• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.131-140
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• 2024

Aids to navigation, such as buoys used in maritime environments, play a crucial role in providing accurate information to navigating vessels, enabling them to precisely determine their position and maintain safe routes by marking surrounding hazardous areas. However, collisions between ships and these aids result in substantial costs for buoy damage and repair. While high-end equipment is currently used to prevent such accidents, its widespread adoption is hindered by cost concerns. This paper presents research on a maritime object detection algorithm utilizing embedded systems to address this issue. Previous studies employed the Hough transform for horizon detection, but its high computational demands posed challenges for real-time processing. To overcome this limitation, our approach first performs image segmentation, followed by an optimized Otsu algorithm for horizon detection. Subsequently, we establish a Region of Interest (ROI) based on the detected horizon, focusing on areas with a high risk of ship collision. Within this ROI, particularly below the horizon line, maritime objects are detected. A Convolutional Neural Network (CNN) model is then applied to determine whether the detected objects are ships. Objects classified as ships within the ROI are considered potential collision risks.

• Journal of Electrochemical Science and Technology
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• v.15 no.4
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• pp.476-483
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• 2024

Lithium-ion (Li-ion) batteries are essential to modern society, but pose safety risks because of thermal runaway and ignition. This study explores the use of hybrid aqueous electrolytes to enhance the safety and performance of Li-ion batteries, focusing on the solid-electrolyte interface (SEI) formed on lithium titanate (Li₄Ti₅O₁₂; LTO) electrodes. We employed high-resolution transmission electron microscopy (HRTEM) and density functional theory (DFT) calculations to analyze the microstructure and stability of the SEI layer. Further, we prepared LTO and LiMn₂O₄ (LMO) electrodes, assembled full cells with hybrid aqueous electrolytes, and carried out electrochemical testing. The HRTEM analysis revealed the epitaxial growth of a LiF SEI layer on the LTO electrode, which has a coherent lattice structure that enhances electrochemical stability. The DFT calculations confirmed the energetic favorability of the LiF-LTO interface, indicating strong adhesion and potential for epitaxial growth. The full cell demonstrated excellent discharge performance, showing a notable improvement in coulombic efficiency after the initial cycle and sustained capacity over 100 cycles. Notably, the formation of a dense, crystalline LiF SEI layer on the LTO electrode is crucial for preventing continuous side reactions and maintaining mechanical stability during cycling. The experimental results, supported by the DFT results, highlight the importance of the orientational relationship between the SEI and the electrode in improving battery performance. The integration of experimental techniques and computational simulations has led to the development of an LTO/LMO full cell with enhanced discharge capabilities and stability. This study provides insights into the growth mechanisms of the SEI layer and its impact on battery performance, demonstrating the potential of hybrid aqueous electrolytes in advancing lithium-ion battery technology. The findings affirm the viability of this approach for optimizing next-generation Li-ion batteries, which can promote the development of safer and more reliable energy storage solutions.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.493-507
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• 2024

RANS-based CFD analysis is widely applied in various engineering fields, including practical hydraulic engineering, due to its high computational efficiency. However, problems of non-physical behavior in the analysis of two phase flow, such as free surfaces, have long been raised. The two-equation turbulence models used in general RANS-based analysis were developed for single phase flow and simulate unrealistically high turbulence energy at the interface where there are abrupt changes in fluid density. To solve this issue, one of the methods recently developed is the buoyancy-modified turbulence model, which has been partially validated in coastal engineering, but has not been applied to open channel flows. In this study, the applicability of the buoyancy-modified turbulence model is evaluated using the VOF method in the open-source program OpenFoam. The results of the uniform flow showed that both the buoyancy-modified k-𝜖 model and the buoyancy-modified k-ω SST model effectively simulated the reduction of turbulence energy near the free surface. Specifically, the buoyancy-modified k-ω SST model accurately simulated the vertical velocity distribution. Additionally, the model is applied to dam-break flows to examine cases with significant surface variation and cavity formation. The simulation results show that the buoyancy-modified turbulence models produce varying results depending on the VOF method and shows non-physical behavior different from experimental results. While the buoyancy-modified turbulence model is applicable in cases with stable surface shapes, it still has limitations in general application when there are rapid changes in the free surface. It is concluded that appropriate adjustments to the turbulence model are necessary for flows with rapid surface changes or cavity formation.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.5
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• pp.499-505
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• 2024

To address the issue of global warming, various regulations and policies for reducing greenhouse gas emissions are being implemented. In this context, the number of countries targeting carbon neutrality, the latter of which entails reducing net carbon emissions to zero, is increasing, and small modular reactors (SMRs) are investigated extensively as a new model for power plants. SMRs, although measuring only 5%-10% of the size of conventional large nuclear power plants, are highly efficient systems that can generate hundreds of megawatts of power. Compared with fossil fuel-based power plants, SMRs generate less carbon emissions and can complement the unstable energy supply from renewable sources. However, the use of SMRs is opposed by local residents owing to the risk of significant radioactive-material leakage when a nuclear-power-plant accident occurs. Hence, floating, small nuclear-power vessels are being investigated and installed in the ocean, thus simplifying the process of securing land, compensating nearby residents, and increasing safety against natural disasters. In this study, the towing stability of SMR power ships is analyzed, and the result shows no significant risk of towing to the destination in sea states 3, 4, and 5.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.285-294
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• 2023

The importance of urban green space creation is increasingly recognized as the most realistic and efficient approach for fine dust mitigation in urban areas. Particularly considering the characteristics of domestic cities, the application of buffer green spaces along roads can maximize the efficiency of fine dust reduction without the need for separate green space creation. Accordingly, this study analyzed the fine dust mitigation effects based on the types of plantings in the central dividers and roadside trees in Jeonju City, Jeollabuk-do. To do this, we controlled various external variables of urban space and considered the planting arrangement types in the central dividers, carrying out the analysis using a CFD simulation. The simulation results confirmed that the central dividers with plantings demonstrated more effective ultrafine dust reduction than those without. Moreover, the arrangement of roadside trees showed a greater ultrafine dust reduction effect when adopting a multilayered structure compared to a single layer. Based on these findings, we concluded that installing both trees and shrubs simultaneously in the central dividers and along roads was effective for ultrafine dust mitigation. On this basis, we quantified the dust reduction effects of plants in urban street environments and proposed planting guidelines for roadside green spaces to improve air quality.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.5
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• pp.285-294
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• 2024

Seismic fragility curves present the conditional probability of damage to target structures due to external seismic load and are widely used in various ways. When constructing such a seismic fragility curve, it is essential to consider various types and numbers of ground motions. In general, the earthquake occurrence characteristics of an area where the target structure of the seismic fragility curve exists are analyzed, and based on this, appropriate ground motions are selected to derive the seismic fragility curve. If the number of selected ground motions is large, the diversity of ground motions is considered, but a large amount of computational time is required. Conversely, if the number of ground motions is too small, the diversity of ground motions cannot be considered, which may distort the seismic fragility curve. Therefore, this study analyzed the relationship between the number of ground motions considered when deriving the seismic fragility curve and the parameters of the seismic fragility curve. Using two example structures, numerical analysis was performed by selecting a random number of ground motions from a total of two hundred, and a seismic fragility curve was derived based on the results. Analysis of the relationship of the parameter of the seismic fragility curve and the number of selected ground motions was performed. As the number of ground motions considered increases, uncertainty in ground motion selection decreases, and when deriving seismic fragility curves considering the same number of ground motions, uncertainty increases relatively as the degree of freedom of the target structure increases. However, considering a relatively large number of ground motions, uncertainty appeared insignificant regardless of increased degrees of freedom. Finally, it is possible that the increase in the number of ground motions could lower the epistemic uncertainty and thus improve the reliability of the results.

• Korean Journal of the Atmospheric Sciences
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• v.78 no.9
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• pp.2657-2675
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• 2021

A new k-distribution scheme of longwave radiation without the correlated-k-distribution assumption is developed. Grouping of spectral points is based on the line-by-line (LBL)-calculated absorption coefficient k at a few sets of reference pressure p_r and temperature θ_r, where the cooling rate is substantial in a spectral band. In this new scheme, the range of k(p_r, θ_r) of a band is divided into a number of equal intervals, or g groups, in log₁₀(k_r). A spectral point at the wavenumber ν is identified with one of the g groups according to its k_ν(p_r, θ_r). For each g group, a Planck-weighted k-distribution function H_g and a nonlinearly averaged absorption coefficient ${\bar{k}}_g(p,{\theta})$ are derived. The function H_g and the absorption coefficient ${\bar{k}}_g(p,{\theta})$ constitute the new k-distribution scheme. In this k-distribution scheme, a spectral point can only be identified with a g group regardless of pressure and temperature, which is different from the correlated-k distribution scheme. The k-distribution scheme is applied to the H₂O, CO₂, O₃, N₂O, and CH₄ absorption bands, and results are compared with LBL calculations. To balance between the accuracy and the computational economy, the number of g groups in a band of a given gas is chosen such that 1) the difference in cooling rate is <0.1 K day^-1 in the troposphere and <1.0 K day^-1 in the stratosphere and 2) the difference in fluxes is <0.5 W m^-2 at both the top of the atmosphere and the surface. These differences are attained with 130 g groups, which is the sum of the g groups of all five gases.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.312-323
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• 2024

This study aims to evaluate the safe operating envelope (SOE) for light aircraft carriers using a computational fluid dynamics (CFD) modeling database of flight deck air-wake. Assessing the SOE is crucial for ensuring the safe operation of carrier-based aircraft, particularly during take-off and landing maneuvers. Traditional methods that only consider relative wind envelopes (RWE) provide basic information but fail to account for the complex airflow patterns over the flight deck. To address this limitation, this research utilizes CFD to analyze the air-wake and integrate these findings into the SOE assessment. Various studies on CFD modeling of airflow around naval ships and aircraft carriers were reviewed, confirming the importance of accurate airflow databases for operational safety. This study employs the KRISO-CVX1 model, a light aircraft carrier designed by the Korea Research Institute of Ships & Ocean Engineering (KRISO), to demonstrate the application of CFD data in SOE evaluations. The methodology involves a detailed analysis of turbulent flow and thermal fields around the carrier deck under different wind speed, direction, and ship speed conditions. The results indicate significant variations in air-wake characteristics depending on the relative wind speed and direction, impacting the operational safety of carrier-based aircraft. This study emphasizes the need for incorporating CFD-based airflow data into SOE assessments to enhance the accuracy and reliability of operational safety evaluations for aircraft carriers. In conclusion, the integration of CFD air-wake modeling databases provides a more comprehensive approach to assessing the SOE, offering improved safety margins for carrier-based aircraft operations. This research is expected to contribute to the development of more robust and precise operational guidelines for naval aviation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.2028-2042
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• 1994

Properly defining signal and noise in a self-organizing system like ART(Adaptive Resonance Theory) neural network model raises a number of subtle issues. Pattern context must enter the definition so that input features, treated as irrelevant noise when they are embedded in a given input pattern, may be treated as informative signals when they are embedded in a different input pattern. The ATR automatically self-scales their computational units to embody context and learning dependent definitions of a signal and noise and there is no problem in categorizing input pattern that have features similar in nature. However, when we have imput patterns that have features that are different in size and nature, the use of only one vigilance parameter is not enough to differentiate a signal from noise for a good categorization. For example, if the value fo vigilance parameter is large, then noise may be processed as an informative signal and unnecessary categories are generated: and if the value of vigilance parameter is small, an informative signal may be ignored and treated as noise. Hence it is no easy to achieve a good pattern categorization. To overcome such problems, a Coupled-ART neural network capable of modularized categorization of patterns is proposed. The Coupled-ART has two layer of tightly coupled modules. the upper and the lower. The lower layer processes the global features of a pattern and the structural features, separately in parallel. The upper layer combines the categorized outputs from the lower layer and categorizes the combined output, Hence, due to the modularized categorization of patterns, the Coupled-ART classifies patterns more efficiently than the ART1 model.