• 대한조선학회논문집
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• 제51권6호
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• pp.530-538
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• 2014

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of important measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model was suggested for FPSO. The weight estimation model using non-linear regression analysis was established by fixing independent variables based on this data and the multiple regression analysis was introduced into the weight estimation model. Its reliability was within 4% of error rate.

• 대한토목학회논문집
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• 제35권4호
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• pp.887-896
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• 2015

본 연구에서는 고립지역의 과거 AADT 자료와 사회 경제지표를 활용하여 장래 AADT를 추정하였다. 과거 교통량 추이 활용 시와 사회 경제지표 활용 시 장래 AADT를 추정했으며, 사회 경제지표를 활용하여 다중회귀 분석방식을 통한 장래 AADT 추정 시 높은 설명력과 낮은 오차율을 보였다. 지리적 특성별 AADT에 미치는 사회 경제지표 분석 결과 고립일반지역은 다양한 사회 경제지표가 AADT에 영향을 미쳤으며, 고립해안지역은 유류가격과 연관성을 보이는 것으로 나타났다. 고립지역의 장래 AADT 추정 모형은 $R^2$, MAPE 분석 시 우수한 것으로 나타났다. 이는 고립지역에서는 통과 교통량이 적고 교통량 변동이 적기 때문에 사회 경제지표를 활용한 장래 AADT 추정방식이 정확하다고 볼 수 있다.

• 한국수자원학회논문집
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• 제55권3호
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• pp.229-243
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• 2022

우리나라 11개 기상관측지역의 월별 기상자료가 증발접시계수에 미치는 영향을 분석하고, 증발접시계수 산정을 위한 4가지 형태의 다변량 선형회귀모형의 적용성을 검토하였다. 개발된 증발접시계수 산정모형의 적용성을 평가하기 위해서 기존에 다른 연구자들에 의해서 제안된 6가지의 모형과 비교 평가하였다. 우리나라 11개 기상관측지역에서 증발접시계수는 1, 2, 3, 7, 11, 12월은 기온에 가장 큰 영향을 받고, 다른 월들은 일사량에 가장 큰 영향을 받는 것으로 나타났다. 전반적으로 모든 월에서 풍속과 상대습도는 기온이나 일사량과 비교해서 증발접시계수에 큰 영향을 미치지 않는 것으로 나타났다. 모든 지역과 월에서 각 지역별로 5개의 독립변수(풍속, 상대습도, 기온, 일조시간과 가조시간의 비, 일사량)를 적용하여 유도된 모형이 가장 양호한 증발량 산정 결과를 보였다. 모형 검증결과에 의하면 다변량 선형회귀분석을 적용하여 증발접시계수를 산정하는 경우 일부 지역과 월에서 제한적으로 적용할 수 있을 것으로 판단된다.

• 한국산학기술학회논문지
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• 제13권1호
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• pp.45-50
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• 2012

본 논문은 CNC 선반에서 Al-6061의 보링 선삭 후 진원도 오차 분석에 관한 것이다. 실험은 실험계획법을 기초로 하여 신호대 잡음비, 분산분석을 사용하여 진원도 오차에 관한 파라미터의 영향을 연구하였다. 그리고 다중선형회귀분석은 진원도 오차의 예측 가능한 수학적 모델을 유도하기 위해 적용되었다. 선삭 파라미터인 고정압, 벽두께, 절삭깊이 그리고 이송속도는 논문의 목적달성을 위해 적용되었고 그 결과를 고찰하였다.

• Environmental Analysis Health and Toxicology
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• 제15권1_2호
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• pp.39-43
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• 2000

Linear alkyl sulfonates (LAS) constitute a large fraction of the surfactants used in cleaning processes in households, trade and industry Despite the industrial significance and the possible environmental impact of these compounds, the fast and inexpensive determination of LAS concentrations is still a difficult task. In this study, near infrared (NIR) spectroscopy which is a rapid spectroscopic analysis method compared with a traditional analytical method for the measurement of LAS concentration such as HPLC, GC and standard wet chemistry method. NIR spectra of LAS between 0.313 and 25.0% (w/v) in water were utilized to develop a calibration model. The best results (R ＝ 0.998, SEP ＝ 0.244% (w/v)) obtained by using partial least-squares regression with spectral data treatment and 2nd derivatization were comparable to the results (SEC ＝ 0.186% (w/v), SEP ＝ 0.206% (w/v)) obtained by using multiple linear least-squares regression (MLR). However, models based on derivative spectra have no significant advantage with MLR.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• 한국정밀공학회지
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• 제17권4호
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• pp.135-141
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• 2000

The major role of temperature sensors in thermal error compensation system of machine tools is improving machining accuracy by supplying reliable temperature data on the machine structure. This paper presents a new method for fault diagnosis of temperature sensors and recovery of faulted data to establish the reliability of thermal error compensation system. The detection of fault and its location is based on the correlation coefficients among temperature data from the sensors. The multiple linear regression model which is prepared using complete normal data is also used fur the recovery of faulted data. The effectiveness of this method was tested by comparing the computer simulation results and measured data in a CNC machining center.

• 소성∙가공
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• 제11권5호
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• pp.394-404
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• 2002

The drawbead model for a three-dimensional a finite element analysis of sheet metal forming processes is developed. The mathematical models of the basic drawbeads like circular drawbead, stepped drawbead, and squared drawbaed are first derived using the bending theory, belt-pulley equation, and Coulomb friction law. Next, the experiments for finding the drawing characteristics of the drawbead are performed. Based on mathematical models and drawing test results, expert models of basic drawbeads are then developed employing a linear multiple regression method. For the expert models of combined drawbeads such as the double circular drawbead, double stepped drawbead, circular-and-stepped drawbead, etc., those of the basic drawbeads are summed. Finally, in order to verify the expert models developed, the drawing characteristics calculated by the expert models of the double circular drawbead and circular-and-stepped drawbead are compared with those obtained from the experiments. The predictions by expert models agree well with the measurements by experiments.

• Communications for Statistical Applications and Methods
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• 제27권2호
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• pp.189-199
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• 2020

This paper studies a Bayesian ordered multiple linear regression model with skew normal error. It is reasonable that the kind of inherent information available in an applied regression requires some constraints on the coefficients to be estimated. In addition, the assumption of normality of the errors is sometimes not appropriate in the real data. Therefore, to explain such situations more flexibly, we use the skew-normal distribution given by Sahu et al. (The Canadian Journal of Statistics, 31, 129-150, 2003) for error-terms including normal distribution. For Bayesian methodology, the Markov chain Monte Carlo method is employed to resolve complicated integration problems. Also, under the improper priors, the propriety of the associated posterior density is shown. Our Bayesian proposed model is applied to NZAPB's apple data. For model comparison between the skew normal error model and the normal error model, we use the Bayes factor and deviance information criterion given by Spiegelhalter et al. (Journal of the Royal Statistical Society Series B (Statistical Methodology), 64, 583-639, 2002). We also consider the problem of detecting an influential point concerning skewness using Bayes factors. Finally, concluding remarks are discussed.