• Economic Analysis
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• v.27 no.3
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• pp.1-32
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• 2021

This study empirically analyzes whether bank capital channel and risk-taking channel for monetary policy work for domestic banks in South Korea by analyzing the impact of the expansionary monetary policy on the rate spread between deposit and loan, capital ratio, and loan amount. For the empirical analysis, the Uhlig (2005)'s sign-restricted SVAR(Structural Vector Auto-Regression) model is used. The empirical results are as follows: the bank's interest rate margin increases, the capital ratio improves, risk-weighted asset ratio increases, and the amount of loans increases in response to expansionary monetary shock. This empirical results confirm that bank capital channel and risk-taking channel work in domestic banks, similar to the previous research results. The implications of this study are as follows. Although the expansionary monetary policy has the effect of improving the bank's financial soundness and profitability in the short term as bank capital channel works, it could negatively affect the soundness of banks by encouraging banks to pursue risk in the long run as risk-taking channel works. It is necessary to note that the capital ratio according to the BIS minimum capital requirement of individual banks may cause an illusion in supervising the soundness of the bank. So, the bank's aggressive lending expansion may lead to an inherent weakness in the event of a crisis. Since the financial authority may have an illusion about the bank's financial soundness if the low interest rate persists, the authority needs to be actively interested in stress tests and concentration risk management in the pillar 2 of the BIS capital accord. In addition, since system risk may increase, it is necessary to conduct regular stress tests or preemptive monitoring of assets concentration risk.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.225-231
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• 2006

The nonpoint source control is based on TPLMS (Total Pollution Load Management System) program. Recently, the Ministry of Environment in Korea has programed TPLMS for 4 major large rivers to improve the water quality in rivers by controling the total pollutant loadings from the watershed area. Usually the urbanization is the main pollutant sources, particularly for nonpoint pollutants, because of high imperviousness and high pollutant mass emissions. The stormwater runoff from urban areas is containing various pollutants such as sediments, metals and toxic chemicals due to human and vehicle activities. Of the various landuses, the highways are highly polluted landuses because of high pollutant accumulation rate by vehicle activities during dry periods. Therefore, this research is achieved to provide pollutant EMCs (Event Mean Concentrations) and mass loadings washed-off from highways during rainfall periods. Five monitoring locations were equipped with an automatic rainfall gage and an flow meter. The results show that the EMC ranges for 95% confidence intervals in highway land use are 45.52-125.76 mg/L for TSS, 52.04-95.48 mg/L for COD, 1.77-4.48 mg/L for TN, 0.29-0.54 mg/L for TP. The ranges of washed- off mass loading are $712.7-2,418.4mg/m^2$ for TSS and $684.1-1,779.6mg/m^2$ for COD.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Atmosphere
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• v.34 no.3
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• pp.283-304
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• 2024

This study analyzed the variability and correlation between ground-level air pollutant concentrations and the atmospheric mixing layer height using data from four types of air pollutants (PM_2.5, PM₁₀, NO₂, and O₃) collected at AirKorea monitoring stations nationwide over a five-year period (2018~2022), and aerosol backscatter data observed by the Vaisala CL31 to derive atmospheric mixing layer heights. The five-year trends and variability of ground-level air pollutant concentrations under seasonal and hourly conditions were examined, as well as the seasonal distribution and diurnal variation of the atmospheric mixing layer height. Five correlation coefficient methodologies were applied to analyze the correlations between ground-level air pollutants and atmospheric mixing layer height under various seasonal and hourly conditions, confirming the dilution effect of the atmospheric mixing layer height. The results showed that PM_2.5, PM₁₀, and NO₂ generally had negative correlations with the atmospheric mixing layer height, while O₃ showed a strong positive correlation up to an altitude of 1,200~1,500 meters, and a negative correlation beyond that altitude. It was also shown that a single high concentration event (e.g., PM₁₀) can alter the overall correlation. The correlation can also vary depending on the characteristics of the correlation coefficient methodology, highlighting the importance of applying the appropriate methodology for each case during the analysis process.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.1-15
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• 2002

Atmospheric aerosols interact with sunlight and affect the global radiation balance that can cause climate change through direct and indirect radiative forcing. Because of the spatial and temporal uncertainty of aerosols in atmosphere, aerosol characteristics are not considered through GCMs (General Circulation Model). Therefor it is important physical and optical characteristics should be evaluated to assess climate change and radiative effect by atmospheric aerosols. In this study GMS-5 satellite data and surface measurement data were analyzed using a radiative transfer model for the Yellow Sand event of April 7~8, 2000 in order to investigate the atmospheric radiative effects of Yellow Sand aerosols, MODTRAN3 simulation results enable to inform the relation between satellite channel albedo and aerosol optical thickness(AOT). From this relation AOT was retreived from GMS-5 visible channel. The variance observations of satellite images enable remote sensing of the Yellow Sand particles. Back trajectory analysis was performed to track the air mass from the Gobi desert passing through Korean peninsular with high AOT value measured by ground based measurement. The comparison GMS-5 AOT to ground measured RSR aerosol optical depth(AOD) show that for Yellow Sand aerosols, the albedo measured over ocean surfaces can be used to obtain the aerosol optical thickness using appropriate aerosol model within an error of about 10%. In addition, LIDAR network measurements and backward trajectory model showed characteristics and appearance of Yellow Sand during Yellow Sand events. These data will be good supporting for monitoring of Yellow Sand aerosols.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.1
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• pp.16-37
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• 2018

In this study, difference in chemical composition of $PM_{2.5}$ observed between the year 2013 and 2015 at six air quality intensive monitoring stations (Bangryenogdo (BR), Seoul (SL), Daejeon (DJ), Gwangju (GJ), Ulsan (US), and Jeju (JJ)) was investigated and the possible factors causing their difference were also discussed. $PM_{2.5}$, organic and elemental carbon (OC and EC), and water-soluble ionic species concentrations were observed on a hourly basis in the six stations. The difference in chemical composition by regions was examined based on emissions of gaseous criteria pollutants (CO, $SO_2$, and $NO_2$), meteorological parameters (wind speed, temperature, and relative humidity), and origins and transport pathways of air masses. For the years 2013 and 2014, annual average $PM_{2.5}$ was in the order of SL ($${\sim_=}DJ$$)>GJ>BR>US>JJ, but the highest concentration in 2015 was found at DJ, following by GJ ($${\sim_=}SJ$$)>BR>US>JJ. Similar patterns were found in $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$. Lower $PM_{2.5}$ at SL than at DJ and GJ was resulted from low concentrations of secondary ionic species. Annual average concentrations of OC and EC by regions had no big difference among the years, but their patterns were distinct from the $PM_{2.5}$, $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$ concentrations by regions. 4-day air mass backward trajectory calculations indicated that in the event of daily average $PM_{2.5}$ exceeding the monthly average values, >70% of the air masses reaching the all stations were coming from northeastern Chinese polluted regions, indicating the long-range transportation (LTP) was an important contributor to $PM_{2.5}$ and its chemical composition at the stations. Lower concentrations of secondary ionic species and $PM_{2.5}$ at SL in 2015 than those at DJ and GJ sites were due to the decrease in impact by LTP from polluted Chinese regions, rather than the difference in local emissions of criteria gas pollutants ($SO_2$, $NO_2$, and $NH_3$) among the SL, DJ, and GJ sites. The difference in annual average $SO{_4}^{2-}$ by regions was resulted from combination of the difference in local $SO_2$ emissions and chemical conversion of $SO_2$ to $SO{_4}^{2-}$, and LTP from China. However, the $SO{_4}^{2-}$ at the sites were more influenced by LTP than the formation by chemical transformation of locally emitted $SO_2$. The $NO_3{^-}$ increase was closely associated with the increase in local emissions of nitrogen oxides at four urban sites except for the BR and JJ, as well as the LTP with a small contribution. Among the meterological parameters (wind speed, temperature, and relative humidity), the ambient temperature was most important factor to control the variation of $PM_{2.5}$ and its major chemical components concentrations. In other words, as the average temperature increases, the $PM_{2.5}$, OC, EC, and $NO_3{^-}$ concentrations showed a decreasing tendency, especially with a prominent feature in $NO_3{^-}$. Results from a case study that examined the $PM_{2.5}$ and its major chemical data observed between February 19 and March 2, 2014 at the all stations suggest that ambient $SO{_4}^{2-}$ and $NO_3{^-}$ concentrations are not necessarily proportional to the concentrations of their precursor emissions because the rates at which they form and their gas/particle partitioning may be controlled by factors (e.g., long range transportation) other than the concentration of the precursor gases.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.173-184
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• 2019

BACKGROUND: This study was carried out to investigate pesticide residues from fifty streams in Korea. Water samples were collected at two times. Thee first sampling was performed from april to may, which was the season for start of pesticide application and the second sampling event was from august to september, which was a period for spraying pesticides multiple times. METHODS AND RESULTS: The 136 pesticide residues were analyzed by LC-MS/MS and GC/ECD. As a result, eleven of the pesticide residues were detected at the first sampling. Twenty eight of the pesticide residues were detected at the second sampling. Seven pesticides were frequently detected from more than 10 water samples. Ecological risk assessment (ERA) was carried out by using residual and toxicological data. Four scenarios were applied for the ERA. Scenario 1 and 2 were performed using LC50 values and mean and maximum concentrations. Scenarios 3 and 4 were conducted by NOEC values and mean and maximum concentrations. CONCLUSION: Frequently detected pesticide residues tended to coincide with the period of preventing pathogen and pest at paddy rice. As a result of ERA, five pesticides (butachlor, carbendazim, carbofuran, chlorantranilprole, and oxadiazon) were assessed to be risks at scenario 4. However, only oxadiazon was assessed to be a risk at scenario 3 for the first sampling. Oxadiazon was not assessed to be a risk at the second sampling. It seems to be temporary phenomenon at the first sampling, because usage of herbicides such as oxadiazon increased from April to march for preventing weeds at paddy fields. However, this study suggested that five pesticides which were assessed to be risks need to be monitored continuously for the residues.

• Journal of the Korea Institute of Building Construction
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• v.9 no.5
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• pp.79-86
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• 2009

In a steel structure, spray-applied fire resistive materials are crucial in preventing structural strength from being weakened in the event of a fire. The quality control of such materials, however, is difficult for manual workers, who can frequently be in short supply. These skilled workers are also very likely to be exposed to environmental hazards. Problems with construction work such as this, which are specifically the difficulty of achieving quality control and the dangerous nature of the work itself, can be solved to some degree by the introduction of automated equipment. It is, however, very difficult to automate the work process, from operation to the selection of a location for the equipment, as the environment of a construction site has not yet been structured to accommodate automation. This is a fundamental study on the possibility of the automation of spray-applied fire resistive coating work. In this study, the linkability of the cutting-edge RTLS to an automation system is reviewed, and a scenario for the automation of spray-applied fire resistive coating work and system composition is presented. The system suggested in this study is still in a conceptual stage, and as such, there are many restrictions still to be resolved. Despite this fact, automation is expected to have good effectiveness in terms of preventing fire from spreading by maintaining a certain level of strength at a high temperature when a fire occurs, as it maintains the thickness of the fire-resistive coating at a specified level, and secures the integrity of the coating with the steel structure, thereby enhancing the fire-resistive performance. It also expected that if future research is conducted in this area in relation to a cutting-edge monitoring TRS, such as the ubiquitous sensor network (USN) and/or building information model (BIM), it will contribute to raising the level of construction automation in Korea, reducing costs through the systematic and efficient management of construction resources, shortening construction periods, and implementing more precise construction