• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.1
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• pp.22-32
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• 2007

This paper describes a study of a helicopter database for the sizing stage of a preliminary design process. The database includes specifications and performance parameters for more than 150 conventional single rotor helicopters currently in market. Design parameters, including configuration and weight parameters, have been analyzed and trend curve equations(regression equations) are derived using the regression analysis method. Finally, the applicability of this research result was verified whether the method is reliable for being adopted as a useful design tool in the early stage of a helicopter design process.

• Journal of Power System Engineering
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• v.19 no.2
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• pp.70-77
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• 2015

Nowadays, car manufacturers applied many high strength steels such AHSS or UHSS to car bodies for weight lightening. Therefore, a variety of applied steel sheet to car bodies increased and the needs of simulation to evaluate weldability also increased in order to reduce the cost and time. In this study, resistance spot welding simulations for DP 780 Steel with 1.0 and 1.4 mm thickness were conducted with respect to lobe curve. 2 regression models to estimate tensile shear strength were suggested and they were second order polynomial regression model and optimized second order regression model. The performance of these models was evaluated in terms of the coefficient of determinant and average error rate.

• Corrosion Science and Technology
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• v.19 no.6
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• pp.302-309
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• 2020

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.

• IE interfaces
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• v.23 no.3
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• pp.205-212
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• 2010

The learning curve model is a mathematical form which represents the relationship between the manufacturing experience and its effectiveness. The semiconductor manufacturing is widely known as an appropriate example for the learning effect due to its complicated manufacturing processes. In this paper, I propose a new compound learning curve model for semiconductor products in which the general learning curve model and the growth curve are composed. The dependent variable and the effective independent variables of the model were abstracted from the existing learning curve models and selected according to multiple regression processes. The simulation results using the historical DRAM data show that the proposed compound learning curve model is one of adequate models for describing learning effect of semiconductor products.

• Fashion & Textile Research Journal
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• v.13 no.5
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• pp.661-671
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• 2011

This study was designed to produce rounded belt pattern and tight-skirt pattern drafting method using 3D body scan data. Subjects were thirty women in their early twenties. In order to figure out the optimum cutting points, namely, where darts are made, using CAD program, curve ratio inflection points on the horizontal curve of waist, abdomen, and hip to find 1 point in the front, two points in the back part. The average length from center front point to maximum curve ratio was 7.7 cm(46.3%) on the waist curve; 7.9 cm(39.4%) on the abdomen curve. And the average length from center back point to maximum curve ratio point was 6.9 cm(39.0%) for first dart and 11.2 cm(63.3%) for second dart on the waist curve; 8.9 cm(35.8%) for first dart and 15.7 cm(63.3%) for second dart on the hip curve respectively. The cutting lines from were made up by connecting curve inflection points. After divided using cutting lines, each patch was flattened onto the plane and all the technical design factors related with patternmaking were measured, such as dart amount, lifting amount of side waist point, etc. Based on the results of correlation analysis among these factors, regression analysis was done to produce equations to estimate the variables necessary to draw up pattern draft method; F1=F8+1.1, $F4=2.5{\times}F2+0.9$, $F5=0.9{\times}F4+1.0$, $F6=0.3{\times}F4+0.4$, $B1=0.9{\times}B8+2.3$, $B4=2.1{\times}B2+1.3$, $B5=0.9{\times}B4+3.5$, and $B6=0.3{\times}B4+0.4$.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.173-182
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• 2014

PURPOSES : The purpose of the study is to a) explore the operating speed of trucks on rural highways affected by road geometry, and thereby b) develop a predictive model for the operating speed of trucks on rural highways. METHODS : Considering that most of the existing studies have focused on cars, the current study aimed to predict the operating speed of trucks by conducting linear regression analysis on the speed data of trucks operating on the linear-curved-linear portions of the road as a single set. RESULTS : The operating speed in the plane curve portion increased with the length of the curve, and decreased with a lower vertical grade and a smaller curve radius. In the straight plane portion, the operating speed increased with a larger curve radius(upstream), and decreased with an increase in the change of the vertical grade, depending on the length of the vertical curve. CONCLUSIONS : This study developed estimation models of truck for operational speed and evaluated the degree of safety for horizontal and vertical alignments simultaneous. In order to represent whole area of the rural highway, the models should be ew-analyzed with vast data related with road alignment factor in the near future.

• Journal of Korean Society of Forest Science
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• v.83 no.4
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• pp.521-530
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• 1994

Voume functions, which are usually expressed by the function of dbh and height, are estimated commonly through the regression analysis with the highest statistical accuracy considered. In Korea, general volume functions for each tree species were prepared by means of the regression analysis with the exponential function ($V=aD^bH^c$) having the dbh(D) and height(H) as independent variables. In this study, regional stem curve functions for the Pinus densiflora in Kangwon-province were derived and a regional volume function model, in which the stem volume can be directly estimated through the rotational integral of the regional stem curve functions, was prepared. The regional volume estimated by the prepared model was more accurate than the volume by the general volume table for the Pinus densiflora in Kangwon-province. Additionary, the form of stem curves derived by the regional stem curve functions showed difference from each other. The stem in Youngwol and Wonju taper down more fast in upper part than that in other regions. These various stem forms also led to the regional difference in volume estimates.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.189-201
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• 2016

In survival analysis, the Nelson-Aalen estimator and Peterson estimator are often used to estimate a cumulative hazard function in randomly right censored data. In this paper, we suggested the smoothing version of the cumulative hazard function estimators using a Bezier curve. We compare them with the existing estimators including a kernel smooth version of the Nelson-Aalen estimator and the Peterson estimator in the sense of mean integrated square error to show through numerical studies that the proposed estimators are better than existing ones. Further, we applied our method to the Cox regression where covariates are used as predictors and suggested a survival function estimation at a given covariate.

• Korean Journal of Veterinary Research
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• v.50 no.1
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• pp.11-17
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• 2010

Linear and non-linear regressions were used to derive the calibration function for the measurement of roxithromycin plasma concentration. Their results were compared with weighted least squares regression by usual weight factors. In this paper the performance of a non-linear calibration equation with the capacity to account empirically for the curvature, y = ax$^{b}$ + c (b $\neq$ 1) is compared with the commonly used linear equation, y = ax + b, as well as the quadratic equation, y = ax$^{2}$+ bx + c. In the calibration curve (range of 0.01 to 10 ${\mu}g/mL$) of roxithromycin, both heteroscedasticity and nonlinearity were present therefore linear least squares regression methods could result in large errors in the determination of roxithromycin concentration. By the non-linear and weighted least squares regression, the accuracy of the analytical method was improved at the lower end of the calibration curve. This study suggests that the non-linear calibration equation should be considered when a curve is required to be fitted to low dose calibration data which exhibit slight curvature.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.679-683
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• 2005

The technique which connects Generalized Regression Neural Networks Model(GRNNM) with Genetic Algorithm (CA) is used to derive rating curve in the river basin. GRNNM architecture consists of 4 layers ; input, hidden, summation and output layer. GA method is applied to estimate the optimal smoothing factor when GRNNM is trained. The derivation of rating curve using GRNNM is considered different kinds of hydraulic characteristics such as water stage, area and mean velocity and is applied two stage stations; Sunsan and Jungam. Furthermore, it is compared with conventional curve-fitting method. Through the training and validation performance, the results show that GRNNM is much superior as compared to the conventional curve-fitting method.