• Journal of Space Technology and Applications
• /
• v.2 no.1
• /
• pp.41-51
• /
• 2022

CubeSat is a satellite platform that is widely used not only for earth observation but also for space exploration. CubeSat is also used in magnetic field investigation missions to observe space physics phenomena with various shape configurations of magnetometer instrument unit. In case of magnetic field measurement, the magnetometer instrument should be far away from the satellite body to minimize the magnetic disturbances from satellites. But the accommodation setting of the magnetometer instrument is limited due to the volume constraint of small satellites like a CubeSat. In this paper, we investigated that the magnetic field interference generated by the cube satellite was analyzed how much it can affect the reliability of magnetic field measurement. For this analysis, we used a reaction wheel and Torque rods which have relatively high-power consumption as major noise sources. The magnetic dipole moment of these parts was derived by the data sheet of the manufacturer. We have been confirmed that the effect of the residual moment of the magnetic torque located in the middle of the 3U cube satellite can reach 36,000 nT from the outermost end of the body of the CubeSat in a space without an external magnetic field. In the case of accurate magnetic field measurements of less than 1 nT, we found that the magnetometer should be at least 0.6 m away from the CubeSat body. We expect that this analysis method will be an important role of a magnetic cleanliness analysis when designing a CubeSat to carry out a magnetic field measurement.

• Journal of the Korean Institute of Gas
• /
• v.26 no.4
• /
• pp.27-35
• /
• 2022

When LPG leaks from the storage tank, the gas try to sink to the ground because LPG is heavier than air. The gas easily creates vapor clouds causing aggressive accidents in no airflow. Therefore, It is important to prevent in advance by analyzing the damaged range caused from LPG leakage and vapor clouds. So, this study analyzed the range of damaged by LPG leakage and vapor clouds with consideration of the cold air flow which is generated by the topographical characteristics and the land use status at night time in the Jeju Hagari. As a result of the cold air flow using KLAM_21, about 2 m/s of cold air was introduced in from the southeast due to the influence of the terrain. The range of damaged by LPG leakage and vapor cloud was analyzed using ALOHA. When the leak hole size is 10 cm at the wind speed of 2 m/s, the range corresponding to LEL 60 % (12,600 ppm) was 61 m which range is expected to influence in nearby residential areas. These results of this study can be used as basic data to prepare preventive measures of accidents caused by vapor cloud. Forward, it is necessary to apply CFD modeling such as FLACS to check the vapor cloud formation due to LPG leakage in a relatively narrow area and to check the cause analysis.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.6
• /
• pp.54-63
• /
• 2022

This paper is a study on the friction damper, which is one of the seismic reinforcement devices for structures. This study developed a damper by replacing the internal friction material with ultra high molecular weight polyethylene (UHMWPE), a type of composite material. In addition, this study applied a multi-friction method in which the internal structure where frictional force is generated is laminated in several layers. To verify the performance of the developed multi-friction damper, this study performed a characteristic analysis test for the basic physical properties, wear characteristics, and disc springs of the material. As a result of the wear test, the mass reduction rate of UHMWPE was 0.003%, which showed the best performance among the friction materials based on composite materials. Regarding the disc spring, this study secured the design basic data from the finite element analysis and experimental test results. Moreover, to confirm the quality stability of the developed multi-friction damper, this study performed an seismic load test on the damping device and the friction force change according to the torque value. The quality performance test result showed a linear frictional force change according to the torque value adjustment. As a result of the seismic load test, the allowable error of the friction damper was less than 15%, which is the standard required by the design standards, so it satisfies the requirements for seismic reinforcement devices.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.6
• /
• pp.93-101
• /
• 2022

RC Slab bridges in Korea account for more than 70% of the total bridges for more than 20 years of service. As the number of aging structures increases, the importance of safety diagnosis and maintenance of structures increases. For highway bridges, cracks are a main cause of deck deterioration, which is very closely related to the decrease in bridge durability and service life. In addition, the damage rate of expansion joints and bearings accounts for approximately 73% higher than that of major members. Therefore, this study defined damage scenarios combined with devices damages and deck deterioration. The stress distribution and maximum stress on the deck were then evaluated using design vehicle load and daily temperature gradient for single and combined damage scenarios. Furthermore, this study performed damage-spread analysis and predicted condition ratings according to a deck deterioration model generated from the inspection and diagnosis history data of cracks. The heterogeneous damages combined with the member damages of expansion joints and bearings increased the rate of crack area and damage spread, which accelerated the time to reach the condition rating of C. Therefore, damage to bridge members requires proper and prompt repair and replacement, and otherwise it can cause the damage to bridge deck and the spread of the damage.

• Journal of the Korean Society of Radiology
• /
• v.16 no.1
• /
• pp.15-23
• /
• 2022

3D printing technology is being used in various fields such as medicine and biotechnology, and materials containing metal powder are being commercialized through recent material development. Therefore, this study intends to analyze the photon spectrum during added filtration using 3D printing material during diagnostic X-ray examination through simulation. Among the Monte Carlo techniques, MCNPX (ver. 2.5.0) was used. First, the appropriateness of the photon spectrum generated in the simulation was evaluated through SRS-78 and SpekCalc, which are X-ray spectrum generation programs in the diagnostic field. Second, photon spectrum the same thickness of Al and Cu filters were obtained for characterization of 3D printing materials containing metal powder. In addition, the total photon fluence and average energy according to changes in tube voltage were compared and analyzed. As a result, it was analyzed that PLA-Al required about 1.2 ~ 1.4 times the thickness of the existing Al filter, and PLA-Cu required about 1.4 ~ 1.7 times the thickness of the Cu filter to show the same degree of filtration. Based on this study in the future, it is judged that it can be utilized as basic data for manufacturing 3D printing additional filters in medical fields.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.569-575
• /
• 2022

It is most desirable to build a crane training program in the same evvironment as the actual port, but it has problem such as time constraint and cost. To overcome these limitations, next-generation training programs based on AR/VR are receiving a lot of attention. In this paper, a prototype of a harbor crane training system based on virtual reality was designed and implemented. The system implemented in this paper consists of two elements: an Arduino-based IoT terminal and an HMD equipped with a Unity application program. The IoT terminal consists of 2 controllers, 2 toggle switches, and 8 button switches to process data generated according to the user's operation. The HMD uses Oculus Quest2 and is connected to the IoT terminal through wireless communication to provide user convenience. The training system implemented in this paper is expected to provide trainees with a training environment independent of time and place through virtual reality and to save time and money.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.8
• /
• pp.339-346
• /
• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.99-110
• /
• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Journal of the Korean earth science society
• /
• v.44 no.2
• /
• pp.135-147
• /
• 2023

Satellite-observed significant wave heights (SWHs), which are widely used to understand the response of the ocean to climate change, require long-term and continuous validation. This study examines the accuracy and error characteristics of SWH observed by nine satellite altimeters in the North Pacific and North Atlantic Ocean for 25 years (1992-2016). A total of 137,929 matchups were generated to compare altimeter-observed SWH and in-situ measurements. The altimeter SWH showed a bias of 0.03 m and a root mean square error (RMSE) of 0.27 m, indicating relatively high accuracy in the North Pacific and North Atlantic Ocean. However, the spatial distribution of altimeter SWH errors showed notable differences. To better understand the error characteristics of altimeter-observed SWH, errors were analyzed with respect to in-situ SWH, time, latitude, and distance from the coast. Overestimation of SWH was observed in most satellite altimeters when in-situ SWH was low, while underestimation was observed when in-situ SWH was high. The errors of altimeter-observed SWH varied seasonally, with an increase during winter and a decrease during summer, and the variability of errors increased at higher latitudes. The RMSEs showed high accuracy of less than 0.3 m in the open ocean more than 100 km from the coast, while errors significantly increased to more than 0.5 m in coastal regions less than 15 km. These findings underscore the need for caution when analyzing the spatio-temporal variability of SWH in the global and regional oceans using satellite altimeter data.

• Advanced Industrial SCIence
• /
• v.2 no.3
• /
• pp.22-28
• /
• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.