj}=600bar$, and the Soot can be reduced without affecting on the emission of NOx at more than $p_{inj}=800bar$. For the soybean oil, NOx and Soot can be simultaneously reduced at $p_{inj}=400bar$ and the Soot can be reduced without affecting on the emission of NOx at $p_{inj}=600bar$.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.2 s.191
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• pp.110-117
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• 2007

In this paper, probabilistic distribution of chassis component fatigue life is determined statistically by applying the design of experiments and the Pearson system. To construct p - ${\varepsilon}$ - N curve, the case that fatigue data are random variables is attempted. Probabilistic density function (p.d.f) for fatigue life is obtained by the design of experiment and using this p.d.f fatigue reliability, any aimed fatigue life can be calculated. Lower control arm and rear torsion bar of chassis components are selected as examples for analysis. Component load histories which are obtained by multi-body dynamic simulation for Belsian load history are used. Finite element analysis is performed by using commercial software MSC Nastran and fatigue analysis is performed by using FE Fatigue. When strain-life curve itself is random variable, the probability density function of fatigue life has very little difference from log-normal distribution. And the cases of fatigue data are random variables, probability density functions are approximated to Beta distribution. Each p.d.f is verified by Monte-Carlo simulation.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.65-73
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• 2017

Purpose: Machine Performance Check (MPC) is a self-checking software based on the Electronic Portal Imaging Device (EPID) to measure daily beam outputs without external installation. The purpose of this study is to verify the usefulness of MPC by comparing and correlating daily beam output of QA Beamchecker PLUS. Materials and Methods: Linear accelerator (Truebeam 2.5) was used to measure 10 energies which are composed of photon beams(6, 10, 15 MV and 6, 10 MV-FFF) and electron beams(6, 9, 12, 16 and 20 MeV). A total of 80 cycles of data was obtained by measuring beam output measurement before treatment over five months period. The Pearson correlation coefficient was used to evaluate the consistency of the beam output between the MPC and the QA Beamchecker PLUS. In this study, if the Pearson correlation coefficient is; (1) 0.8 or higher, the correlation is very strong (2) between 0.6 and 0.79, the correlation is strong (3) between 0.4 and 0.59, the correlation is moderate (4) between 0.2 and 0.39, the correlation is weak (5) lower than 0.2, the correlation is very weak. Results: Output variations observed between MPC and QA Beamchecker PLUS were within 2 % for photons and electrons. The beam outputs variations of MPC were $0.29{\pm}0.26%$ and $0.30{\pm}0.26%$ for photon and electron beams, respectively. QA Beamchecker PLUS beam outputs were $0.31{\pm}0.24%$ and $0.33{\pm}0.24%$ for photon and electron beams, respectively. The Pearson correlation coefficient between MPC and QA Beamchecker PLUS indicated that photon beams were very strong at 15 MV, and strong at 6 MV, 10 MV, 6 MV-FFF and 10 MV-FFF. For electron beams, the Pearson correlation coefficient were strong at 16 MeV and 20 MeV, moderate at 9 MeV and 12 MeV, and very weak at 6 MeV. Conclusion: MPC showed significantly strong correlation with QA Beamchecker PLUS when testing with photon beams and high-energy electron beams in the evaluation of daily beam output, but the correlation when testing with low-energy electron beams (6 MeV) appeared to be low. However, MPC and QA Beamchecker PLUS are considered to be suitable for checking daily beam output, as they performed within 2 % of beam output consistency during the observation. MPC which can perform faster than the conventional daily beam output measurement tool, is considered to be an effective method for users.