• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_2
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• pp.993-1002
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• 2022

This paper relates to a method of modeling the thrust dynamic load caused by the thrust variation occurring on the blade due to the tower shadow below the rated wind speed. A method that uses thrust coefficient is presented by introducing "tower shadow coefficient of thrust variation". For a 2MW wind turbine, the values of "tower shadow coefficient of thrust variation" are calculated and analyzed at wind speeds below the rated. The dynamic load model of thrust under tower shadow is evaluated in Matlab/Simulink using the obtained "tower shadow coefficient of thrust variation" and thrust coefficient. It shows that the thrust variations acting on the three blades by the tower shadow can be expressed using both the thrust coefficient and the introduced "tower shadow coefficient of thrust variation".

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.99-113
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• 2001

This paper deals with the problem of estimating the means in two inverse Gaussian populations with equal but unknown coefficient of variation. The maximum likelihood estimators are derived by solving a cubic equation and their asymptotic variances are presented for comparative purpose. Monte-Carlo simulation is conducted to investigate the efficiency of the estimators relative to the sample means over a wide range of values for the sample size and the coefficient of variation. The effect on this efficiency under the departure from the assumption of common coefficient of variation is also studied.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.6
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• pp.875-885
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• 2019

This paper is aimed to suggest the coefficient of variation of the total load on the segment lining and the coefficient of variation of earth pressure in reliability based design of shield tunnel. For this purpose, the statistical characteristics of weathered soil and weathered rock were calculated by analyzing the site survey data of the domestic urban section. The coefficient of variation could be estimated by applying these values to Terzaghi's theory using MCS technique. As a result, the coefficient of variation of rock load for weathered soil and weathered rock was 0.08~0.14. The coefficient of variation for the total load acting on the tunnel was LC1 = 0.38, LC2 = 0.33, and LC3 = 0.37. The proposed coefficients of variation can be used in the reliability-based design of shield tunnel segments.

• Fisheries and Aquatic Sciences
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• v.6 no.4
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• pp.220-224
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• 2003

In order to determined the appropriate processing season of Pacific oyster (Crassostrea gigas), 30 individuals cultured in Kamak Bay on the southern coast of Korea were taken bimonthly 7 times a year, and seasonal variation of edible portion weight, edible portion yield and coefficient of fatness were investigated. There were close relationships between edible portion weight (X) and total weight (Y) and between coefficient of fatness (X) and edible portion yield (Y), which were expressed in regression lines, i.e. Y=0.2709X-1.9094 (r=0.9254) and Y=44.0596X+ 15.1127 (r=0.8485), respectively. From the results of seasonal variation of edible portion weight, yield and coefficient of fatness, the appropriate processing season of raw Pacific oyster was between winter and spring, that is, between December and following April the next year.

• Wind and Structures
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• v.18 no.6
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• pp.633-650
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• 2014

The uncertainty of extreme wind speeds is one key contributor to the uncertainty of wind loads and their effects on structures. The probability distribution of annual extreme wind speeds may be characterized using a classical Gumbel Type distribution. The expression that establishes the relationship between the extreme wind speeds at different recurrence periods and the corresponding coefficients of variation is formulated, and its efficacy is validated. The coefficients of variation are calibrated to be about 0.125 and 0.184 according to defined Chinese and US design specifications, respectively. Based on the wind data of 54 cities in China, 49 meteorological stations in the US, 3 stations in Singapore, the coefficients span intervals of (0.1, 0.35), (0.08, 0.20) and (0.06, 0.14), respectively. For hurricanes in the US, the coefficients range approximately from 0.3 to 0.4. This convenient technique is recommended as one alternative tool for coefficient of variation analyses in the future revisions of related codes. The sensitivities of coefficients of variation for 49 meteorological stations in the US are quantified and demonstrated. Some contradictions and incompatibilities can be clearly detected and illustrated by comparing the coefficients of variation obtained with different combinations of recurrence period wind data.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.3
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• pp.311-321
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• 2020

In this study, coefficient of variation for the seismic load effect on the segment lining was calculated. The statistical characteristics of the soil property were analyzed for the probability characteristics of domestic soil. In order to calculate the coefficient of variation for the seismic load effect, the MCS technique was applied, and the closed-form equation was applied to calculate the seismic load effect. As a result of calculating the coefficient of variation, the coefficient of variation of the seismic load effect on the weathered soil was analyzed in the range of 0.06~0.15, and the coefficient of variation was judged to be used as basic data for designing the limit state of the shield tunnel on seismic condition.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.4
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• pp.407-414
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• 2016

As wind turbines are getting larger in size with multi-MW capacity, the blades are getting longer, over 40 m, and hence the asymmetric loads produced during the rotation of the rotor blades are increasing. Some factors such as wind shear, tower shadow, and turbulence have an effect on the asymmetric loads on the blades. This paper focuses on a method of modeling the dynamic load acting on a blade because of thrust variation under wind shear. A method that uses thrust coefficient is presented. For this purpose, "wind shear coefficient of thrust variation" is defined and introduced. Further, we calculate the values of the "wind shear coefficient of thrust variation" for a 2 MW on-shore wind turbine, and analyze them for speeds below the rated wind speed. Then, we implement a dynamic model that represents the thrust variation under wind shear on a blade, using MATLAB/Simulink. It is shown that it is possible to express thrust variations on three blades under wind shear by using both thrust coefficient and "wind shear coefficient of thrust variation."

• 한국해양학회지
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• v.17 no.2
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• pp.74-82
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• 1982

The study on the variation coefficient of water temperature and salinity was comducted during the year from 1968 to 1980 in the Southern Sea of Korea. The results obtaland from the study as followes; 1. The variation coefficient of water temperature and salinity wewe large either at the front area or the thermocline and malocline area. 2. The variation coefficient of water temperature was the largest at the time when the power was strong ty each water mass(The largest value in Tsushima and Yellow Sea Warm Current area was occurred at the 50m layer in the Summer, and that in the South Korean Coastal Water area and the Southern Part of Yellow Sea was at all layer in the Winter). 3. The variation coefficient of salinity was the largest at the surface layer in warm current area that was influenced by the low salinity of the East China Coastal Water in the Summer ,and that of salinity in the South Korean Coastal Water area and Soutern Part of Yellow Sea was nearly half of the value of the warm current area.

• Journal of the Korean Statistical Society
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• v.35 no.3
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• pp.331-341
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• 2006

An approximate distribution of the plug-in estimator of the squared coefficient of variation ($CV^2$) is derived by using Edgeworth expansions under general population models. Also bias of the estimator is investigated for several important distributions. Under the normal distribution, we proposed the new estimator for $CV^2$ based on median of the sampling distribution of plug-in estimator.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.509-516
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• 2008

We derive the influence function on the coefficient of variation. Empirical influence function and Sample influence function are used to verify the validity of the derived influence function. To show the validity of the influence function, we carry out simulations with random samples from normal distribution $N(20,1^2)$ and $N(20,5^2)$, respectively. The simulation result proves that the derived influence function is very accurate in estimating changes in the coefficient of variation when an observation is deleted.