• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.1
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• pp.68-77
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• 1998

The purpose of this paper is to establish a method estimating the daily urban water demand using statistical analysis that is used for developing the efficient management and operation of the water supply facilities, and accurary of the model is verified by error rate and F-value. The data used in this study were the daily urban water use, the weather conditions such as temperature, precipitation, relative humidity, etc, and the day of The week. The case study was taken placed for the city of Namwon in Korea. The raw data used in this study were rearranged either by month or by season for analysis purpose, and the statistical analysis was applied to the data to obtain the regression model As a result of this study, the linear regression model was developed to estimate the daily urban water use with weather condition. The regression constant and coefficients of the model were determined for each month of a year. The accuracy of the model was within 3% of average error and within 11% of maximum error. The resulting model was found to he useful to the practical operation and management of the water supply facilities.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.551-557
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• 2023

In recent years, radiation has become a socially important issue, increasing the need for accurate prediction of radiation levels. In this study, machine learning-based models such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and LightGBM, which predict the dose rate by time(nSv h^-1) by selecting only important variables, were used, and the correlation between temperature, humidity, cumulative precipitation, wind direction, wind speed, local air pressure, sea pressure, solar radiation, and radiation dose rate (nSv h^-1) was analyzed by collecting weather data and radiation dose rate for about 6 months in Jangseong, Jeollanam-do. As a result of the evaluation based on the RMSE (Root Mean Squared Error) and R-Squared (R-Squared coefficient of determination) scores, the RMSE of the XGBoost model was 22.92 and the R-Squared was 0.73, showing the best performance among the models used. As a result of optimizing hyperparameters of all models using the GridSearch method and comparing them by adding variables inside the measuring instrument, it was confirmed that the performance improved to 2.39 for RMSE and 0.99 for R-Squared in both XGBoost and LightGBM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.290-291
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• 2021

Defects in the coating process of plastic automotive components are caused by various causes and phenomena. The correlation between defect occurrence rate and meteorological and environmental conditions such as temperature, humidity, and fine dust was analyzed. The defect rate data categorized by type and cause was collected for a year from a automotive parts coating company. This data and its correlation with environmental condition was acquired and experimented by machine learning techniques to predict the defect rate at a certain environmental condition. Correspondingly, the model predicted 98% from fine dust and 90% from curtaining (runs, sags) and hence proved its reliability.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.2
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• pp.154-159
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• 1993

Theoretical prediction of the equilibrium of temperature and relative humidity dependance involving $HNO_{3(g)}-NH_{3(g)}$ and $NH_4NH_{3(s, aq)}$ was compared with atmospheric measurement of particulate nitrate$(NO_3^-)$, Ammonia-Nitric Acid partial pressure product $([$NH_{3(g)}][HNO_{3(g)}]ppb^2$) by a triple filter pack sampler from Oct 1991 to July 1992. The measured $HNO_3NH_3$ concentration product K was greater than equilibrium constant $K_p$ calculated from thermodynamic data of $NH_4NO_{3(s, aq)}-HNO_{3(g)}-NH_{3(g)}$ during fall, winter and spring. But K was lower than $K_p$ in summer. K was greater than $K_p$ as the result of supersaturation by air pollution, particularly anthropogenic $NH_3$.The reason of $K < K_p$ was due to removal of particulate nitrate$(NO_3^-)$ by rainout and washout. $NH_4NO_3$ which consists mainly of particulate nitrate is formed by reaction between $HNO_3$ and $NH_3$. As a result of the removal of particulate nitrate$(NO_3^-)$ by rainout and washout, concentrations of $HNO_3$ and $NH_3$ are decreased by equilibrium transfer(Le Chatelier's Law) in atmosphere.

• Corrosion Science and Technology
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• v.19 no.4
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• pp.174-188
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• 2020

Atmospheric corrosion is a natural surface degradation process of metal due to changes in environmental parameters in the surrounding atmosphere. It is very sensitive to environmental parameters such as temperature, relative humidity, sulphur dioxide, and chloride, making it a major global economic challenge. Existing forecasting empirical corrosion models including the ISO standard are based on statistical analysis of experimental studies without considering the behavior of atmospheric parameters. The present study proposes a reliable global empirical model for estimating short and long-term atmospheric corrosion rates based on environmental parameters and corrosion mechanisms obtained from a parametric study. Repercussion of atmospheric corrosion rate due to individual and combined influences of environmental parameters specifies their importance in the estimation. New global empirical coefficients obtained for environmental parameters are statistically established (R² =0.998) with 95% confidence limit. They are validated using experimental datasets of existing studies observed at 88 different continental locations. The current proposed model can predict atmospheric corrosion by means of corrosion formation mechanisms influenced by combined effects of environmental parameters, further abating applicability limitations of location and time.

• Journal of Institute of Convergence Technology
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• v.4 no.2
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• pp.27-33
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• 2014

To well design the solar energy system using solar energy, the correlation to calculate solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry bulb temperature, relative humidity, sunshine duration, and cloud cover) which are different from previous other researches. And then, we finally proposed the first order non-linear correlation from the measured solar irradiation using four meteorological observation data with MINITAB. To show the deviation of the solar irradiation between measured and calculated, this study compared using the daily total solar irradiance and the maximum peak value. From those results, the calculation error was estimated about maximum 25.4% for the daily total solar irradiance. The error of the solar irradiation between measured and calculated was made from the curve fitting error. So, solar irradiation prediction correlation with higher accuracy can be obtained using 2nd or higher order terms with four meteorological observation data.

• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• Journal of Preventive Medicine and Public Health
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• v.48 no.4
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• pp.203-215
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• 2015

Objectives: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea. Methods: We collected and stored 660 000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul. Results: Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient. Conclusions: In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.1
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• pp.15-20
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• 2017

The summer climate is very hot and humid in Korea. The humidity is an important factor in determining thermal comfort. Recently, the research for dehumidification device development has been attempted to save energy that is required for the operation of the current dehumidifiers on the market. Existing dehumidification systems have disadvantages such as wasting energy to drive a compressor. Meanwhile, dehumidification systems with membranes can dehumidify humid air without increasing the dry bulb temperature so it doesn't have to consume cooling energy. In this paper, the cooling energy savings was studied when a dehumidification system was applied in a model building instead of a chiller. The sensible heat load was almost the same result, but the latent heat load was decreased by 38.9% and the total heat load was decreased by 8.5%. As a result, electric energy used to drive the compressor in a chiller was saved by applying a membrane air-conditioning system instead.

• Journal of the Mineralogical Society of Korea
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• v.9 no.1
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• pp.17-25
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• 1996

Usual experimental adsorption isotherms as a function of relative humidity were constructed from adsorbed water contents in soils, which were kept more than 2 days in vacuum desiccators with constant humidities controlled by sulfuric acids of various concentrations. From the experimental data, the adsorption surface areas were calculated on the basis of the existing adsorption theory, such as Langmuir, BET, and Aranovich. Based on the Gibbs function describing chemical potential of perfect gas, the relative humidities in the desiccators were transformed into their chemical potentials, which were assumed to be the same as the potentials of equilibratedly adsorbed water in soils. Moreover, the water potentials were again transformed into the equivalent capillary pressures, heads of capillary rise, and equivalent radius of capillary pores, on the basis of Laplace equation for surface tension pressure of spherical bubbles in water. Adsorption quantity distributions were calculated on the profile of chemical potentials of the adsorbed water, equivalent adsorption and/or capillary pressures, and equivalent capillary radius. The suggested theories were proved through its application for the prediction of temperature rise of sulfuric acid due to hydration heat. Adsorption heat calculated on the basis of the potential difference was dependant on various factors, such as surface area, equilibrium constants in Langumuir, BET, etc.