• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.363-368
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• 2021

Ship engine rooms have improved automation systems owing to the advancement of technology. However, there are many variables at sea, such as wind, waves, vibration, and equipment aging, which cause loosening, cutting, and leakage, which are not measured by automated systems. There are cases in which only one engineer is available for patrolling. This entails many risk factors in the engine room, where rotating equipment is operating at high temperature and high pressure. When the engineer patrols, he uses his five senses, with particular high dependence on vision. We hereby present a preliminary study to implement an engine-room patrol robot that detects and informs the machine room while a robot patrols the engine room. Images of ship engine-room equipment were classified using a convolutional neural network (CNN). After constructing the image dataset of the ship engine room, the network was trained with a pre-trained CNN model. Classification performance of the trained model showed high reproducibility. Images were visualized with a class activation map. Although it cannot be generalized because the amount of data was limited, it is thought that if the data of each ship were learned through transfer learning, a model suitable for the characteristics of each ship could be constructed with little time and cost expenditure.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.699-708
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• 2023

Nuclear power plants (NPPs) consider possible external events, including natural hazards, during the design phase to ensure safe operation. However, in recent years, due to the increasing probability of natural hazards exceeding the design, a careful review of extreme natural hazards and unforeseen external events during the design phase has become necessary. In this study, the objective was to screen potential extreme natural hazards at NPP sites in Korea. Initially, we investigated and analyzed the characteristics of NPP sites and the events caused by external hazards. Furthermore, we analyzed existing literature and research data to establish screening procedures and criteria that suit the actual conditions of domestic NPPs. Based on these criteria and data, we conducted qualitative screening for each NPP site and identified potential extreme natural hazards through quantitative screening and walkdown. As a result of the screening, in addition to internal flooding caused by heavy rain, wind pressure and extreme air pressure caused by extreme winds were screened as potential extreme natural hazards common to all sites. Additionally, at the Kori site, storm surge was selected as the most significant potential extreme natural hazard.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.74-81
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• 2024

In the near future, autonomous ships, ships controlled by shore remote control centers, and ships operated by navigators will coexist and operate the sea together. In the advent of this situation, a method is required to evaluate the safety of the maritime traffic environment. Therefore, in this study, a plan to evaluate the safety of navigation through ship control simulation was proposed in a maritime environment, where ships directly controlled by navigators and autonomous ships coexisted, using autonomous operation technology. Own ship was designed to have autonomous operational functions by learning the MMG model based on the six-DOF motion with the PPO algorithm, an in-depth reinforcement learning technique. The target ship constructed maritime traffic modeling data based on the maritime traffic data of the sea area to be evaluated and designed autonomous operational functions to be implemented in a simulation space. A numerical model was established by collecting date on tide, wave, current, and wind from the maritime meteorological database. A maritime meteorology model was created based on this and designed to reproduce maritime meteorology on the simulator. Finally, the safety evaluation proposed a system that enabled the risk of collision through vessel traffic flow simulation in ship control simulation while maintaining the existing evaluation method.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1071-1079
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• 2020

For integrated water management, it is essential to secure basic data such as the amount of agricultural water intake. The river water intake through the intake weir is carried out through the agricultural irrigation canal, and a method for measuring the quantity of water intake is required to suit the characteristics of the measuring points. In this study, the accuracy of the calculated flow data was determined by applying a microwave water surface current meter. The microwave water surface current meter is a method of calculating surface velocity using doppler effect, which is mainly used in high-velocities situations such as flood. Surface velocity is difficult to represent the average velocity of the entire section at low dicharges or high wind speeds, it is considered to be low in continuous utilization throughout the year, and it is necessary to verify whether the measurement using an microwave water surface curren meter is appropriate in agricultural irrigation canal. The data measured with an microwave water surface curren meter were compared with the actual flow data to calculate the intake data in agricultural irrigation canal. In agricultural irrigation canal, the low-level discharge calculated using an microwave water surface current meter at a minimum velocity of about 0.3 m/s and a minimum discharge of about 1.0 m3/s or higher was found to have a high tendency and accuracy compared to the standard discharge, especially when the high discharge was high. Although effective results can be obtained in terms of quantity at low discharge, it is deemed that subsequent studies are needed to calculate the average discharge of the cross section at low discharge, given that the trend of data is unstable. Through this study, it is suggested that it is appropriate to calculate the amount of water intake through the microwave water surface current meter in artificial waterways with a certain discharge or higher, so it is expected to be widely distributed as a method for measuring river water intake.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.100-119
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• 2020

During recent years, the heat environment and particulate matter (PM₁₀) have become serious environmental problems, as increases in heat waves due to rising global temperature interact with weakening atmospheric wind speeds. There exist urban heat islands and urban pollution islands with higher temperatures and air pollution concentrations than other areas. However, few studies have examined these issues together because of a lack of micro-scale data, which can be constructed from spatial data. Today, with the help of satellite images and big data collected by private telecommunication companies, detailed spatial distribution analyses are possible. Therefore, this study aimed to examine the spatial distribution patterns of urban heat islands and urban pollution islands within Busan Metropolitan City and to compare the distributions of the two phenomena. In this study, the land surface temperature of Landsat 8 satellite images, air temperature and particulate matter concentration data derived from a private automated meteorological observation system were gridded in 30m × 30m units, and spatial analysis was performed. Analysis showed that simultaneous zones of urban heat islands and urban pollution islands included some vulnerable residential areas and industrial areas. The political migration areas such as Seo-dong and Bansong-dong, representative vulnerable residential areas in Busan, were included in the co-occurring areas. The areas have a high density of buildings and poor ventilation, most of whose residents are vulnerable to heat waves and air pollution; thus, these areas must be considered first when establishing related policies. In the industrial areas included in the co-occurring areas, concrete or asphalt concrete-based impervious surfaces accounted for an absolute majority, and not only was the proportion of vegetation insufficient, there was also considerable vehicular traffic. A hot-spot analysis examining the reliability of the analysis confirmed that more than 99.96% of the regions corresponded to hot-spot areas at a 99% confidence level.

• Journal of Environmental Impact Assessment
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• v.24 no.5
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• pp.456-471
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• 2015

Emission source term is one of the strong controlling factors for the air quality simulation capability, particularly over the urban area. Ulsan is an industrial area and frequently required to simulate for environmental assessment. In this study, two CAPSS (Clean Air Policy Support System) emission data; CAPSS-2003 and CAPSS-2010 in Ulsan, were employed as an input data for WRF-CMAQ air quality model for emission assessment. The simulated results were compared with observations for the local emission dominant synoptic conditions which had negative vorticities and lower geostrophic wind speed at 850hPa weather maps. The measurements of CO, $NO_2$, $SO_2$ and $PM_{10}$ concentrations were compared with simulations and the 'scaling factors' of emissions for CO, $NO_2$, $SO_2$, and $PM_{10}$ were suggested in in aggregative and quantitative manner. The results showed that CAPSS-2003 showed no critical discrepancies of CO and $NO_2$ observations with simulations, while $SO_2$ was overestimated by a factor of more than 12, while $PM_{10}$ was underestimated by a factor of more than 20 times. However, CAPSS-2010 case showed that $SO_2$ and $PM_{10}$ emission were much more improved than CAPSS-2003. However, $SO_2$ was still overestimated by a factor of more than 2, and $PM_{10}$ underestimated by a factor of 5, while there was no significant improvement for CO and $NO_2$ emission. The estimated factors identified in this study can be used as'scaling factors'for optimizing the emissions of air pollutants, particularly $SO_2$ and $PM_{10}$ for the realistic air quality simulation in Ulsan.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.161-176
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• 2021

In this study, one of the distributed hydrologic models, VELAS, was used to analyze the variation of hydrologic elements based on water balance analysis to evaluate the groundwater recharge in more detail than the annual time scale for the past and future. The study area is located in Yanggok-ri, Seobu-myeon, Hongseong-gun, Chungnam-do, which is very vulnerable to drought. To implement the VELAS model, spatial characteristic data such as digital elevation model (DEM), vegetation, and slope were established, and GIS data were constructed through spatial interpolation on the daily air temperature, precipitation, average wind speed, and relative humidity of the Korea Meteorological Stations. The results of the analysis showed that annual precipitation was 799.1-1750.8 mm, average 1210.7 mm, groundwater recharge of 28.8-492.9 mm, and average 196.9 mm over the past 18 years from 2001 to 2018 in the study area. Annual groundwater recharge rate compared to annual precipitation was from 3.6 to 28.2% with a very large variation and average 14.9%. By the climate change RCP 8.5 scenario, the annual precipitation from 2019 to 2100 was 572.8-1996.5 mm (average 1078.4 mm) and groundwater recharge of 26.7-432.5 mm (average precipitation 16.2%). The annual groundwater recharge rates in the future were projected from 2.8% to 45.1%, 18.2% on average. The components that make up the water balance were well correlated with precipitation, especially in the annual data rather than the daily data. However, the amount of evapotranspiration seems to be more affected by other climatic factors such as temperature. Groundwater recharge in more detailed time scale rather than annual scale is expected to provide basic data that can be used for groundwater development and management if precipitation are severely varied by time, such as droughts or floods.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.