• 한국기계가공학회지
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• 제3권4호
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• pp.37-42
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• 2004

End-milling have been widely used in aircraft, automobile part and moulding industry. However, various working factors such as spindle speed, feed rate and depth of cut in end-milling have an effect on spindle vibration. There it is demanded the quantitative analysis of spindle vibration in order to get the optimum surface roughness. This study was carried out to analyze an influence of working factors on spindle vibration by design of Experiment. The results are shown that mathematical model of regression equation for an influence of working factors on vibration acceleration of spindle in end-milling by regression analysis is presented.

• 한국농공학회지
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• 제43권4호
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• pp.97-105
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• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• 한국농공학회논문집
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• 제59권4호
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• 산업경영시스템학회지
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• 제19권40호
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• 농업과학연구
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• 제38권3호
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• pp.571-581
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• 2011

This study develops a mid-term preceding observation model of radish to complement an existing short-term agricultural observation model. The first purpose of the study is to extend a three seasonal classification(spring, summer, fall) of fruit-vegetables to a four seasonal classification that involves the winter additionally. This allows us to verify the reason for demand and supply unbalance and unstable price of radish. The second purpose is to construct a mid-term preceding observation model that would be used to forecast planted areas, output, monthly shipment and price. To achieve these purposes, several multiple regression models are estimated. A system is consisted of a planted areas equation, a yield equation, monthly shipment distribution equation, and monthly price equation. To calculate output an auxiliary equation is involved in the system and the consumer price index etc are considered as exogenous variables.

• 천문학회보
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• 제45권1호
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• pp.69.1-69.1
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• 2020

While Dark Energy is one of the explanations for the accelerating expansion of the Universe, its nature remains a mystery. The standard (flat) ΛCDM model is consistent with cosmological observations: type Ia Supernova, BAO, CMB, and so on. However, the analysis of observations assuming a model, model-dependent approach, is likely to bias the results towards the assumed model. In this poster, I will introduce model-independent approach with Gaussian process and the application of Gaussian process regression to reconstruct the equation of state of dark energy.

• 응용통계연구
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• 제29권1호
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• pp.75-83
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• 2016

본 연구에서는 금융시장 통합화에 따른 금융 시장 전염을 생물학적 전염개념에 기초하여 분석하는 검정 방법론을 제시하였다. 금융 시장 통합화를 측정하기 위하여 U-통계량을 사용하였고, 금융 시장 전염 검정을 위하여 단일방정식 오차수정 모형을 중심으로 잠재 요인모형, 분위수 회귀모형과 런검정을 사용하였다. 시뮬레이션결과 단일방정식 오차수정 모형이 자기상관을 갖는 오차항을 포함한 선형 회귀모형에서 비교적 높은 수준의 적합도를 일관성 있게 보여 주고 있다.

• 한국도로학회논문집
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• 제15권3호
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• pp.45-52
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• 2013

PURPOSES : This study is to construct the regression models of drainage asphalt concrete specimens and to provide the appropriate coefficients of hydraulic conductivity prediction models. METHODS: In terms of easy calculation of the hydraulic conductivity from porosity of asphalt concrete pavement, the estimation model of hydraulic conductivity was proposed using regression analysis. 10 specimens of drainage asphalt concrete pavement were made for measurement of the hydraulic conductivity. Hydraulic conductivity model proposed in this study was calculated by empirical model based on porosity and the grain size. In this study, it shows the compared results from permeability measured test and empirical equation, and the suitability of proposed model, using regression analysis. RESULTS: As the result of the regression analysis, the hydraulic conductivity calculated from the proposal model was similar to that resulted from permeability measured test. Also result of RMSE (Root Mean Square Error) analysis, a proposed regression model is resulted in more accurate model. CONCLUSIONS: The proposed model can be used in case of estimating the hydraulic conductivity at drainage asphalt concrete pavements in fields.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.132-134
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• 2009

Hemorrhagic shock is a common cause of death in emergency rooms. Since the symptoms of hemorrhagic shock occur after shock has considerably progressed, it is difficult to diagnose shock early. The purpose of this study was to improve early diagnosis of hemorrhagic shock using a survival prediction model in rats. We measured ECG, blood pressure, respiration and temperature in 45 Sprague-Dawley rats, and then obtained a logistic regression equation predicting survival rates. Area under the ROC curves was 0.99. The Hosmer-Lemeshow goodness-of-fit chi-square was 0.86(degree of freedom=8, p=0.999). Applying the determined optimal boundary value of 0.25, the accuracy of survival prediction was 94.7%

• 한국물환경학회지
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• 제22권1호
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• pp.166-171
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• 2006

The objectives of the present paper were to investigate the concentration of total coliform in wastewater effluents and the effect of water chemical and physical characters in it. The most correlated parameter with total coliform was COD. It means that the wastewater treatment efficient effects on total coliform concentration. And we developed predictive model for the total coliform concentration. The estimated parameters for model were COD, temperature, nitrite, chloride, Mn and regression model equation was determined; log (Total Coli.) = 1.861+0.065[COD]+0.038[temperature]-0.0004[$Cl^-$]+3.697[Mn]-0.32 [$NO_2-N$] The developed model provided very strong correlation ($R^2:0.82$) between total coliform and regression equation. The parameters having high sensitivity were COD and temperature. So the study indicated that if the temperature and COD of wastewater effluent were known, we would estimate the concentration of total coliform and decide the most effective usage of chlorine.