• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.1
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• pp.149-158
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• 2002

The carbonation process is affected by both the concrete material properties such as W/C ratio, types of cement and aggregates, admixture characteristics and the environmental factors such as $CO_2$ concentration, temperature, humidity. Based on results of preliminary study on carbonation, this study is to develop a carbonation prediction model by taking account of $CO_2$ concentration, temperature, humidity ad W/C ratio among major factor affecting the carbonation process. And to constitute a model formula which correspond to the mix design of the nuclear power plant, test coefficient that correspond to the design of the nuclear power plant is obtained based on the results of accelerated carbonation test. Also a field coefficient which is obtained based on results of the field examination is included to improve the conformity of the actual structures of nuclear power plant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.570-576
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• 2020

This study uses beacons and smartphone Global Positioning System (GPS) receivers to establish a location-based disaster/hazard prediction system. Beacons are usually installed indoors to locate users using triangulation in the room, but this study is differentiated from previous studies because the system is used outdoors to collect information on registration location and temperature and humidity in hazardous areas. In addition, since it is installed outdoors, waterproof, dehumidifying, and dustproof functions in the beacons themselves are required, and in case of heat and humidity, the sensor must be exposed to the outside, so the waterproof function is supplemented with a separate container. Based on these functions, information on declining and vulnerable areas is identified in real time, and temperature/humidity information is collected. We also propose a system that provides weather and fine-dust information for the area concerned. User location data are acquired through beacons and smartphone GPS receivers, and when users transmit from declining or vulnerable areas, they can establish the data to identify dangerous areas. In addition, temperature/humidity data in a microspace can be collected and utilized to build data to cope with climate change. Data can be used to identify specific areas of decline in a microspace, and various analyses can be made through the accumulated data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.31-32
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• 2020

Concrete changes its internal moisture distribution depending on the external environment, and changes in the condition of the material's interior over time affect the performance of the concrete. These effects are closely related to the long-term behavior and durability of concrete, and the degree of deterioration varies from climate to climate in each region. In this study, we use actual climate data from each region with distinct climates. A multi-physical analysis based on the method was conducted to predict the difference and degree of deterioration rate by climate.

• Smart Media Journal
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• v.11 no.5
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• pp.38-47
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• 2022

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R² value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R² values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

• Journal of Environmental Science International
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• v.31 no.7
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• pp.617-635
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• 2022

The effect of coupled data assimilation (DA) on the meteorological prediction in the west coastal region of Korea was evaluated using a coupled atmosphere-ocean model (e.g., COAWST) in the spring (March 17-26) of 2019. We performed two sets of simulation experiments: (1) with the coupled DA (i.e., COAWST_DA) and (2) without the coupled DA (i.e., COAWST_BASE). Overall, compared with the COAWST_BASE simulation, the COAWST_DA simulation showed good agreement in the spatial and temporal variations of meteorological variables (sea surface temperature, air temperature, wind speed, and relative humidity) with those of the observations. In particular, the effect of the coupled DA on wind speed was greatly improved. This might be primarily due to the prediction improvement of the sea surface temperature resulting from the coupled DA in the study area. In addition, the improvement of meteorological prediction in COAWST_DA simulation was also confirmed by the comparative analysis between SST and other meteorological variables (sea surface wind speed and pressure variation).

• Korean Journal of Food Science and Technology
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• v.16 no.4
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• pp.437-442
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• 1984

The color changes and sorption characteristics of dried whole red pepper stored at various relative humidity and temperature were studied. Dried whole red pepper was browned at relative humidity above 67%, and was molded at relative humidity above 84%, and was decolorated at relative humidity below 32%. Therefore, about 50% RH condition was suitable for the preservation of dried whole red pepper and the safe moisture content levels for storge to prevent decolorization were ranging from 15.65% to 19.62% dry basis (DB) with varying temperatures. The moisture contents of monolayer value for the dried whole red pepper were ranging from 7.52% to 9.23% (DB) with varying temperatures. The third order regression equation for the equilibrium moisture content prediction with relative humidity was determined.

• Atmosphere
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• v.27 no.4
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• pp.385-398
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• 2017

Numerical weather prediction experiments were carried out by applying topographic effects to reduce or enhance the solar radiation by terrain. In this study, x and ${\kappa}({\phi}_o,\;{\theta}_o)$ are precalculated for topographic effect on high resolution numerical weather prediction (NWP) with 1 km spatial resolution, and meteorological variables are analyzed through the numerical experiments. For the numerical simulations, cases were selected in winter (CASE 1) and summer (CASE 2). In the CASE 2, topographic effect was observed on the southward surface to enhance the solar energy reaching the surface, and enhance surface temperature and temperature at 2 m. Especially, the surface temperature is changed sensitively due to the change of the solar energy on the surface, but the change of the precipitation is difficult to match of topographic effect. As a result of the verification using Korea Meteorological Administration (KMA) Automated Weather System (AWS) data on Seoul metropolitan area, the topographic effect is very weak in the winter case. In the CASE 1, the improvement of accuracy was numerically confirmed by decreasing the bias and RMSE (Root mean square error) of temperature at 2 m, wind speed at 10 m and relative humidity. However, the accuracy of rainfall prediction (Threat score (TS), BIAS, equitable threat score (ETS)) with topographic effect is decreased compared to without topographic effect. It is analyzed that the topographic effect improves the solar radiation on surface and affect the enhancements of surface temperature, 2 meter temperature, wind speed, and PBL height.

• Atmosphere
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• v.24 no.4
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• pp.471-479
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• 2014

A road surface temperature prediction model (UM-Road) using input data of the Unified Model (UM) output and road physical properties is developed and verified with the use of the observed data at road weather information system. The UM outputs of air temperature, relative humidity, wind speed, downward shortwave radiation, net longwave radiation, precipitation and the road properties such as slope angles, albedo, thermal conductivity, heat capacity at maximum 7 depth are used. The net radiation is computed by a surface radiation energy balance, the ground heat flux at surface is estimated by a surface energy balance based on the Monin-Obukhov similarity, the ground heat transfer process is applied to predict the road surface temperature. If the observed road surface temperature exists, the simulated road surface temperature is corrected by mean bias during the last 24 hours. The developed UM-Road is verified using the observed data at road side for the period from 21 to 31 March 2013. It is found that the UM-Road simulates the diurnal trend and peak values of road surface temperature very well and the 50% (90%) of temperature difference lies within ${\pm}1.5^{\circ}C$ (${\pm}2.5^{\circ}C$) except for precipitation case.

• Computers and Concrete
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• v.21 no.5
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• pp.559-567
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• 2018

In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures ($400^{\circ}C-800^{\circ}C$) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models' results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.