• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.235-244
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• 2022

In this study, a class operation case of an engineering design project targeting a golf ball dispenser, a commercial product, was presented. The project was carried out according to the systematic engineering design approach suggested by Kim Jong-won and W. Beitz. This method broadly divides engineering design into four stages: 'product planning → conceptual design → basic design → detailed design'. In particular, the conceptual design stage is divided into 'functional structure diagram → detailed working principle exploration → various design alternatives creation → optimal design selection'. In the conceptual design, the input/output of the golf ball dispenser was defined and a functional structure diagram was prepared for it. Through this process, it was possible to subdivide the functions of the product and to easily explore the working principle for each. The searched working principles are devised as various design alternatives by various combinations, and for each proposal, the advantages and disadvantages were compared with each other to derive the optimal design alternative. In the basic design, the prototype layout was completed through failure mode analysis and the actual prototype was manufactured using it. Through the entire process, students participating in the class will be able to design commercial products in a systematic way and experience manufacturing prototypes within the department of mechanical engineering curriculum.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.17-23
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• 2009

Due to the rapid increase of vehicles on the street, Korean society is facing worsening traffic congestions and air pollutions. Also, the oil price pickup has led to increasing need for the use of public transportation. In particular, transfering among public transportation may be a main factor for riders who are commuting for a long distance journey. In order to ensure such connectivity, transfer stations have been actively built in Korea. However, it would be necessary to shift those vehicles, from cars to public transportations by enhancing the users' satisfaction with public transportation through strategies for minimizing the users' waiting cost by building an efficient connective system between transportation modes as well as the preparation of aforementioned transfer stations. Therefore, this study aimed to develop an algorithm for minimizing transferring passengers' waiting costs based on service intervals of linked buses within the transfer facilities. In order to adjust the service interval, we calculated the total costs, involving the wait cost of transfer passengers and bus operation costs, and produced an allocation interval, that would minimize the costs. We selected a KTX departing from Seoul station, and a No. 6014 bus route in Gwangmyeong city where it starts from the Gwangmyeong station in order to for verifying the model. Then, the transfer passengers' total waitting cost was reduced equivalent to the maximum of 212 minutes, and it revealed that the model performed very effectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1D
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• pp.27-36
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• 2010

Recently, the management of metro business in large cities has become more difficult because of increased construction and operation costs. The purpose of this paper presents the construction of transfer system to resolve about recent tendency to decrease of metro-users and diminution of use efficiency which are serious problems of Busan metro. To cope with this situation, it is necessary to examine the methods of obtaining returns on development profits of land value rises that occur due to transfer system construction between Busan metro line #1 and line #2 in Yangsan area. Therefore, it was made use of research on metro utilization to presuppose service improvement, as an alternative, in the transfer system construction between metro and metro which might be powerful influence over metro-users. In this research, it was examined the actual situation of rises in land values brought about by the transfer system construction of metro line #1 and line #2 in Yangsan area with application of four (4) methods, and have calculated a basis of the development profits produced by the transfer system construction of metro line. According to the economical efficiency analysis, the total construction cost amount to 4,827.1 billion won of case #1 based on single track, and evaluate economically as B/C to 1.013, NPV to 72.7 billion, IRR to 5.614 percent.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.63-72
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• 2009

This study aims at establishing the decision-making support system for the highway assets, long-term performance presumption and evaluation of asset value, which are appropriate for Korea, and proposing the methods of the optimal engineering method and the timing decision for the preventive maintenance through the project evaluation, the optimization method and life-cycle analysis related to the highways. In order to supplement the current problem of the near-sighted budget management system, which chooses the maintenance place of the highway, depending on the level of the budget with fixed amount, the long-term required budget prediction system and the economy principle were introduced, so that the pavement agency can predict the level of the required budget, and it was aimed to develop the pavement asset evaluation system to maintain the performance of the highway with the minimum of the cost. In the use of the highway pavement asset evaluation system, to maintain the appropriate level of the pavement evaluation index, when the budget was efficiently established in the reference of the required maintenance budget for the chosen section of the highway in the year concerned, it was possible to analyze the most rational pavement maintenance budget. With this result, it is estimated to prevent the unnecessary waste of budget in advance, and through the development of the decision-making system for the long-term performance presumption and the asset value estimation of the pavement, it is expected to able to analyze the previous evaluation of the project related to the highway and the feasibility of introduction.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.237-250
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• 2023

Orthophotos and DEMs were generated by UAV-based aerial photogrammetry and an attempt was made to apply them to detailed investigations for the production of flood traces. The cultivated area located in Goa-eup, Gumi, where the embankment collapsed and inundated inundation occurred due to the impact of 6th Typhoon Sanba in 2012, was selected as rhe target area. To obtain optimal accuracy of UAV photogrammetry performance, the UAV images were taken under the optimal placement of 19 GCPs and then point cloud, DEM, and orthoimages were generated through image processing using Pix4Dmapper software. After applying CloudCompare's CSF Filtering to separate the point cloud into ground elements and non-ground elements, a finally corrected DEM was created using only non-ground elements in GRASS GIS software. The flood level and flood depth data extracted from the final generated DEM were compared and presented with the flood level and flood depth data from existing data as of 2012 provided through the public data portal site of the Korea Land and Geospatial Informatix Corporation(LX).

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.65-76
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• 2024

With the recent activation of the offshore wind power industry, there has been a development of power plants with a scale exceeding 400MW, comparable to traditional thermal power plants. Renewable energy, characterized by intermittency depending on the energy source, is a prominent feature of modern renewable power generation facilities, which are structured based on controllable inverter technology. As the integration of renewable energy sources into the grid expands, the grid codes for power system connection are progressively becoming more defined, leading to active discussions and evaluations in this area. In this paper, we propose a method for selecting optimal reactive power compensation capacity when multiple offshore wind farms are integrated and connected through a shared interconnection facility to comply with grid codes. Based on the requirements of the grid code, we analyze the reactive power compensation and excessive stability of the 400MW wind power generation site under development in the southwest sea of Jeonbuk. This analysis involves constructing a generation site database using PSS/E (Power System Simulation for Engineering), incorporating turbine layouts and cable data. The study calculates reactive power due to charging current in internal and external network cables and determines the reactive power compensation capacity at the interconnection point. Additionally, static and dynamic stability assessments are conducted by integrating with the power system database.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.