• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.8-9
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• 2021

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.352-358
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• 2022

We explored the frequency response of krill target strength (TS) to understand the Antarctic krill (Euphausia superba) and ice krill (Euphausia crystallorophias) using the stochastic distorted-wave Born approximation (SDWBA) model. The results showed that the distribution of orientation and the fatness factor could significantly impact on the frequency response of TS. Krill TS is clearly depended on acoustic properties, which could affect to estimate the biomass of two krill species. The results provide insight into the importance of understanding TS variation to estimate the Antarctic krill and ice krill biomass, and their ecology related to the environmental features in the Southern Ocean.

• 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.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.15-28
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• 2024

Wind forecast is one of the key meteorological factors required for safe aircraft takeoff and landing. In this study, we developed an artificial intelligence-based wind compensation method by learning the Korea Air Force Weather Research and Forecast (KAF-WRF) forecast data and the Airfield Meteorological Observation System (AMOS) data at five airports using Support Vector Machine (SVM). The SVM wind prediction models were composed of three types according to the learning period (30 days, 40 days, and 60 days) using seven KAF-WRF variables as training data, and the wind prediction performance at the five airports was evaluated using Root Mean Squared Errors (RMSE). According to the results, the SVM wind prediction model trained using U (east-west) and V (north-south) components performed approximately 18% better than the model trained using wind speed and wind direction. The wind correction of KAF-WRF with AMOS observations via SVM outperformed the conventional KAF-WRF wind predictions in eight out of ten cases, capturing abrupt changes in wind direction and speed with a 25% reduction in RMSE.

• Corrosion Science and Technology
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• v.23 no.4
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• pp.324-333
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• 2024

Atmospheric corrosion poses a significant threat to durability of metallic materials and safety of structures, making precise prediction of corrosion rates crucial in industrial and engineering settings. Understanding the exact rate of corrosion is essential. However, accurate inclusion of various environmental factors that can influence atmospheric corrosion in the calculation of corrosion rate is a complex challenge. This study introduces a physics-based model that incorporates electrochemical methods and considers active surface area affected by surface contaminants to estimate atmospheric corrosion rate of carbon steel. The model can evaluate corrosion levels using key factors such as chloride deposition rate, relative humidity, and the presence of surface particles. By integrating these considerations, this model moves beyond empirical estimations, providing a more stable prediction of corrosion rate that is less susceptible to environmental variations. This model provides a robust tool for defense applications, offering precise insights into the dynamics of atmospheric corrosion that could enhance the maintenance and safety of weapon systems.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.4
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• pp.243-251
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• 2012

Real-time monitoring for environmental factors(temperature, salinity, chlorophyll, etc.) and carbonate components( pH and $fCO_2$) was conducted during 5-6th of July, 2012 at a seaweeds farm in Gijang, Busan. Surface temperature and salinity were ranged from $12.5{\sim}17.6^{\circ}C$ and 33.7~34.0, respectively, with highly daily and inter-daily variations due to tide, light frequency(day and night) and currents. Surface $fCO_2$ and pH showed a range of $381{\sim}402{\mu}atm$ and 8.03~8.15, and chlorophyll-a concentration in surface seawater ranged 0.8~5.8 ${\mu}g\;L^{-1}$. Environmental and carbonate factors showed the highest/lowest values around 5 pm of 5th July when the lowest tidal height and strongest thermocline in the water column, suggesting that biological production resulted in decrease of $CO_2$ and increase of pH in the seaweed farm. Processes affecting the surface $fCO_2$ distribution were evaluated using a simple budget model. In day time, biological productions by phytoplankton and macro algae are the main factors for $CO_2$ drawdown and counteracted the amount of $CO_2$ increase by temperature and air-sea exchange. The model values were a little higher than observed values in night time due to the over-estimation of physical mixing. The model suggested that algal production accounted about 14-40% of total $CO_2$ variation in seaweed farm.

• Journal of Environmental Impact Assessment
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• v.29 no.5
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• pp.391-402
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• 2020

Health Impact Assessment based on municipal law is performed and written in the sanitary and public health part in the current environmental impact assessment. Residential development projects such as housing site development etc., are not subject to health impact assessment under Article 13 of the Environmental Health Act. However, health impact assessment is conducted partially based on the review that health impact assessment targets which are identified among substances emitted from pollutants nearby industrial complexes should be assessed risk (including carcinogenic and non-carcinogenic) at the stage of the environmental impact assessment consultation. Although residential development projects do not have plans for pollutant emitting facilities that emit hazardous air pollutants, there is a possibility that residents might be affected by pollutants from industrial complex near residential area in the future. In this study, Health impact assessment was conducted to examine the impact on residents in planned areas by analyzing previous residential development projects. We predicted future impact by using the literature survey results on surrounding area (case1) and conducting contribution analysis (case2) and predicting exposure concentration of carcinogenic substances applying Atmospheric Diffusion Model (AERMOD). By this study, we concluded that applying on-site survey, contribution analysis and prediction of exposure concentration by using AERMOD complementarily will be effective to assess the health impact to the receptors by pollutants from industrial complexes near the planned zone.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.427-434
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• 2015

The objective of this study is to establish an rational mixture-proportioning procedure for low-$CO_2$ concrete using supplementary cementitious materials (SCMs) achieving the targeted $CO_2$ reduction ratio as well as the conventional requirements such as initial slump, air content, and 28-day compressive strength of concrete. To evaluate the effect of SCM level on the $CO_2$ emission and compressive strength of concrete, a total of 12537 data sets were compiled from the available literature and ready-mixed concrete plants. The amount of $CO_2$ emission of concrete was assessed under the system boundary from cradle to concrete production stage at a ready-mixed concrete plant. Based on regression analysis using the established database, simple equations were proposed to determine the mixture proportions of concrete such as the type and level of SCMs, water-to-binder ratio, and fine aggregate-to-total aggregate ratio. Furthermore, the $CO_2$ emissions for a given concrete mixture can be straightforwardly calculated using the proposed equations. Overall, the developed mixture-proportioning procedure is practically useful for determining the initial mixture proportions of low-$CO_2$ concrete in the ready-mixed concrete field.

• Journal of the Korean GEO-environmental Society
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• v.1 no.1
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• pp.5-12
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• 2000

Mudstone, found Du-Ho Dong and around there in Po-Hang, is used as construction material. When it is exposed to the air and contacts with water, the strength is decreased rapidly and then it causes a lot of problems. In the field, clay soils with $K_o$ condition have anisotropic characteristics which behave differently according to the change of principal stress direction. In this study, $K_o$ consolidation is performed to make the completely weathered mudstone under the same conditions of construction place. Then, the triaxial compression test is performed at different shear velocity and anisotropy by sampling degree and the stress - strain behavior is shown the strain softening behavior. The stress - strain relationship from triaxial compression test is compared with the prediction value of Cam-clay model. From the results of tests, $K_o$ value decreases with the increase of sampling degree. Generally the behavior of $K_o$ consolidated specimen shows work-softening characteristic. The trend of behaviour of the measured is nearly to same to the predicted by Cam-clay model. But the measured value of deviator stress is very higher than the predicted. Therefore, Cam-clay model was not appropriate to the completely weathered mudstone consolidated with $K_o$ condition in Pohang region.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.215-224
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• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.