• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.327-333
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• 2015

Wind turbines, in the case of less than rated wind speed, is controlled to achieve maximum power. MPC(Maximun Power Control) method, by controlling the rotational speed of the generator, is a method to achieve maximum power but should know the wind speed. However, for several reasons, there have been proposed methods of estimating the wind speed rather than measuring wind speed. TSR(Tip Speed Ratio) is needed to know to estimate the wind speed. However, a complex interaction formula has to be solved to find a TSR. Therefore, many methods have been suggested to solve a complex interaction formula. In this paper, the new method has been proposed to simplify the complicated interaction formula by using the regression method. Matlab/Simulink is used to simulate and to verify the proposed method.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.5
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• pp.447-456
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• 2019

In the early stage of ship design, the rapid prediction of resistance of hull forms is required. Although there are more accurate prediction methods such as model test and CFD analysis, statistical methods are still widely used because of their cost-effectiveness and quickness in producing the results. This study suggests the prediction formula for the residual resistance coefficient (Cr) of the low-speed full ships. The formula was derived from the statistical analysis of model test results in KRISO database. In order to improve prediction accuracy, the local variables of hull forms are defined and used for the regression process. The regression formula for these variables using only principal dimensions of hull forms are also provided.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.543-551
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• 2015

In this paper, Used on the bridge and ship, investigate the physical relationship of aluminium plate girders(A6082-T6) considering the marine environment. Plate girder will experience the patch loading such as moving load, surcharge in the product life cycle. The ultimate strength of aluminum plate girders subjected to these loads applied multiple numerical model and performed the elasto-plastic large deflection series analysis and was proposed the predicted formula for regression analysis. The predicted formula was shown by the relationship of ultimate strength and slenderness. If the slenderness is low(0-2.3), it causes a 9 % error, and If the slenderness is higher(2.3-4.0), it causes a 1-2 % error. Therefore, the propriety of proposed prediction formular was found to be assess rationally.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1243-1258
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• 2012

The objective of this study is to develop the relational formula to estimate the streamflow depletion due to groundwater pumping near stream, which has been statistically derived by using the simulated data. The integrated surface water and groundwater model, SWAT-MODFLOW was applied to the Sinduncheon and Juksancheon watersheds to obtain the streamflow depletion data under various pumping conditions. Through the multiple regression analyses for the simulated streamflow depletion data, the relational formula between the streamflow depletion rate and various factors such as pumping rate, distance between well and stream, hydraulic properties in/near stream, amount of rainfall was obtained. The derived relational formula is easy to apply for assessing the effects of groundwater pumping on near stream, and is expected to be a tool for estimate the streamflow contribution to the pumped water.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.6
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• pp.729-736
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• 2015

In this paper, It was performed to optimize for the deck's structural design of a double ended car ferry ship respect to Goal-Driven Optimization (GDO). It was examined for the strength and deformation of the deck and determined to save economic cost the optimal point. The deck thickness based on the Design of Experiments (DOE) and response surface method was increased to 110%. and can improve the deck's strength and stiffness. By performing the regression analysis respect to the result, we propose the optimal regression model formula as a third degree polynomial regression models. The coefficient of determination $R^2$ was about 0.98 and reliability could be obtained.

• Water for future
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• v.24 no.3
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• pp.71-82
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• 1991

Methods of predicting water resources availiability of a river basin can be classified as empirical formula, water budget analysis and regression analysis. The purpose of this study is to develop a method to estimate the monthly runoff required for long-term water resources development project. Using the monthly runoff data series at gaging stations alternative multiple regression models were constructed and evaluated. Monthly runoff volume along with the meteorological and physiographic parameters of 48 gaging stations are used, those of 43 stations to construct the model and the remaining 5 stations to verify the model. Regression models are named to be Model-1, Model-2, Model-3 and Model-4 developing on the way of data processing for the multiple regressions. From the verification, Model-2 is found to be the best-fit model. A comparison of the selected regression model with the Kajiyama's formula is made based on the predicted monthly and annual runoff of the 5 watersheds. The result showed that the present model is fairly resonable and convinient to apply in practice.

• Journal of Aerospace System Engineering
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• v.2 no.4
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• pp.1-6
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• 2008

In generally, the regression rate was expressed with average value and oxidizer mass flux in hybrid propulsion system. This can not represent the local value of regression rate along with oxidizer flow direction. In this study, experimental studies were performed with Separation method and Cutting method for measure local regression rate. In axial injection, the local regression rate decreases rapidly with axial location near entrance and increases with axial direction from the leading edge and the empirical formula for local regression rate with function of oxidizer mass flux and location was derived. Swirl injection regression rate has higher value at the leading edge of the fuel and comparatively uniform regression rate at the downstream. Overall regression rate of swirl injection is higher increased about 54 % than regression rate of axial injection.

• Journal of Korea Technology Innovation Society
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• v.5 no.2
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• pp.141-151
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• 2002

Many technology investment projects can be considered as set of sequential options. A compound real option can be used for evaluating sequential technology investment decisions under significant uncertainty and measuring its value. In this paper, the formula developed by Geske and Johnson(1984) and Buraschi and Dumas(2001) was applied to evaluate the technology investment with related double real option. Also double real option was com-pared with net present value method and multiple linear regression model was used to assess the partial effects of risk free rate and log-term volatility on its value.

• Journal of the Korean Society of Safety
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• v.20 no.1 s.69
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• pp.113-118
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• 2005

This study delivers the actual condition of investment for industrial accident prevention based on survey of 500 construction sites from 'reports far industry safety and health' published by Korea Occupational Safety & Health Agency (KOSHA). The various research techniques were used such as technical statistic analysis for construction industry, cost comparison of industrial accident prevention and accident loss. A formula was deduced to calculate accident loss and accident frequency by accident prevention cost through regression analysis.