• Journal of the Korean Society for Railway
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• v.10 no.6
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• pp.766-771
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• 2007

In this research, we were going to make the function which can forecast the operating cost of metropolitan railroad that is performing a role of assistant highway within the city. In order to do this, based on service records of subway line 1st to 8th in Seoul, we extracted 23 variables which can affect to the operating cost, and we selected the final variable for estimate the function of operating cost from correlation among variables and influence analysis. Then, we performed regression analysis by stages using final variable. 6 independent variables are chosen for presuming the operating cost, and we obtained the final 3 variables (quantity of holding motor cars, peak quantity of possessed motor cars, and quantity of stations) as a result of regression analysis. Through this research, function of operating cost of metropolitan railroad has better applicability than existing preliminary validity, and it is used by further preliminary validity investigation and master plan or validity investigation which is accompanied by operation designing, thus we expect that it could make a great contribution to the priority order of investment for metropolitan railroad or process of policy decision.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.569-578
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• 2020

By estimating conditional quantile functions of the response, quantile regression (QR) can provide comprehensive information of the relationship between the response and the predictors. In addition, kernel quantile regression (KQR) estimates a nonlinear conditional quantile function in reproducing kernel Hilbert spaces generated by a positive definite kernel function. However, it is infeasible to use the KQR in analysing a massive data due to the limitations of computer primary memory. We propose a divide and conquer based KQR (DC-KQR) method to overcome such a limitation. The proposed DC-KQR divides the entire data into a few subsets, then applies the KQR onto each subsets and derives a final estimator by aggregating all results from subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Fire Science and Engineering
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• v.28 no.2
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• pp.69-75
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• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.760-768
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• 2017

The transition temperature shift (TTS) of the reactor pressure vessel materials is an important factor that determines the lifetime of a nuclear power plant. The prediction of the TTS at the end of a plant's lifespan is calculated based on the equation of Regulatory Guide 1.99 revision 2 (RG1.99/2) from the US. The fluence factor in the equation was expressed as a power function, and the exponent value was determined by the early surveillance data in the US. Recently, an advanced approach to estimate the TTS was proposed in various countries for nuclear power plants, and Korea is considering the development of a new TTS model. In this study, the TTS trend of the Korean surveillance test results was analyzed using a nonlinear regression model and a mixed-effect model based on the power function. The nonlinear regression model yielded a similar exponent as the power function in the fluence compared with RG1.99/2. The mixed-effect model had a higher value of the exponent and showed superior goodness of fit compared with the nonlinear regression model. Compared with RG1.99/2 and RG1.99/3, the mixed-effect model provided a more accurate prediction of the TTS.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.481-488
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• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of Korean Academy of Nursing
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• v.45 no.5
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• pp.733-741
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• 2015

Purpose: This study was a descriptive survey research to identify the impact of bowel function, anxiety and depression on quality of life in patients with rectal cancer who had a sphincter-preserving resection. Methods: Participants were 100 patients who had rectal cancer surgery at W hospital in Korea. Bowel function, anxiety & depression, and quality of life were measured using the BFI (Bowel Function Instrument), HADS (Hospital Anxiety-Depression Scale) and the FACT-C (Functional Assessment of Cancer Therapy-Colorectal). Results: The mean scores were $39.81{\pm}5.16$ for bowel function, $6.15{\pm}3.25$ for anxiety, $7.24{\pm}3.13$ for depression, and $72.50{\pm}13.27$ for quality of life. There were significant negative correlations between quality of life and anxiety (r= -.59, p <.001) and between quality of life and depression (r= -.53, p <.001). But the correlation between quality of life and bowel function was significantly positive (r=.22, p =.025). The influence of the independent variables on the total quality of life was examined using multiple regression analysis. Anxiety (${\beta}$= -.38, p =.002), bowel function (${\beta}$= -.25, p =.028) and occupation (${\beta}$=.16, p =.048) were identified as factors affecting quality of life. The explanation power of this regression model was 44% and it was statistically significant (F=16.53, p <.001). Conclusion: The results of this study indicate that in order to improve the bowel function of patients after sphincter-preserving resection for rectal cancer, effective nursing interventions should be developed. As psychological problem such as anxiety and depression can relate to quality of life for these patients, nurses should work on improving the situation by providing continuous emotional nursing.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.111-120
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• 2016

In usual, the discontinuous variance function was estimated nonparametrically using a kernel type estimator with data sets split by an estimated location of the change point. Kang et al. (2000) proposed the Gasser-$M{\ddot{u}}ller$ type kernel estimator of the discontinuous regression function using the adjusted observations of response variable by the estimated jump size of the change point in $M{\ddot{u}}ller$ (1992). The adjusted observations might be a random sample coming from a continuous regression function. In this paper, we estimate the variance function using the Nadaraya-Watson kernel type estimator using the adjusted squared residuals by the estimated location of the change point in the discontinuous variance function like Kang et al. (2000) did. The rate of convergence of integrated squared error of the proposed variance estimator is derived and numerical work demonstrates the improved performance of the method over the exist one with simulated examples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.179-191
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• 2004

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Phonetics and Speech Sciences
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• v.5 no.1
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• pp.91-98
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• 2013

This paper investigated Korean prosody as it relates to graded internal structure in an identification function. Within Korean prosody, variants regarded as dialectal variations can appear as different prosodic scales, which contain the range of within-category variations. The current experiment was intended to show how the prosodic scale corresponding to the range of within-category differences relates to f0 contours for speakers of two Korean dialects, North Kyungsang and South Cholla. In an identification task, participants responded by selecting an item from two answer choices. The probability of choosing the correct response from the two choices was computed by a logistic regression analysis using intercepts and slopes. That is, the correct response between two choices was used to show a linear line with an s-shape presentation. In this paper, to investigate the graded internal structure of labeling, 25%, 50%, and 75% of predicted probability were assessed. Listeners from North Kyungsang showed progressive variations, whereas listeners from South Cholla revealed random patterns in the internal structure of the identification function. In this paper, the results were plotted using scatterplot graphs, applying the range of within-category variation and predicted probability obtained from the logistic regression analyses. The scatterplot graphs showed the different degree of the responses for f0 scales (i.e., variations within categories). The results demonstrate that the gradient structures of native pitch accent users become more progressive in response to f0 scales.