• 한국산림과학회지
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• 제89권3호
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• pp.297-305
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• 2000

이 연구는 북한산국립공원(北漢山國立公圓) 북동사면(北東斜面) 일대(一帶) 계류수질(溪流水質) 오염(汚染)에 영향(影響)하는 인자(因子)를 파악함으로써 국립공원내 계류수질 보전을 위한 과학적 기초 자료를 제공하기 위하여 1998년 7월부터 1999년 11월까지 수행하였다. 계류수질오염(溪流水質汚染)을 판단하는 지표(指標)인 전기전도도(電氣傳導度)의 설명에 유의한 영향을 미치는 인자는 탐방객수와 $Cl^-$ 점유비 등 2개 인자로 5% 수준에서 유의하여 북한산국립공원을 이용하는 탐방객수(探訪客數)의 증가(增加)는 계류수의 전기전도도(電氣傳導度)를 상승시켜 계류수질(溪流水質) 오염(汚染)에 영향(影響)을 미치는 것으로 분석(分析)되었다. 다중회귀분석결과 용존산소포화도의 설명에 유의한 영향을 미치는 인자는 용존산소량과 수온 등 2개 인자로 1% 수준에서 유의하였다. $Cl^-$의 설명에 유의한 영향을 미치는 인자는 전기전도도, $K^+$, $Na^+$, $SO_4{^{2-}}$, 이온총량, $Cl^-$점유비, $SO_4{^{2-}}$점유비 등 7개 인자로 5%, 1% 수준에서 유의하였다. 또한, $NO_3{^-}$의 설명에 유의한 영향을 미치는 인자는 $Na^+$, $Ca^{2+}$, $SO_4{^{2-}}$, $Cl^-$점유비, $NO_3{^-}$점유비 등 5개 인자로 5%, 1% 수준에서 유의하였으며, $SO_4{^{2-}}$의 설명에 유의한 영향을 미치는 인자는 $NO_3{^-}$, 이온총량 등 2개 인자로 5% 수준에서 유의하였다.

• 한국환경과학회지
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• 제21권10호
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• pp.1235-1244
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• 2012

The objective of this study is to evaluate Flow-Pollutant load delivery ratio equations developed from rural watershed on main subwatersheds within Juam Lake. Two regression equations for BOD and three equations for T-P were evaluated on Bosung cheon, Dongbok cheon, Songgwang cheon, Naenam cheon, and Sinpyeon cheon. The results show that estimation of BOD delivery ratio using flow-delivery equation is reliable when relative composition of discharge load of pollutant sources of a watershed is similar to those of watershed where the equation developed. On the other hand, application of regression equation for T-P was feasible when the landuse pattern and relative composition of discharge load of pollutant sources of a watershed is similar to those of watershed where the equation developed.

• 한국지반공학회논문집
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• 제34권6호
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• pp.37-47
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• 2018

본 연구에서는 모래다짐말뚝(sand compaction pile, SCP)과 자갈다짐말뚝(gravel compaction pile, GCP)으로 보강된 지반의 극한지지력을 예측할 수 있는 식을 제안하고자 34개의 국내외 실내재하시험 데이터를 수집하고 이를 분석하였다. 수집된 자료를 기존의 이론식에 의한 극한지지력 산정 값과 비교하여 기존 이론식의 예측 정도를 파악하였다. 또한 극한 지지력 예측식을 제안하고자 다중회귀분석을 수행하였으며, 단일잔류 교차검증에 따른 예측오차평가를 통하여 가장 효율적인 입력변수의 수 및 조합을 선정하였다. 최종적으로 SCP와 GCP의 실내재하시험에 대한 극한 지지력을 예측하기 위한 다중회귀식을 제안하였으며 그 성능을 평가하였다.

• 한국농공학회지
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• 제26권2호
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• Geomechanics and Engineering
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• 제23권2호
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• pp.115-125
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• 2020

This study aims to explore practical and useful equations for rapid evaluation of uniaxial compressive strength of concrete (UCS-C) during the preliminary design stage of aggregate selection. For this purpose, aggregates which were produced from eight different intact rocks were used in the production of concretes. Laboratory experiments involved the tests for uniaxial compressive strength (UCS-R), point load index (PLI-R), P wave velocity (UPV-R), apparent porosity (n-R), unit weight (UW-R) and aggregate impact value (AIV-R) of the rock samples. UCS-C, point load index (PLI-C) and P wave velocity (UPV-C) of concrete samples were also determined. Relationships between UCS-R-rock parameters and UCS-C-concrete parameters were developed by regression analyses. In the simple regression analyses, PLI-C, UPV-C, UCS-R, PLI-R, and UPV-R were found to be statistically significant independent variables to estimate the UCS-C. However, higher coefficients of determination (R²=0.97-1.0) were obtained by multiple regression analyses. The results of simple regression analysis were also compared to the limited number of previous studies. The strength conversion factor (k) values were found to be 14.3 and 14.7 for concrete and rock samples, respectively. It is concluded that the UCS-C can roughly be estimated from derived equations only for the specified rock types.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1625-1625
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• 2001

The calibration equations for Brix value determination of intact mango were developed using the NIR spectra in a short wavelength region from 700 to 1100 nm. Multiple linear regression (MLR) and partial least square regression (PLS) was used for the calibration. It was found that the best wavelength region for PLS calibration from 900 to 1000 nm was similar to the wavelength region selected by MLR from 906 nm to 996 nm. Both MLR and selected region PLS provided sufficiently accurate prediction equations for Brix determination of intact mango. For MLR, the prediction results were SEP = 0.45 Brix and Bias = -0.04 Brix while PLS prediction results were SEP : 0.46 Brix and Bias = -0.2 Brix. It was concluded that MLR and PLS would have similar abilities in making calibration equation for Brix determination of intact mango if the appropriate wavelengths or wavelength region were selected. The appropriate wavelength region for PLS regression could be assumed by using the wavelength region selected by MLR in place of random selection, The relationship between calibration results of MLR and PLS regression is discussed.

• 대한환경공학회지
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• 제28권4호
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• pp.402-406
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• 2006

본 연구에서는 pH가 7과 8인 조건에서 디메칠아민 농도를 0.05 mM로 고정시키고 클로라민 농도 변화에 따른 NDMA 생성농도를 측정하여 회귀식을 개발하였다. 클로라민/디메칠아민 비율이 1보다 클 때와 작을 때에 NDMA 생성에 큰 차이를 보여 통계분석에서 구분하여 회귀식을 제시하였다. NDMA 생성 농도는 클로라민/디메칠아민 비율에 높은 상관성을 보여 주었다. 개발한 회귀식을 근거로 하여 NDMA 생성 농도를 기존 연구에 적용한 결과 상대오차는 $-79{\sim}163%$로 조사되었다. 상수 처리장에서 원수의 디메칠아민 농도를 알 경우 이 식을 적용 하면 NDMA 발생 범위를 제시하여 주입할 클로라민 농도나 전처리 필요성을 결정하는데 도움을 줄 것으로 생각된다.

• 드라이브 ㆍ 컨트롤
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• 제20권4호
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• pp.133-139
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• 2023

A weighing system calculates the bucket's excavation amount of an excavator. Usually, the excavation amount is computed by the excavator's motion equations with sensing data. But these motion equations have computing errors that are induced by assumptions to the linear systems and identification of the equation's parameters. To reduce computing errors, some commercial weighing system incorporates particular motion into the excavation process. This study introduces a linear regression model on an artificial neural network that has fewer predicted errors and doesn't need a particular pose during an excavation. Time serial data were gathered from a 30tons excavator's loading test. Then these data were preprocessed to be adjusted by MPL (Multi Layer Perceptron) or CNN (Convolutional Neural Network) based linear regression models. Each model was trained by changing hyperparameter such as layer or node numbers, drop-out rate, and kernel size. Finally ID-CNN-based linear regression model was selected.

• 한국농공학회지
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• 제41권5호
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• pp.86-92
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• 1999

Effluent concentration estimation equations for wetland system were developed throught statistical analysis of treatment wetland experimental data. Existin g empirical equations were reviewed for thier accuracy with experimental data, and compared with the estimatin equations. About 70 experimental data sets were used for multiple regression, and variables include influent concentration, hydraulic loading rate, average daily air temperature , and plant coverage. The estimatin equations developed for BOD5 , SS ,T-P, and T-N predicted effluent concentrations moderately well, and coefficient fo determination ($R^2$) for them was 0.74 , 0.60, 0.59 and 0.58 respectively. The equations obtained from same data but excluding plant coverage showed relatively lower $R^2$ than the former case, and it was 0.66, 0.52, 0.41 and 0.57 respectively. The EPA, WPCF , and Kadlec and Knight equations worked poorly and $R^2$ for them was significantly lower than the estimation equation developed in the study. The reason might be that the existing equations were oversimplified that they did ot include important parameters such as air temperature and plant coverage. Therefore, developing reasonable estimation equations from experiment under realistic condition is highly recommended rather than using exiting estimation equations.

• Journal of Biosystems Engineering
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• 제12권2호
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• pp.38-43
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• 1987

An experimental study was conducted to develop a thin-layer rewetting equation of short grain rough rice of Akihikari variety. Four thin-layer rewetting equations were experimentally determined from $25^{\circ}C$ to $45^{\circ}C$ and 70%RH to 85%RH conditions. Diffusion, Henderson, Page, and Thompson equations widely used as thin-layer drying equations were selected. Experimental data were fitted to these equations using linear regression analysis except diffusion equation. The diffusivity in the diffusion equation was determined by optimization method. Four equations were highly significant. In order to compare the goodness of fit of each equation, the error mean square of each equawas calculated. The diffusion model was not a very good model because the error mean square was very large. The other three models showed the same level or error mean square and could predict satisfactorily the rewetting rate or short grain rough rice.