• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.26-36
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• 2021

In this study, a deep learning model for predicting the queue length was developed using the information collected from the image detector. Then, a multiple regression analysis model, a statistical technique, was derived and compared using two indices of mean absolute error(MAE) and root mean square error(RMSE). From the results of multiple regression analysis, time, day of the week, occupancy, and bus traffic were found to be statistically significant variables. Occupancy showed the most strong impact on the queue length among the variables. For the optimal deep learning model, 4 hidden layers and 6 lookback were determined, and MAE and RMSE were 6.34 and 8.99. As a result of evaluating the two models, the MAE of the multiple regression model and the deep learning model were 13.65 and 6.44, respectively, and the RMSE were 19.10 and 9.11, respectively. The deep learning model reduced the MAE by 52.8% and the RMSE by 52.3% compared to the multiple regression model.

• Journal of the Korean Geotechnical Society
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• v.29 no.11
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• pp.99-106
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• 2013

In this study, the correlation of evaluation items and safety rating for 104 of large-scale slopes along the general national road was analyzed. And, we proposed the regression model to predict the safety rating using the multiple regressions analysis. As the result, it is shown that the evaluation items of slope angle, rainfall and groundwater have a low correlation with safety rating. Also, the regression model suggested by multiple regression analysis shows high predictive value, and it would be possible to apply if the evaluation items of excavation condition and groundwater (rainfall) are not clear.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.953-955
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• 2016

본 논문은 다중 회귀 분석을 이용하여 3축 가속도센서기반의 활동량을 추정하는 방법을 제안한다. 본 연구를 위해 총 59명의 피 실험자가 자체 제작한 활동량계를 착용한 뒤 트레드밀에서 일정한 속도로 걷는/뛰는 동작을 수행한 신호를 수집하였다. 수집한 3축 가속도 신호의 에너지 값에서 사전에 정의한 특징들을 산출한다. 그 다음 각 특징별로 선형, 지수, 로지스틱 회귀 분석을 적용하여 적합도가 높은 특징을 선정한다. 마지막으로 산출된 회귀식들을 사용하여 다중 회귀 분석 방법으로 활동량을 추정한다. 호흡가스 대사 분석기(K4B2)를 착용한 뒤 동일한 방법으로 실험을 수행 하고 제안한 방법과 정확도를 비교한 결과 제안한 방법의 정확도는 86.38 %로 산출되었다. 이는 기존의 Kim 외 3인의 연구결과[1]보다 2.70 %, Actical의 정확도보다 4.31 % 높은 수치이다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.187-194
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• 1996

In this paper, we propose regression neural networks based on regression trees. We map regression trees into three layered feedforward networks. We put multi feature split functions in the first layer so that the networks have a better chance to get optimal partitions of input space. We suggest two supervised learning algorithms for the network training and test both in single feature split and multifeature split functions. In experiments, the proposed regression neural networks is proved to have the better learning performance than those of the single feature split regression trees and the single feature split regression networks. Furthermore, we shows that the proposed learning schemes have an effect to prune an over-grown tree without degrading the learning performance.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.91-101
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• 2001

It has been shown that the multiple trees predictors are more accurate in reducing test set error than a single tree predictor. There are two ways of generating multiple trees. One is to generate modified training sets by resampling the original training set, and then construct trees. It is known that arcing algorithm is efficient. The other is to perturb randomly the working split at each node from a list of best splits, which is expected to generate reasonably good trees for the original training set. We propose a new combined multiple regression trees predictor which uses the latter multiple regression tree predictor as a predictor based on a modified training set at each stage of arcing. The efficiency of those prediction methods are compared by applying to high throughput screening of chemical compounds for biological effects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• Proceedings of the Korea Water Resources Association Conference
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• 1991.07a
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• pp.119-132
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• 1991

장기 수자원 개발계획의 수립에 필요한 월유출량의 추정을 위해, 수위계획지점의 유출자료를 사용하여 다중회귀분석으로 회귀모형을 수립함으로써 미계측지점의 월유출량 추정을 가능토록 하였다. 사용한 자료는 총 48개 수위관측소의 월유출량 및 기상·지상인자이며 이중 43개지점은 모형의 개발에 나머지 5개 지점은 모형의 검증에 이용하였다. 또한 모형을 유역별모형과 전체모형, 평균치모형과 개별자료모형으로 구분하여 모형-1, 모형-2, 모형-3 그리고 모형-4의 4개 모형을 수립하였으며, 검증결과 모형-2가 가장 적절한 모형으로 판단 되었다. 선정된 회기모형과 기존의 가지야마공식의 적용성을 통계적 방법에 의해 비교한 결과, 본 다중회기모형의 연유출량 뿐아니라 월별유출량의 변화성향을 매우 잘 나타내고 있으며, 적용 또한 용이함이 입증되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.433-433
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• 2012

제주지역의 지하수 함양 지체시간을 분석하기 위해 18개 지점의 지하수 관측자료를 기초로 강수-지하수위 자료를 강수사상별로 분류하여 분석하였다. 지하수 함양에 결정적인 영향을 주는 인자로 지하수위의 대수층 두께와 지점의 투수계수를 설정하였다. 대체로 고도가 낮은 지역에서는 지하수 함양 지체가 짧았으나 고도가 높아질 수록 대수층 두께도 증가하여 지하수 함양지체시간은 길게 나타났다. 하지만 대수층 두께만으로 지체시간이 결정되는 것은 아니며 이에 투수계수 자료를 함께 분석해야만 타당한 결과를 얻을 수 있을 것으로 판단하여 대수층 두께와 지점 투수계수를 변수로 두고 관측된 지하수 함양지체시간과의 관계를 다중선형회귀분석을 통해 구하였다. 다중상관계수는 0.9정도로 높게 나타났으며, 대수층 두께에 대한 통계학적 유의성도 적합하게 나타났다. 이와 같이 결정된 회귀식은 향후 지하수 함양지체시간의 공간분포를 결정함에 있어 활용이 가능하며 분포형 수문모형과 연계시킬 경우 통합모델링에 적절하게 반영될 수 있을 것으로 판단된다.

• Economic and Environmental Geology
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• v.52 no.1
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• pp.65-79
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• 2019

In this study, simple regression and multiple regression analyses were performed to analyze the relationship between breccia and clay content and shear strength in fault cores. The results of the simple regression analysis performed for each rock (andesitic rock, granite, and sedimentary rock) and three levels of normal stress (${\sigma}_n=54$, 108, 162 kPa), reveal that the shear strength is proportional to breccia content and inversely proportional to clay content. Furthermore, as normal stress increases, the shear strength is influenced by the change in component content, correlating more strongly with clay content than with breccia content. In the multiple regression analysis, which considers both breccia and clay content, the shear strength is found to be more sensitive to the change in breccia content than to that of clay. As a result, the most suitable regression model for each rock is proposed by comparing the coefficients of determination ($R^2$) estimated from the simple regression analysis with those from the multiple regression analysis. The proposed models show high coefficients of determination of $R^2=0.624-0.830$.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.