• Journal of Environmental Science International
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• v.31 no.10
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• pp.815-824
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• 2022

It is necessary to understand the amount of snowfall and area of snow cover of Mt. Halla to ensure the safety of mountaineers and to protect the ecosystem of Mt. Halla against climate change. However, there are not enough related studies and observation posts for monitoring snow load. Therefore, to supplement the insufficient data, this study proposes an analysis of snow load and snow cover using normalized-difference snow index. Using the images obtained from the Sentinel2 satellite, the normalized-difference snow index image of Mt. Halla could be acquired. This was examined together with the meteorological data obtained from the existing observatory to analyze the change in snow cover for the years 2020 and 2021. The normalized-difference snow index images showed a smaller snow pixel number in 2021 than that in 2020. This study concluded that 2021 may have been warmer than 2020. In the future, it will be necessary to continuously monitor the amount of snow and the snow-covered area of Mt. Halla using the normalized-difference snow index image analysis method.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1189-1198
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• 2021

When performing remote tasks using robots in nuclear power plants, a 3D shape measurement system is advantageous in improving the efficiency of remote operations by easily identifying the current state of the target object for example, size, shape, and distance information. Nuclear power plants have high-radiation and underwater environments therefore the electronic parts that comprise 3D shape measurement systems are prone to degradation and thus cannot be used for a long period of time. Also, given the refraction caused by a medium change in the underwater environment, optical design constraints and calibration methods for them are required. The present study proposed a method for developing an underwater 3D shape measurement system with improved radiation tolerance, which is composed of commercial electric parts and a stereo camera while being capable of easily and readily correcting underwater refraction. In an effort to improve its radiation tolerance, the number of parts that are exposed to a radiation environment was minimized to include only necessary components, such as a line beam laser, a motor to rotate the line beam laser, and a stereo camera. Given that a signal processing circuit and control circuit of the camera is susceptible to radiation, an image sensor and lens of the camera were separated from its main body to improve radiation tolerance. The prototype developed in the present study was made of commercial electric parts, and thus it was possible to improve the overall radiation tolerance at a relatively low cost. Also, it was easy to manufacture because there are few constraints for optical design.

• Informatization Policy
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• v.29 no.4
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• pp.3-25
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• 2022

The governance structure of public broadcasters is the hottest topic in domestic media policy. This study examines the flow of discussions by period by synthesizing the research literature on the governance structure of domestic public broadcasting since 2000. As a result, active discussion on the governance structure of public broadcasting was confirmed to have appeared following the conservative governments after 2008, derived from the "crisis theory of public broadcasting." In addition, the discussion was conducted centered on the public values of public broadcasting and direction of system improvement, political independence of public broadcasting, composition of the board of directors, and presidential election system. This study suggested that future discussions on the governance structure of public broadcasting should expand the existing discussion of political independence to encompass the values, responsibilities, and systems of public broadcasting that presuppose citizen participation.

• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.65-72
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• 2022

The Internet of Things (IoT) provides data convergence and sharing functions, and IoT technology is the most fundamental core technology in creating new services by convergence of various cutting-edge technologies. However, there are different classification systems for the Internet of Things, and when it is limited to the domestic public sector, it is difficult to properly grasp the current status of which devices are installed and operated with what share, and systematic data or research The results are very difficult to find. Therefore, in this study, the relevance of the classification system for IoT devices was analyzed according to reality based on sales, shipments, and growth rate, and based on this, the actual share of IoT devices among domestic public institutions was analyzed in detail. The derived detailed analysis results are expected to be efficiently utilized in the process of selecting IoT devices for research and analysis to advance information protection technology such as responding to malicious code attacks on IoT devices, analyzing incidents, and strengthening security vulnerabilities.

• Journal of Korean Port Research
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• v.9 no.2
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• pp.9-24
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• 1995

The intensification of the competition among container terminals has led to important affect such as, decision making or aggressive intervention of customers for terminal operation. Therefore, in case of developed ports, the large transition of port operation is composed of the press of terminal customer than decision making of terminal manager. Overall port tariffs for using terminal is made out by the conference of terminal customers than the supreme headquarters of terminal operation, and the related investment of terminal has been made according to requisition or proposal of customers. Therefore, among decision making problems that shippers, shipping companies, and freight forwarder among decision making problems that shippers, shipping companies, and freight forwarder face, the choice of the container terminal is one of the most important problems. So, the decision making of the users seems to have a significant impact on the competitive power of container terminals. The main objective of this study is to design port marketing strategy for strengthening the competitive power of container terminals. The results of this study were found as follows: Firstly, port authority should establish user-oriented operation policy of terminal as the means of activating the opinion window, using terminal monitoring system(TMS). Secondly, terminal planning and development of government should be made to minimize the lead time, to induce the civil capital and to utilize the economies of scale. Thirdly, port authority needs to endeavor to analyze the information of competing foreign terminals as well to promote the concentrated marketing for the terminal on the users, to train the expert and to develop the new port charge system. Fourthly, to improve the competitive power of the container terminal, Port Authority should optimize the subsystems related to port marketing, far more these systems should be joined organically to work effectively. Finally, port authority system should be introduced, Namely, port should have the enterprise inclination as well s the public inclination.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.587-595
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• 2008

Many studies on bus scheduling optimization have been done from the 1960s to recent years for establishing rational bus schedule plan that can improve convenience of bus passengers and minimize unnecessary runs. After 2000, as part of the Intelligent Transport Systems (ITS), the importance of the schedule management and providing schedule information through bus schedule optimization has become a big issue, and much research is being done to develop optimization models that will increase bus passenger convenience and, on the side of bus management, minimize unnecessary bus operation. The purpose of this study is to calculate the optimal bus frequency and create a timetable for each bus stop by applying DTR or DTRC model that use data for each bus stop and route segment. Model verification process was implemented using data collected from bus management system (BMS) and integrated transit-fare card system for bus route of Seoul's No. 472 line. In order to evaluate the reliability and uncertainty of optimal solution, sensitivity analysis was implemented for the various parameters and assumptions used in the bus scheduling model.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.13-24
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• 2023

Due to recent climate change, the amount of rainfall during the summer season in South Korea has been decreasing, leading to an increase in areas affected by frequent droughts. Droughts have the characteristic of occurring over a wide area and being unpredictable in terms of their onset and end, necessitating proactive research to cope with them. In this study, we conducted an assessment of agricultural drought vulnerability in Taean-gun, Chungcheongnam-do, focusing on irrigation facilities and paddy fields. The assessment criteria were meteorological impact, drought occurrence status, supplementary water supply capacity, and drought response capability, with nine specific indicators selected. The drought response capability was analyzed by applying a scoring system as a key component of the agricultural drought vulnerability assessment, while the other indicators were quantified using an entropy weighting technique. The results of the assessment showed that Anmyeon-eup and Taean-eup were the safest areas, while Wonbuk-myeon, Nam-myeon, and Gonam-myeon were the most vulnerable. It is expected that the findings can be utilized to enhance understanding and proactive measures for coping with agricultural drought, and to determine the priority of drought response in different regions.