• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.52-59
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• 2004

Recently the high speed end-milling processing is demanded the high-precise technique with good surface roughness and rapid time in aircraft, automobile part and molding industry. The working factors of high speed end-milling has an effect on surface roughness of cutting surface. Therefore this study was carried out to analyze the working factors to get the optimum surface roughness by design of experiment. From this study, surface roughness have an much effect according to priority on distance of cut, feed rate, revolution of spindle and depth of cut. By design of experiment, it is effectively represented shape characteristics of surface roughness in high speed end-milling. And determination($R^2$) coefficient of regression equation had a satisfactory reliability of 76.3% and regression equation of surface roughness is made by regression analysis.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.10a
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• pp.23-28
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• 1992

The ultimate capacity of end-plate connection is investigated through nonlinear finite element analysis. The example models are divided into stiffened case and unstiffened one. The refined finite element models are analyzed by utilizing a general purpose structural analysis computer program ADINA and the moment-rotation relationships of the connection are determined. The results are compared with the regression equation deduced by Krishnamurthy. It is planned to deduce a bilinear regression equation through a parametric study on various dimensions of the connection.

• Water for future
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• v.6 no.2
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• pp.6-11
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• 1973

This paper include the hydrometeorological analyses of evapotranspiration which is import factor concerning the estimate of water budgest over a certain basin. Evapotranspiration model mode by the multiple regression analysis between the evapotranspiration measured on various kinds of ground cover (water, bare soil and lawn) and the other meteorological elements affecting the evapotranspiration process, and the simple regression analysis between the evapo transpiration measured on each ground cover and the evapotranspiration on water and vegetables calculated from the Penman equation. It is expected that the evapotranspiration models are a very useful formulae estimating ten days amounts or a month's amounts.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.40-45
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• 2016

Interrupted cutting has different cutting characteristics compared with continuous cutting. In interrupted cutting, the workpiece has a groove that regularly impacts the cutting tool and workpiece. Therefore, tool damage occurs rapidly, and this increases the cutting force and surface roughness. In this study, we performed interrupted cutting of carbon steel for machine structure (SM45C) using a coated carbide tool (TT7100). To predict the cutting force, we analyzed the experimental results with a regression analysis. The results were as follows: We confirmed that the factors affecting the principal force and radial force were cutting speed, depth of cut, and feed rate. From the multi-regression analysis, we deduced regression equations, and their coefficients of determination were 89.6, 89.27, and 28.27 for the principal, radial, and feed forces, respectively. This means that the regression equations were significant for the principal and radial forces but not for the feed force.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.113-118
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• 2011

It is not easy to predict the shrinkage rate of a plastic injection mold in its design process. The shrinkage rate should be considered as one of the important performances to produce the reliable products. The shrinkage rate can be determined by using the CAE tools in the design produces. However, since the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. Therefore this study was carried out to presume for mutual relation of analysis condition to get the optimum average shrinkage by regression analysis. The results shown that coefficient of determination of regression equation has a fine reliability over 87% and regression equation of average shrinkage is made by regression analysis.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.49-58
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• 2017

PURPOSES : The objective of this study is to develop a simple regression model in designing the asphalt concrete (AC) overlay thickness using the Mechanistic-empirical pavement design guide (MEPDG) program. METHODS : To establish the AC overlay design equation, multiple regression analyses were performed based on the synthetic database for AC thickness design, which was generated using the MEPDG program. The climate in Seoul city, a modified Hirsh model for determining dynamic modulus of asphalt material, and a new damaged master curve approach were used in this study. Meanwhile, the proposed rutting model developed in Seoul city was then used to calibrate the rutting model in the MEPDG program. The AC overlay design equation is a function of the total AC thickness, the ratio of AC overlay thickness and existing AC thickness, the ratio of existing AC modulus and AC overlay modulus, the subgrade condition, and the annual average daily truck traffic (AADTT). RESULTS : The regression model was verified by comparing the predicted AC thickness, the AADTT from the model and the MEPDG. The regression model shows a correlation coefficient of 0.98 in determining the AC thickness and 0.97 in determining AADTT. In addition, the data in Seoul city was used to validate the regression model. The result shows that correlation coefficient between the predicted and measured AADTT is 0.64. This indicates that the current model is more accuracy than the previous study which showed a correlation coefficient of 0.427. CONCLUSIONS:The high correlation coefficient values indicate that the regression equations can predict the AC thickness accurately.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.775-784
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• 2022

In this study, to determine the optimal order of the full-logged I-D-F polynomial equation, which is mainly used to calculate the probable rainfall over a temporal rainfall duration, the probable rainfall was calculated and the regression coefficients of the full-logged I-D-F polynomial equation was estimated. The optimal variable of the polynomial equation for each station was selected using a stepwise selection method, and statistical significance tests were performed through ANOVA. Using these results, the statistically appropriately calculated rainfall intensity equation for each station was presented. As a result of analyzing the variable selection outputs of the full-logged I-D-F polynomial equation at 9 stations in Gyeongbuk, the 1^st to 3^rd order equations at 6 stations and the incomplete 3^rd order at 1 station were determined as the optimal equations. Since the 1^st order equation is similar to the Sherman type equation and the 2^nd order one is similar to the general type equation, it was presented as a unified form of rainfall intensity equation for convenience of use by increasing the number of independent variables. Therefore, it is judged that there is no statistical problem in considering only the 3^rd order polynomial regression equation for the full-logged I-D-F.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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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.

• Korean Journal of Applied Biomechanics
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• v.27 no.1
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• pp.53-58
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• 2017

Objective: The purpose of this study was to compare the accuracy of stride time and stride length provided by a commercial APDM inertial sensor system (APDM) with the results of three dimensional motion capture system (3D motion) during treadmill walking. Method: Five healthy men participated in this experiment. All subjects walked on the treadmill for 3 minutes at their preferred walking speed. The 3D motion and the APDM were simultaneously used for extracting gait variables such as stride time and stride length. Mean difference and root mean squared (RMS) difference were used to compare the measured gait variables from the two measurement devices. The regression equation derived from the range of motion of the lower limb was also applied to correct the error of stride length. Results: The stride time extracted from the APDM was almost the same as that from the 3D motion (the mean difference and RMS difference were less than 0.0001 sec and 0.0085 sec, respectively). For stride length, mean difference and RMS difference were less than 0.1141 m and 0.1254 m, respectively. However, after correction of the stride length error using the derived regression equation, the mean difference and the RMS difference decreased to 0.0134 m and 0.0556 m or less, respectively. Conclusion: In this study, we confirmed the possibility of using the temporal variables provided from the APDM during treadmill walking. By applying the regression equation derived only from the range of motion provided by the APDM, the error of the spatial variable could be reduced. Although further studies are needed with additional subjects and various walking speeds, these results may provide the basic data necessary for using APDM in treadmill walking.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.167-175
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• 2016

This study analyzed empirically the same data through SPSS statistic(regression analysis) and AMOS program(structural equation model) used for cause and effect analysis. The result of empirical analysis was as follows. The different outcome of coefficients and p-values were deducted. Especially, in the mediated effect testing, meanwhile, SPSS statistic(regression analysis) pictured mediated effect, AMOS program(structural equation model) did not picture mediated effect on the reject zone of null hypothesis(absolute t-value and C.R.-value were nearby 1.96). Eventually, this study showed that what program used determined the outcomes of coefficients and p-values(In particular, the outcomes were differentiated further in the increasing measurement error) though using the same data.