• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.25 no.2
• /
• pp.112-121
• /
• 2013

The calibration of resistance factor in reliability theory for limit state design of gravel compaction piles (GCP) requires a reliable estimate of ultimate bearing capacity. The static load test is commonly used in geotechnical engineering practice to predict the ultimate bearing capacity. Many graphical methods are specified in the design standard to define the ultimate bearing capacity based on the load-settlement curve. However, it has some disadvantages to ensure reliability to obtain an uniform ultimate load depend on engineering judgement. In this study, a well-fitting nonlinear regression model is proposed to estimate the ultimate bearing capacity, for which a nonlinear regression analysis is applied to estimate the ultimate bearing capacity of GCP and the results are compared with those calculated using previous graphical method. Affect the resistance factor of the estimate method were analyzed. To provide a database in the development of limit state design, the load test conditions for predicting the ultimate bearing capacity from static load test are examined.

• Journal of Korea Water Resources Association
• /
• v.50 no.9
• /
• pp.627-635
• /
• 2017

It is required to estimate reliable design floods for hydraulic structures in order to respond more effectively to recent climate change. In this study, differences of design floods that were estimated the flood frequency analysis (FFA) and the design rainfall-runoff analysis (DRRA) were analyzed. In Korea, due to lack of measured flood data, the DRRA method is used in practice to determine the design floods. However, assuming the design floods estimated by the FFA as true values, the DRRA method over estimated the design floods by 79%. Thus, this study proposed a practical method to estimated design flood in ungauaged watersheds through regressive adjustment of flood quantiles estimated from the DRRA method. To this end, after investigating the differences between design floods acquired from the FFA and the DRRA method, nonlinear regression analyses were performed to develop the adjustment formulas for 8 large-dam watersheds. Applying the adjustment formula, the accuracy was improved by 65.0% on average over the DRRA method. In addition, when considering the watershed size, the adjustment formula increases the accuracy by 2.1%p on average over when not considering the watershed size.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.477-482
• /
• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
• /
• 2003.04a
• /
• pp.123-123
• /
• 2003

야채 soup mix의 제조조건이 야채죽의 물리적 및 관능적 특성에 대한 영향을 조사하였다. Soup mix의 제조조건인 주입액의 양 (쌀 무게에 대한 물의 체적 비), 버섯, 당근 그리고 대파의 첨가량(쌀 무게에 대한 잣의 무게 비)에 따른 물리적 특성과 관능적 특성의 변화를 모니터링 하고자 반응 표면분석법 (response surface methodology, RSM)을 사용하였으며, 이때 실험계획은 중심합성계획법을 적용하였다. 요인변수(Xn)를 중심합성계획에 따라 17실험구로 구분하여 조리실험을 실시하였고, 이들 요인변수에 의해 영향을 받는 반응변수(Yn)는 야채죽의 물리적 특성으로 하여 회귀분석에 사용하였다. 회귀분석에 의한 모델식의 예측에는 SAS (statistical analysis system) program을 사용하였으며, 야채죽의 조리조건이 물리적 및 관능적 특성에 미치는 영향은 SAS program을 이용한 3차원 반응표면분석법으로 해석하였다. 야채의 배합비를 달리한 야채죽의 물리적 특성인 색도 L, a 및 b값에 대한 F-value는 자각 1.50, 11.75 및 5.58이고,유의수준이 각각 0.3044, 0.0019와 0.0169로서 a값과 b값의 유의성이 1% 수준에서 인정되어 이들 제조조건이 a값과 b값에 큰 영향을 미치는 것으로 나타났다. 점도와 퍼짐성에 대한 유의수준은 각각 0.6920과 0.7528이고, 반응표면회귀식의 $R^2$은 각각 0.4766과 0.4436으로 유의성이 인정되지 않았다. 고형분에 대한 유의수준은 0.2026이고 회귀식의 $R^2$는 0.7107으로서 버섯, 당근 및 대파 첨가량이 고형분의 변화에 영향을 미치지 않은 것을 알 수 있었다. 관능적 특성인 색상에 대하여 soup mix의 제조조건이 야채죽에 미치는 영향은 F-value는 6.23이고, 유의수준이 0.0124으로서 1%수준에서 유의성이 인정되었으며, 회귀식의 $R^2$은 0.8890이다. 향에 대한 유의확률은 0.4555이고 0.05이상이므로 유의성이 인정되지 않아 설정된 범위내에서 야채죽의 향에 대하여 크게 영향을 미치지 않는 것으로 나타났다. 점성에 대한 반응표면회귀식의 $R^2$은 0.8134로서 그 유의성이 5%수준에서 인정되었다. 맛과 전반적 기호도에 대하여 야채의 배합비에 따른 반응표면회귀식의 $R^2$은 각각 0.7374와 0.8651이며, 유의화률은 0.1578과 0.0228으로 나타나 전반적인 기호도에 대한 영향은 10% 유의수준에서 영향을 주었다. 결론적으로, 물리적 특성인 색도, 점성, 퍼짐성과 고형분의 함량은 관능적 특성인 색과 비교적 높은 정(正)의 상관을 나타내었으며, 관능적 특성인 향과의 상관은 유의성이 인정되지 않았다. 야채죽 제조를 위한 soup mix의 제조조건에 있어서 야채의 배합비는 색과 점성에 영향을 미치는 가장 주요한 조건이라고 생각된다.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.24 no.3
• /
• pp.299-310
• /
• 2008

In this study, the regression analysis method was tested for the estimation of peak stress at stress concentration area in the cervical bone. Submerge type EZ plus implant (Megagen. Daegu, Korea), 4.1 mm in cervical diameter and 9.6 mm in endosseous length, were axisymmetrically modelled together with surrounding alveolar bone of which the width was 10 mm. Vertical force of 100 N was applied to a head of crown above 8.5 mm from the outer surface of the cortical bone. Four different mesh models were composed of differently sized elements in vicinity of sharp corners, and they include 6 stress monitoring points that are located in the same geometrical points regardless of the differences in the meshes. Primary consideration was given to the stresses in the cortical bone surrounding the implant neck. The results showed that virtually all the stresses were concentrated in the cortical bone regardless of mesh designs. The peak stresses were successfully calculated by a regression analysis in a stable manner, as far as the mesh is designed to represent the acute gradient of stresses near the sharp corner.

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

• Journal of Korea Society of Industrial Information Systems
• /
• v.20 no.5
• /
• pp.1-11
• /
• 2015

This paper is focused on how the trajectory of launch vehicle could be optimally estimated by the quadratic regression of trajectory data mining for the operation of telemetry ground system in NARO space center during real-time. To receive the telemetry data, the telemetry ground system has to track the space launch vehicle without tracking loss, and it is possible by the well-designed algorithm to estimate a flight position in real-time. For this reason, the quadratic regression model instead of interpolation was considered to estimate the exact position data of launch vehicle and the improvement of antenna performance. For analysis, the real trajectory data which had been logged during NARO 1st launch mission were used, the estimation result of launcher current position was analyzed by the mathematical modeling. In conclusion, the algorithm using quadratic regression based on trajectory data mining showed the better performance than previous interpolation algorithm to estimate the next flight position and the antenna driving performance.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2002.05a
• /
• pp.541-546
• /
• 2002

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2002.11a
• /
• pp.25-58
• /
• 2002

• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.19-33
• /
• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.