• Journal of the Korean earth science society
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• v.40 no.1
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• pp.24-36
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• 2019

In the mid-eastern Yellow Sea, glacio-eustatic sea-level fluctuations and a regional tectonic subsidence have combined to represent an aggradational stacking pattern of sedimentary units during late Pleistocene-Holocene. The accumulated sediments are divisible into two-type units of Type-A and Type-B in high-resolution air-gun seismic profiles and the deep-drilled core of YSDP-105. Type-A unit largely comprises clast-rich coarse-grained sediments of non-marine to paralic origin, whereas Type-B unit consists mostly of tidal fine-grained sediments. Based on a bottom model of the sedimentary units, this study suggested a geoacoustic model of long-coring bottom layers at the YSDP-105 drilling site of the mid-eastern Yellow Sea. The geoacoustic model of 64-m depth below the seafloor with four-layer geoacoustic units was reconstructed in continental shelf strata at 45 m in water depth. For actual modeling, the geoacoustic property values of the models were compensated to in situ depth values below the seafloor using the Hamilton modeling method. We suggest that the geoacoustic model will be used for geoacoustic and underwater acoustic experiments of mid- and low-frequency reflecting on the deep bottom layers in the mid-eastern Yellow Sea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Journal of Environmental Impact Assessment
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• v.32 no.4
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• pp.203-213
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• 2023

The AERMOD model was the most used, accounting for 89.0%, based on the analysis of the environmental impact assessment reports published in the Environmental Impact Assessment Information Support System (EIASS) between 2021 and 2022. The mismatch of versions between AERMET and AERMOD was found to be 25.3%. There was the operational time discrepancy of 50.6% from industrial complexes, urban development projects between used in the model and applied in estimating pollutant emissions. The results of applying various versions of the AERMET and AERMOD models to both area sources and point sources in both simple and complex terrain in the Gunsan area showed similar values after AERMOD version 12 (15181). Emissions are assessed as 24-hour operation, and the predicted concentration in both simple and complex terrain when using the variable emission coefficient option that applies an 8-hour daytime operation in the model is lowered by 37.42% ~ 74.27% for area sources and by 32.06% ~ 54.45% for point sources. Therefore, to prevent the error in using the variable emission coefficient, it is required to clearly present the emission calculation process and provide a detailed explanation of the composition of modeling input data in the environmental impact assessment reports. Also, thorough reviews by special institutions are essential.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.41-58
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• 2023

As naval warfare changes to composite warfare that includes simultaneous engagements against surface, underwater, and air enemies, performance and tactical analysis are required to respond to naval warfare. In particular, for practical analysis of composite warfare, it is necessary to study engagement simulations that can appropriately utilize the limited performance resources of the detection system. This paper proposes a DEVS (Discrete Event Systems Specifications)-based simulation model for composite warfare analysis. The proposed model contains generalized models of combat platforms and armed objects to simulate various complex warfare situations. In addition, we propose a detection performance allocation algorithm that can be applied to a detection system model, considering the characteristics of composite warfare in which missions must be performed using limited detection resources. We experimented with the effectiveness of composite warfare according to the strength of the detection system's resource allocation, the enemy force's size, and the friendly force's departure location. The simulation results showed the effect of the resource allocation function on engagement time and success. Our model will be used as an engineering basis for analyzing the tactics of warships in various complex warfare situations in the future.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.3
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• pp.267-273
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• 2013

In order to provide effective operating conditions for the fan in a wet cooling tower with film fill, a new program to search for the minimum fan power was developed using a model of the optimal total annual cost of the tower based on Merkel's model. In addition, a type of design map for a cooling tower was also developed. The inlet water temperature and heat load were considered as key parameters. The present program was first validated using several typical examples. The results showed that for a given heat load, a three-dimensional graph of the fan power (z-axis), mass flux of air (x-axis, minimum fan power), and inlet water temperature (y-axis, maximum of minimum fan power) showed a saddle configuration. The minimum fan power increased as the heat load increased. The conventionally known fact that the most effective cooling tower operation coincides with a high inlet water temperature and low air flow rate can be replaced by the statement that there exists an optimum mass flux of air corresponding to a minimum fan power for a given inlet water temperature, regardless of the heat load.

• Journal of Environmental Science International
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• v.23 no.2
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• pp.207-218
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• 2014

A ventilation model was developed for predicting the air change per hour(ACH) in buildings and the airflow rates between zones of a multi-room building. In this model, the important parameters used in the calculation of airflow are wind velocity, wind direction, terrain effect, shielding effect by surrounding buildings, the effect of the window type and insect screening, etc. Also, the resulting set of mass balance equations required for the process of calculation of airflow rates are solved using a Conte-De Boor method. When this model was applied to the building which had been tested by Chandra et al.(1983), the comparison of predicted results by this study with measured results by Chandra et al. indicated that their variations were within -10%~+12%. Also, this model was applied to a building with five zones. As a result, when the wind velocity and direction did not change, terrain characteristics influenced the largest and window types influenced the least on building ventilation among terrain characteristics, local shieldings, and window types. Except for easterly and westerly winds, the ACH increased depending on wind velocity. The wind direction had influence on the airflow rates and directions through openings in building. Thus, this model can be available for predicting the airflow rates within buildings, and the results of this study can be useful for the quantification of airflow that is essential to the research of indoor air quality(temperature, humidity, or contaminant concentration) as well as to the design of building with high energy efficiency.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.5
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• pp.518-526
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• 2012

The objective of this research was to develop fugitive emission models of HFC-134a (Hydrofluorocarbon-134a) at the operation and disposal stages of passenger cars. It is essential to estimate the emission of HFC-134a from mobile air conditioner (MAC) due to its high Global Warming Potential (GWP) and extensive use as a refrigerant in MAC. The first-order emission model was introduced and the emission rate constant was assumed to be unvaried with time. A commercial recovery station of refrigerants was used to recover the HFC-134a from the MAC. Average emission rate constant and annual emission rate during the operation period of vehicle are estimated to be $0.0538{\pm}0.0092$ (n=21) $yr^{-1}$ and $5.2{\pm}0.6%$, respectively within a confidence interval of 95%. According to the model results, about 50% of HFC-134a would be emitted from the MAC during the 10 years operation of passenger cars. On the other hand, average remaining portion of HFC-134a in the MACs of scrap cars is $58.2{\pm}4.8%$ (n=50) within a confidence interval of 95%, suggesting that over 40% of the initially charged amount could be released fugitively after disposal provided that the HFC-134a would not be properly treated or recycled.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.39-46
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• 2020

The utilization of artificial intelligence (AI) in the engagement has been a key research topic in the defense field during the last decade. To pursue this utilization, it is imperative to acquire a realistic simulation to train an AI engagement agent with a synthetic, but realistic field. This paper is a case study of training an AI agent to operate with a hardware realism in the air-warfare dog-fighting. Particularly, this paper models the pursuit of an opponent in the dog-fighting setting with a gun-only engagement. In this context, the AI agent requires to make a decision on the pursuit style and intensity. We developed a realistic hardware simulator and trained the agent with a reinforcement learning. Our training shows a success resulting in a lead pursuit with a decreased engagement time and a high reward.