• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.23-31
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• 2019

The design requirements of modern satellites vary depending on the purpose of operation. Like conventional medium and large-scale satellites, small satellites which operate on low orbit may also serve military purposes. As a result, there is increased demand for high-resolution photos and videos and multi-target observation becomes important. The most important design parameter for multi-target observation is the satellites' maneuverability. For increased maneuverability, the miniaturization is required to increase the stiffness of the satellite as this decreases the mass moment of inertia of the satellite. In the case of a solar panel having relatively low stiffness compared to the satellites' body, vibrations are generated when the attitude maneuver is performed, which greatly influences the image acquisition. For verification of such vibrational characteristics, the satellites is modeled as a reduced model, and experimental zig for simulating attitude maneuver is introduced. A rigidity simulator for simulating the stiffness of the satellite is also proposed. Additionally, the objective of the experimental method is to simulate the maneuvering angle of the satellite based on the winding length of the wire using a step motor, and to experimentally verify the vibration characteristics of the satellite body and the solar panel generated during the maneuvering test.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.63-71
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• 2019

The two-fluid model proposed by Herman and Prigogine is useful for analyzing macroscopic traffic flow in a network. The two-fluid model is used for analyzing a network through the relationship between the ratio of stopped vehicles and the average moving speed of the network, and the two-fluid model has also been applied in the urban transportation network where many signalized or unsignalized intersections existed. In general, the average travel speed and moving speed of a network decrease, and the ratio of stopped vehicles and low speed vehicles in network increase as the traffic demand increases. This study proposed the two-fluid model considering congested and uncongested traffic situations. The critical velocity and the weight factor for congested situation are calibrated by minimizing the root mean square error (RMSE). The critical speed of the Seoul network was about 34 kph, and the weight factor of the congestion on the network was about 0.61. In the proposed model, $R^2$ increased from 0.78 to 0.99 when compared to the existing model, suggesting that the proposed model can be applied in evaluating network performances or traffic signal operations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.33-39
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• 2021

As the interest and demand in the recycled yarn field has increased rapidly worldwide, domestic companies are also promoting research and development and business on recycled yarn. The chemical and thermal properties of four types of virgin and recycled PET samples from A and B company, which are the leading domestic companies in the recycled polyester yarn business, were confirmed through infrared (FT-IR) spectroscopy and differential scanning calorimetry (DSC). Virgin and recycled PET from two companies were compared. FT-IR spectroscopy revealed the typical spectra of PET for both companies and a different peak at 872 cm-1. DSC confirmed that the melting point and crystallization temperature of recycled PET were lower than those of virgin PET. These results indicate that small amounts of contaminants are an important parameter affecting the thermal properties of recycled PET. In the DSC results after seven repeats of the heating and cooling processes, all four samples showed that a lower melting point, crystallization temperature, and low heat flow intensity increased with increasing number of cycles. The results of melting and crystallization enthalpy also showed similar patterns.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.175-182
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• 2021

There have been many instances of damage to buildings with soft stories, and it is important to consider vertically irregular buildings when evaluating the seismic performance of existing buildings. However, because conventional methods do not easily reflect vertical irregularities with sufficient accuracy, it is possible to underestimate or overestimate the seismic performance of buildings with vertical irregularities. This study aims to develop a seismic performance evaluation method for vertically irregular buildings using the stiffness-based soft story ratio (SSR), which is a parameter that represents the ratio of the demand and the capacity for displacement and refers to the ratio of displacement concentration in buildings. The seismic performance evaluation method developed in this study is compared with the conventional seismic performance evaluation method for four piloti buildings, using the first-floor column as a variable. Conventional seismic performance evaluation methods often overestimate the seismic performance for models in which vertical irregularities are maximized. However, results of the proposed seismic performance evaluation method are identical to those from a detailed evaluation for all models. Therefore, it is considered that the proposed seismic performance evaluation method can provide more precise seismic performance evaluation results than conventional methods in the case of piloti buildings, where vertical irregularities are maximized.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.107-118
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• 2022

The social need for stable hydrogen storage technologies that respond to the increasing demand for hydrogen energy is increasing. Among them, underground hydrogen storage is recognized as the most economical and reasonable storage method because of its vast hydrogen storage capacity. In Korea, low-depth hydrogen storage using artificial protective structures is being considered. Further, establishing corresponding safety standards and ground stability evaluation is becoming essential. This study evaluated the hydro-mechanical behavior of the ground during a hydrogen gas leak from a low-depth underground hydrogen storage facility through the HM coupled analysis model. The predictive reliability of the simulation model was verified through benchmark experiments. A parameter study was performed using a metamodel to analyze the sensitivity of factors affecting the surface uplift caused by the upward infiltration of high-pressure hydrogen gas. Accordingly, it was confirmed that the elastic modulus of the ground was the largest. The simulation results are considered to be valuable primary data for evaluating the complex analysis of hydrogen gas explosions as well as hydrogen gas leaks in the future.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.109-116
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• 2021

Traditionally, the engineering field has been dominated by face-to-face education focused on experimental practice, but demand for online learning has soared due to the rapid development of IT technology and Internet communication networks and recent changes in the social environment such as COVID-19. In order for efficient online education to be conducted in the engineering field, where the proportion of experimental practice is relatively high compared to other fields, virtual laboratory practice content that can replace actual experimental practice is very necessary. In this study, we developed a line tracer model and a virtual experimental software to simulate it for efficient online learning of microprocessor applications that are essential not only in the electric and electronic field but also in the overall engineering field where IT convergence takes place. In the developed line tracer model, the user can set various hardware parameter values in the desired form and write the software in assembly language or C language to test the operation on the computer. The developed line tracer virtual experimental software has been used in actual classes to verify its operation, and is expected to be an efficient virtual experimental practice tool in online non-face-to-face classes.

• Journal of Powder Materials
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• v.30 no.4
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• pp.324-331
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• 2023

Lithium (Li) is a key resource driving the rapid growth of the electric vehicle industry globally, with demand and prices continually on the rise. To address the limited reserves of major lithium sources such as rock and brine, research is underway on seawater Li extraction using electrodialysis and Li-ion selective membranes. Lithium lanthanum titanate (LLTO), an oxide solid electrolyte for all-solid-state batteries, is a promising Li-ion selective membrane. An important factor in enhancing its performance is employing the powder synthesis process. In this study, the LLTO powder is prepared using two synthesis methods: sol-gel reaction (SGR) and solid-state reaction (SSR). Additionally, the powder size and uniformity are compared, which are indices related to membrane performance. X-ray diffraction and scanning electron microscopy are employed for determining characterization, with crystallite size analysis through the full width at half maximum parameter for the powders prepared using the two synthetic methods. The findings reveal that the powder SGR-synthesized powder exhibits smaller and more uniform characteristics (0.68 times smaller crystal size) than its SSR counterpart. This discovery lays the groundwork for optimizing the powder manufacturing process of LLTO membranes, making them more suitable for various applications, including manufacturing high-performance membranes or mass production of membranes.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.337-345
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• 2023

Monitoring river microplastics is a challenging task since it is a time-consuming and high-cost process. The use of a physical model to have a better understanding of river microplastics' behaviors can complement the challenging monitoring process. However, there have been very limited studies on modeling river microplastics. In this study, therefore, we evaluated the applicability of one commonly used river water quality model, i.e., the Water Quality Analysis Simulation Program (WASP), in simulating the microplastic concentration in the river environment. We simulated the microplastic concentration in the Anyangcheon stream using the WASP's biochemical oxygen demand (BOD) and suspended solid (SS) variables as possible surrogate variables for the microplastics. Simulation analyses indicate that the SS state variable performs better than the BOD state variable to mimic the observed concentrations of microplastics. This is because of the characteristics of each water quality parameter; the BOD variable, a biochemical indicator, is inappropriate for modeling the behaviors of microplastics, which have generally constant biochemical features. In contrast, the SS variable, which has similar physical behaviors, followed the observed patterns of the microplastic concentrations well. To build a more advanced and accurate model for simulating the microplastic concentration, comprehensive and long-term monitoring studies of the river microplastics under different environmental conditions are needed, and the unit of microplastic concentration should be carefully addressed before its modeling application.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.