• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.421-425
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• 2012

The objective of this study was to improve the detection limit of rapid detection kit for Salmonella Typhimurium by image analysis system. The rapid detection kit was comprised of four elements: sample pad, conjugate pad, nitrocellulose pad and absorbent pad. Gold nanoparticle and Salmonella antibody were used as a tag and a receptor. Salmonella antibody and goat rabbit IgG antibody were used as test and control lines on nitrocellulose membrane. The color intensity of test line began to increase from $10^5CFU/mL$ of Salmonella sample. A multiple linear regression analysis was employed to explain the relationship between predicted and measured number of Salmonella cells. The developed model could successfully predict the cell number of Salmonella with validation against extra-experimental result.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• 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.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.151-163
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• 2018

The objective of this study was to estimate LOADEST (LOAD Estimator) coefficients for simulating pollutant loads in ungauged watersheds. Regression models of LOADEST were used to simulate pollutant loads, and the multiple linear regression (MLR) was used for coefficients estimation on watershed characteristics. The fifth and third model of LOADEST were selected to simulate T-N (Total-Nitrogen) and T-P (Total-Phosphorous) loads, respectively. The results and statistics indicated that regression models based on LOADEST simulated pollutant loads reasonably and model coefficients were reliable. However, the results also indicated that LOADEST underestimated pollutant loads and had a bias. For this reason, simulated loads were corrected the bias by a quantile mapping method in this study. Corrected loads indicated that the bias correction was effective. Using multiple regression analysis, a coefficient estimation methods according to the watershed characteristic were developed. Coefficients which calculated by MLR were used in models. The simulated result and statistics indicated that MLR estimated the model coefficients reasonably. Regression models developed in this study would help simulate pollutant loads for ungauged watersheds and be a screen model for policy decision.

• 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.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.24-27
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• 2006

16개 기상관측소에서 채취한 토양 시료에 대한 토양 물성 및 열특성를 측정하였으며 이를 통하여 공극률, 함수비, 충적밀도, 입도 분포, 유기물 함량, 토양구성광물의 종류 및 함량이 열전도도에 미치는 영향을 파악하였다. 상관성 분석결과 입도분포, 유기물함량 및 토양 구성광물의 종류 및 함량은 낮은 상관성을 보였으며 용적밀도 $(R^2=0.60)$, 함수비$(R^2=0.54)$와 공극률$(R^2=0.56)$은 높은 상관성을 보였다. 또한 함수비(2%)와 토양의 종류에 따른 다중회귀 분석을 통하여 토양의 열전도도를 추정할 수 있는 회귀식을 제시하였다.

• 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.