• Journal of Distribution Science
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• v.15 no.3
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• pp.17-25
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• 2017

Purpose - The study is to examine the impact of the US Purchasing Managers' Index (PMI) on Korea and the US industrial economy including the distribution industry. We analyze its effect on the industrial economy centered on the distribution industry using economy indices in Korea and the US. Research design, data, and methodology - The variables are used to analyze the dynamic relationship which occurs among the US PMI, the industrial production index, producer price index, unemployment rate, and manufacturing Inventories Index in Korea and the US from January 1990 to July 2016 using Vector Error Correction Model. Results - As a main result, the impact of the US PMI on all the economy indices both Korea and the US has the same cyclical movement. The US PMI is positively related to the producer price and the industrial production index of Korea and the US, while it is negatively related to unemployment rate, and the manufacturing inventories index in Korea and the US. Conclusions - The US PMI as an advanced index has a power to predict the economies on Korea and the US. In the end, we find that the US PMI has a great impact on Korea and the US industrial economy.

• Journal of Distribution Science
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• v.17 no.1
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• pp.11-19
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• 2019

Purpose - We document the impact of economic policy uncertainty (EPU) in the US and China on the dynamic spillover effect of macroeconomics such as stock price, housing price in Korea. Research design, data, and methodology - We use the nine variables to analyze the effect which produces a result among the EPU indexes of the US and China on economic variables which is the consumer price index (CPI), housing purchase price composite index, housing lease price, the stock price index in banking industry, construction industry and distribution industry, and composite leading indicator from January 1995 to December 2016 with the Vector Error Correction Model. Result - The US EPU index has significantly a negative relation on the CPI, housing purchase price index, housing lease price index, the stock price index in banking industry, construction industry, and distribution industry in Korea. Conclusions - We find the dynamic effect of the EPU indexes in the US and China on the macroeconomics returns in Korea. This study has an empirical evidence that the economy market in Korea is influenced by the EPU index of the US rather than it of China. The higher EPU, the more risky the economy of in Korea.

• Wind and Structures
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• v.19 no.4
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• pp.371-387
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• 2014

Probability plotting positions are popular and used as the basis for distribution fitting and for inspecting the quality of the fit because of its simplicity. The plotting positions that lead to excellent approximation to the mean of the order statistics should be used if the objective of the fitting is to estimate quantiles. Since the mean depends on the sample size and is not amenable for simple to use closed form solution, many plotting positions have been presented in the literature, including a new plotting position that is derived based on the weighted least-squares method. In this study, the accuracy of using the new plotting position to fit the Gumbel distribution for estimating quantiles is assessed. Also, plotting positions derived by fitting the mean of the order statistics for all ranks is proposed, and an approximation to the covariance of the order statistics for the Gumbel (and Weibull) variate is given. Relative bias and root-mean-square-error of the estimated quantiles by using the proposed plotting position are shown. The use of the proposed plotting position to estimate the quantiles of annual maximum wind speed is illustrated.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.451-456
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• 2018

The use of single component distribution to describe the irregular stand structure of degraded forest often lead to bias. Such biasness can be overcome by the application of finite mixture distribution. Therefore, in this study, finite mixture distribution was used to characterise the irregular stand structure of the Gmelina arborea plantation in Omo forest reserve. Thirty plots, ten each from the three stands established in 1984, 1990 and 2005 were used. The data were pooled per stand and fitted. Four finite mixture distributions including normal mixture, lognormal mixture, gamma mixture and Weibull mixture were considered. The method of maximum likelihood was used to fit the finite mixture distributions to the data. Model assessment was based on negative loglikelihood value ($-{\Lambda}{\Lambda}$), Akaike information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The results showed that the mixture distributions provide accurate and precise characterisation of the irregular diameter distribution of the degraded Gmelina arborea stands. The $-{\Lambda}{\Lambda}$, AIC, BIC and RMSE values ranged from -715.233 to -348.375, 703.926 to 1433.588, 718.598 to 1451.334 and 3.003 to 7.492, respectively. Their performances were relatively the same. This approach can be used to describe other irregular forest stand structures, especially the multi-species forest.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.485-490
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• 2014

The purpose of this Study investigated corneal power, corneal astigmatism and corneal axis according to spherical equivalent of refractive error. We measured spherical equivalent, corneal power and corneal astigmatism in 100 subjects from January 2014 to July 2014. Measured spherical equivalent of refractive error were $-3.01{\pm}3.79D$, corneal power of $43.79{\pm}1.60D$ and corneal astigmatism of $-1.17{\pm}0.79D$ respectively. Prevalence of spherical equivalent of refractive error were as follows : myopes (61%), emmetropes (22%), hyperopes(17%). Corneal astigmatism of refractive error greater than +0.75D was 63% and prevalence of corneal astigmatism were as follows : with-the-rlue astigmatism (84.13%), against-the-rule astigmatism(9.52%) respectively. Corneal power by spherical equivalent increased from hyperopia to myopia. Between spherical equivalent of refractive error and the mean corneal power was significant correlation(r=-0.25, p=0.01). A correlation were found between corneal power and spherical equivalent of refractive error in adults. They have the highest distribution of prevalence myopia among the refractive error. When the refractive error was increased, we found that corneal power was steeper. It is recognized that this can be refractive error factor and correct visual function is considered.

• Journal of Korean Ophthalmic Optics Society
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• v.10 no.3
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• pp.229-235
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• 2005

The results of having studied the relationships between environmental factors and family history which affected distribution of ametropia according to departments are like these. The subjects of study were 920 persons who were the industrial high school students in Daejeon. First, When we searched ametropia degree and congenital factors, expecially in case of emmetropia, it showed that the students' parents who wore glasses amounted to 22.6% in ratio and the students' brothers and sisters wore glasses in 22.8%. But in case of the students of ametropia, their parents' rate of wearing glasses was 27.7%. The result showed that refractive error status of offsprings was related to their parents' eye condition. Second, We studied emmetropia and ametropia who used computer for over 3 hours in order to see relationships between refractive error and acquired factors. The emmetropias were 45.6%, while the ametropias were 70.1%. The result showed that it appeared 24.5% more in ametropia. In the end, refractive error can be greatly influenced by environmental factors. Third, The result of comparing distribution of ametropia with VDT using time according to each department showed the students of constructional information department had much more ametropia rate. They used computers than any other students in taking lessons and spent much more time than other department's students.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• Journal of IKEEE
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• v.18 no.2
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• pp.272-281
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• 2014

In electrical impedance tomography (EIT), modified Newton Raphson (mNR) method is widely used inverse algorithm for static image reconstruction due to its convergence speed and estimation accuracy. The unknown conductivity distribution is estimated iteratively by minimizing a cost functional such that the residual error namely the difference in measured and calculated voltages is reduced. Although, mNR method has good estimation performance, EIT inverse problem still suffers from ill-conditioned and ill-posedness nature. To mitigate the ill-posedness, generally, regularization methods are adopted. The inverse solution is highly dependent on the choice of regularization parameter. In most cases, the regularization parameter has a constant value and is chosen based on experience or trail and error approach. In situations, when the internal distribution changes or with high measurement noise, the solution does not get converged with the use of constant regularization parameter. Therefore, in this paper, in order to improve the image reconstruction performance, we propose a new scheme to determine the regularization parameter. The regularization parameter is computed based on residual error and updated every iteration. The proposed scheme is tested with numerical simulations and laboratory phantom experiments. The results show an improved reconstruction performance when using the proposed regularization scheme as compared to constant regularization scheme.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.172-178
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• 2019

Although the needs for providing resilient PNT information are increasing, threats due to the intentional RFI or space weather change are challenging to resolve. eLoran, which is a terrestrial navigation system that use a high-power signal is considered as a best back-up navigation system. Depending on the user's environment in the eLoran system, the user may use one of E-field or H-field antennas. H-field antenna, which has no restriction on setting stable ground and is relatively resistant to noise of general electronic equipment, is composed of two loops, and shows anisotropic gain pattern due to the different measurement at the two loops. Therefore, the H-field antenna's phase estimation value of signal varies depending on its direction even at the static environment. The error due to the direction of the signal should be eliminated if the user want to estimate the own position more precisely. In this paper, a method to compensate the error according to the geometric distribution between the H-field antenna and the transmitting station is proposed. A model was developed to compensate the directional error of H-field antenna based on the signal generated from the eLoran signal simulator. The model is then used to the survey measurement performed in the land area and verify its performance.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.659-668
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• 2006

The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.