• 자원환경지질
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• 제44권2호
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• pp.135-152
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• 2011

제주도 남측사면에서 산악지역부터 해안지역에 걸쳐 분포하는 용천수에 대해 풍수기와 갈수기의 2회에 걸쳐 측정된 $NO_3$ 농도를 수해지질학적 인자 및 토지 이용 특성 인자를 포함하는 공간 변수들의 다중선형 회귀모형으로 예측하였다. 용천수의 $NO_3$ 농도는 평균 20 mg/L이며, <0.02~86 mg/L의 범위를 보여 인위적 오염의 정도가 매우 다양하다. 공간 변수는 용천수를 중심으로 원형 버퍼를 설정하여 추출하였으며, 수정결정계수 증가율과 원형 버퍼의 제한점을 고려하여 반경 400 m를 최적 범위로 설정하였다. 선택된 회귀 모형들은 p-값과 더빈-왓슨 통계치에 근거하여 모두 통계적으로 유의하였다. 설명변수는 수정결정계수, Cp (total squared error), AIC (Akaike's Information Criterion)등을 기준으로 선택하였으며 변수들의 유의성과 다중공선성을 확인하여 최적 회귀 모형을 제시하였다. 일부 용천수들은 이상치로 확인되었으나 전체 시료의 10%이내였으며, 이들은 원형 버퍼를 사용하는 다중회귀분석의 한계를 지시한다고 할 수 있다. 변수의 유의성 기준으로 선정된 최적 회귀 모형의 결정계수는 이상치 제거 전이 0.74-0.79, 제거 후가 0.86-0.87의 범위로 높은 설명력을 보여주었으며, 자연지역 면적 비율이 용천수의 $NO_3$ 농도에 가장 큰 영향력을 가지는 것으로 나타났다. 용천수 $NO_3$ 농도에 대한 인위적 토지이용의 영향력은 최적 버퍼 반경에서 두 조사 시기 모두 과수원 > 주거지역 > 밭의 순으로 나타났다. 이러한 결과는 제주도 남측사면 용천수의 수질이 수리지질학적 인자보다는 토지 이용 특성에 크게 좌우됨을 지시하며, 용천수의 오염 취약성이 주변의 지표 오염원, 특히 과수원 분포에 민감함을 보여준다.

• 한국수자원학회논문집
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• 제44권12호
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• pp.915-928
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• 2011

본 연구에서는 댐유역의 연 실제증발산량에 영향을 미치는 주요한 수문기후요소를 파악하고 유역으로부터의 연 실제증발산량 산정을 위한 다변량회귀식을 제시하고자 하였다. 이를 위하여 우리나라 5개 댐유역(괴산댐, 섬진강댐, 소양강댐, 안동댐, 합천댐)에서연 물수지분석을실시하여 연실제증발산량을 산정하였고, 수문기후자료를 이용한 다변량회귀식으로부터 산정된 증발산량과 비교 검토함으로서 다변량회귀식의 타당성을 검토하였다. 또한 잠재증발산식들을 이용한 실제증발산량 산정 가능성을 파악하기 위하여 잠재증발산식들(Penman식, FAO P-M식, Makkink식, Preistley-Taylor식, Hargreaves식)로부터 산정된 잠재증발산량과 실제증발산량의 상관성을 검토하였다. 검토 결과 실제증발산량과 잠재증발산량 사이에 상관관계가 적어서 잠재증발산량을 이용한 실제증발산량 산정방법은 적절하지 않은 것으로 나타났다. 기존에 제안된 유역 실제증발산량 산정식들과 비교를 통하여 연 실제증발산량을 산정하는데 있어서 다변량회귀식의 적용성을 확인하였다. 또한 각 댐 유역의 실제증발산량에 영향을 미치는 주요 수문기후요소는 각기 다른 것으로 나타났으나, 공통적으로 강수량이 연 실제증발산량 산정을 위한 주요 기후요소인 것으로 나타났다.

• 한국소음진동공학회논문집
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• 제20권11호
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• pp.1018-1024
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• 2010

The printer noise consists of the noise of the various components and parts such as motor, fan and solenoid. And the human's printing sound recognition shows various aspects when the printer starts to print papers because the components operate at the same time. Especially, printers are usually installed in the quiet office room. Therefore the printing noise is related to its competitiveness in the market. The importance of the printer sound qualities is increasing and it is necessary to develop the sound quality evaluation system, so it is a key point to identify the noise source of the printer and develop the sound quality index to each component. By using this evaluation system, it is possible to evaluate the sound quality of a prototype printer compared to the already existing one. In this paper, the printer sound quality evaluation system was developed by the following steps. Firstly, the signal processing method was applied to the recorded printing sound to identity and split the noise of components. Secondly, the MLR(multiple linear regression) method and the psychoacoustics were used to develop the sound quality index. Finally, the improvement of the printer sound quality is possible by using the result of the MLR and the path analysis. The output of this research will be applied to the development of a new printer.

• 한국정보통신학회논문지
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• 제23권1호
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• pp.20-26
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• 2019

미세먼지의 인체 영향으로 인해 기존 대기 환경 모니터링 네트워크에서 측정된 과거 데이터를 활용하여 미세먼지를 예측하려는 다양한 연구가 진행되고 있다. 하지만 기존 설계된 예측 모델의 측정 환경, 세부 조건을 정확히 설정하기 어려우며, 측정된 기상 데이터의 누락과 같은 문제로 기존 연구 결과에 기반 한 새로운 예측 모델의 설계가 필요하다. 본 논문에서는 미세먼지 예측을 위한 선행 연구로서 다수의 연구에서 사용된 기계 학습 알고리즘인 다중 선형 회귀와 인공 신경망을 통해 예측 모델을 설계하여 미세먼지 예측을 위한 알고리즘의 적합성을 평가하였다. RMSE를 통한 예측 성능 비교 결과, MLR 모델의 경우 18.13, MLP 모델의 경우 14.31의 값을 보여 미세먼지 농도를 예측함에 있어 인공 신경망 모델이 예측에 더 적합함을 보였다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• 대기
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• 제34권2호
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• 공업화학
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• 제33권6호
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Molecular & Cellular Toxicology
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• 제5권1호
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• pp.14-22
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• 2009

In vitro cytotoxicity of 23 alkyl phenols (APs) on human cervical cancer cell lines (HeLa) was determined using the lactate dehydrogenase (LDH) cytotoxicity assay. Two different sets of descriptors were used to construct the calibration model based on Genetic Algorithm-Multiple Linear Regression (GA-MLR) based on the experimental data. A statistically robust Structure-Activity Relationships (QSAR) model was achieved ($R^2$=95.05%, $Q^2_{LOO}$=91.23%, F=72.02 and SE= 0.046) using three Dragon descriptors based on Me (0D-Constitutional descriptor), BELp8 (2D-Burden eigenvalue descriptor) and HATS8p (3D-GETAWAY descriptor). However, external validation could not fully prove its validity of the selected QSAR in characterization of the cytotoxicity of APs towards HeLa cells. Nevertheless, the cytotoxicity profiles showed a finding that 4-n-octylphenol (4-NOP), 4-tert-octyl-phenol (4-TOP), 4-n-nonylphenol (4-NNP) had a more potent cytotoxic effect than other APs tested, inferring that increased length and molecular bulkiness of the substituent had important influence on the LDH cytotoxicity.

• Bulletin of the Korean Chemical Society
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• 제30권1호
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• pp.67-76
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• 2009

The development of a variety of methods like AM1, PM3, PM5 and DFT now allows the calculation of atomic and molecular properties with high precision as well as the treatment of large molecules with predictive power. In this paper, these methods have been used to calculate a number of quantum chemical descriptors (like Klopman atomic softness in terms of $E_n^{\ddag}\;and\;E_m^{\ddag}$, chemical hardness, global softness, electronegativity, chemical potential, electrophilicity index, heat of formation, total energy etc.) for 75 alkanes to predict their boiling point values. The 3D modeling, geometry optimization and semiempirical & DFT calculations of all the alkanes have been made with the help of CAChe software. The calculated quantum chemical descriptors have been correlated with observed boiling point by using multiple linear regression (MLR) analysis. The predicted values of boiling point are very close to the observed values. The values of correlation coefficient ($r^2$) and cross validation coefficient ($r_{cv}^2$) also indicates the generated QSPR models are valuable and the comparison of all the methods indicate that the DFT method is most reliable while the addition of Klopman atomic softness $E_n^{\ddag}$ in DFT method improves the result and provides best correlation.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.182-182
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• 2015

우리나라에서는 강우관측시스템의 지역적 불균형으로 상대적으로 소규모 저수지의 경우 미계측유역의 특성을 가지며, 신뢰성 있는 강우량, 유출량, 증발량 자료가 매우 부족한 실정이다. 다목적댐 유역과 같은 계측유역의 경우 상류유역의 유입량 자료의 확보가 용이하지만 대부분의 유역의 경우 계측장비가 부족하여 신뢰성이 확보된 유입량 자료를 얻는데 많은 어려움이 있다. 본 연구에서는 미계측유역의 유입량 산정을 위하여 계측유역을 대상으로 강우-유출 모형의 매개변수를 산정하였으며, 산정된 매개변수를 유역특성인자와의 상관성을 토대로 다중선형회귀분석기법(multiple linear regression, MLR)을 적용하여 지역화(regionalization)를 위한 회귀식을 도출하였다. 이를 위해 양질의 유량자료가 확보된 K-water 17개 댐 유역을 대상으로 매개변수를 산정하였으며 이 중 2개의 댐 유역을 미계측유역으로 간주하여 개발된 모형을 검증하였다. 대부분의 통계 지표에서 우수한 모의능력을 확인하였으며, 본 연구를 통하여 개발된 지역화 기법을 미계측유역에 활용한다면 보다 정량적이고 효율적인 수자원 계획이 가능할 것으로 판단된다. 향후 연구로는 불확실성을 고려한 Bayesian GLM 모형을 이용한 지역화기법을 개발하여 매개변수의 불확실성까지 고려할 수 있는 방안을 모색하고자 한다.