• 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.

• International Journal of Concrete Structures and Materials
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• v.4 no.1
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• pp.63-68
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• 2010

With the emergence of ultra-high strength of concrete, the compression lap splice has become an important area of interest. According to ACI 318-08, a compression splice can be longer than a tension splice when high-strength concrete is used. By reevaluating the test results of compression splices and performing regression analysis, a simplified design equation for splice length in compression was developed based on the basic form of design equations for development/splice lengths of deformed bars and hooks in tension. A simple linear relation between $l_s/d_b$ and $f_{sc}\sqrt{f'_c}$ was assumed, and yields good values for the correlation coefficient and the mean and the COV (coefficient of variation) of the ratios of tests to predictions of splice strengths in compression. By including the 5% fractile coefficient of 0.83, a design equation for splice length in compression was developed. The splice length calculated using the proposed equation has a reliability that is equivalent to other provisions for reinforcing bars.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.1
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• pp.106-116
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• 1989

This study aims at scrutinizing the long4errn consolidation characteristics of peats sampled at three different regions of Chonbuk province. The standard consolidation test and the single load consolidation test were performed about these samples and especially in case of the latter the loading period was 350 days. The main condusions analyzed are as follows. 1. Void ratio showed much greater values than that of the general clay and was decresed greatly according to the increase of the load. 2. In case of the relationship between the sefflement and the long-term settlement time the rate of settlement increment became great according to the increase of the load step and the long4erm settlement became linely proportional to the logarithm of time alter 10 minutes. 3. The linear correlation was showed between the long4erm settlement time and the void ratio and therefore equations by regression analysis were derived in order to estimate the long-term settlement The slope of straight lines increased according th the increase of the load step and secondary consolidation coefficients ranged from 0.04-0.27. 4. The secondary consolidation coeffcient became linealy proportional to the compression index and the ratio of Ca to CC was 0.072. 5. The period required in ending the primary consolidation was about 10 minutes and alter that the secondary consolidation coefficient appeared to have constant value. Therefore the secondary consolidation coefficient was judged to be used as a significant factor in estimating the long4erm settlement. 6. In case of the single load consolidation test the secondary consolidation coefficient showed the tendancy increasing according to the increase of the consolidation pressure.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.2
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• pp.87-93
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• 2009

The labyrinth weir can be defined that the plane shape of overflow part is not straight line and is a kind of weir having overflow length increased by changing its plane shape. Recently, the labyrinth weir can be widely applied to various hydraulic facilities such as dam spillway, irrigation facilities, and canal structures by increasing precipitation. This study was performed to analyze the hydraulic characteristics according to triangle labyrinth weir using hydraulic model experiments and finally estimate the discharge coefficients for triangle labyrinth weirs. The formulae of discharge coefficient provided in this study, which make it feasible to calculate the overflow rate by a coefficient of correlation. sum of residuals, MAPE(Mean Absolute Percentage Error), are expected to be widely applied to design of hydraulic facilities such as dam spillway and irrigation system.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.

• Korean Journal of Soil Science and Fertilizer
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• v.31 no.4
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• pp.359-367
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• 1998

In order to determine the nitrogen supplying capabilities (NSC) of corn fields, 47 field experiments were performed in Pennsylvania over 3 year from 1986 and NSCs were estimated by the regression analysis with chemical properties and soil attributes. Although the content of $NO_3-N$ in soil showed the best correlation with NSC ($R^2=0.518$), the standardized partial regression coefficient of $NO_3-N$ for NSC was 0.52, with some variations over the years. This value was slightly higher than those of the other properties which ranged from 0.001 to 0.351. Multiple linear regression with soil attributes for the evaluation of NSC was better than simple regression with $NO_3-N$. The coefficient of determination ($R^2$) for the evaluation of NSC was gradually increased; 0.599 with selected chemical properties, 0.698 with quantitative attributes(chemical properties and depth of Ap horizon), and 0.839 with quantitative and selected qualitative soil attributes. Consequently, in order to evaluate NSC, analysis by multiple linear regression with soil attributes was more reliable and better model than by the simple regression model.

• Restorative Dentistry and Endodontics
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• v.38 no.3
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• pp.146-153
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• 2013

Objectives: Aluminum step wedge (ASW) equivalent radiodensity (eRD) has been used to quantify restorative material's radiodensity. The aim of this study was to evaluate the effects of image acquisition control (IAC) of a digital X-ray system on the radiodensity quantification under different exposure time settings. Materials and Methods: Three 1-mm thick restorative material samples with various opacities were prepared. Samples were radiographed alongside an ASW using one of three digital radiographic modes (linear mapping (L), nonlinear mapping (N), and nonlinear mapping and automatic exposure control activated (E)) under 3 exposure time settings (underexposure, normal-exposure, and overexposure). The ASW eRD of restorative materials, attenuation coefficients and contrasts of ASW, and the correlation coefficient of linear relationship between logarithms of gray-scale value and thicknesses of ASW were compared under 9 conditions. Results: The ASW eRD measurements of restorative materials by three digital radiographic modes were statistically different (p = 0.049) but clinically similar. The relationship between logarithms of background corrected grey scale value and thickness of ASW was highly linear but attenuation coefficients and contrasts varied significantly among 3 radiographic modes. Varying exposure times did not affect ASW eRD significantly. Conclusions: Even though different digital radiographic modes induced large variation on attenuation of coefficient and contrast of ASW, E mode improved diagnostic quality of the image significantly under the underexposure condition by improving contrasts, while maintaining ASW eRDs of restorative materials similar. Under the condition of this study, underexposure time may be acceptable clinically with digital X-ray system using automatic gain control that reduces radiation exposure for patient.

• Bulletin of the Korean Chemical Society
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• v.28 no.10
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• pp.1689-1696
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• 2007

Equilibrium molecular dynamics (EMD) simulations are used to evaluate the transport coefficients of argonkrypton mixtures at two liquid states (state A: 94.4 K and 1 atm; state B: 135 K and 39.5 atm) via modified Green-Kubo formulas. The composition dependency of the volume at state A obeys close to the linear model for ideal liquid mixture, while that at state B differs from the linear model probably due to the high pressure. The radial distribution functions for the Ar-Kr mixture (x = 2/3) show a mixing effect: the first peak of g11 is higher than that of g(r) for pure Ar and the first peak of g22 is lower than that of g(r) for pure Kr. An exponential model of engineering correlation for diffusion coefficient (D) and shear viscosity (η) is superior to the simple linear model for ideal liquid mixtures. All three components of thermal conductivity (λpm, λtm, and λti) at state A and hence the total thermal conductivity decrease with the increase of x. At state B, the change in λtm is dominant over those in λpm and λti, and hence the total thermal conductivity decrease with the increase of x.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.561-570
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• 1998

The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.