• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.299-308
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• 2022

This study was conducted to predict the tendency for heat exchange and boil-off gas (BOG) in a liquefied hydrogen tank under sloshing excitation. First, athe fluid domain excited by sloshing was modeled using a multiphase-thermal flow domain in which liquid hydrogen and hydrogen gas are in the saturated state. Both the the volume of fluid (VOF) and Eulerian-based multi-phase flow methods were applied to validate the accuracy of the pressure prediction. Second, it was indirectly shown that the fluid velocity prediction could be accurate by comparing the free surface and impact pressure from the computational fluid dynamics with those from the experimental results. Thereafter, the heat ingress from the external convective heat flux was reflected on the outer surfaces of the hydrogen tank. Eulerian-based multiphase-heat flow analysis was performed for a two-dimensional Type-C cylindrical hydrogen tank under rotational sloshing motion, and an inflation technique was applied to transform the fluid domain into a computational grid model. The heat exchange and heat flux in the hydrogen liquid-gas mixture were calculated throughout the analysis,, whereas the mass transfer and vaporization models were excluded to account for the pure heat exchange between the liquid and gas in the saturated state. In addition, forced convective heat transfer by sloshing on the inner wall of the tank was not reflected so that the heat exchange in the multiphase flow of liquid and gas could only be considered. Finally, the effect of sloshing on the amount of heat exchange between liquid and gas hydrogen was discussed. Considering the heat ingress into liquid hydrogen according to the presence/absence of a sloshing excitation, the amount of heat flux and BOG were discussed for each filling ratio.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.

• Journal of Marine Life Science
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• v.8 no.1
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• pp.87-92
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• 2023

Microplastic particles are ubiquitous in the environment and not standardized particles of size, shape, or type. Therefore, it is very limited to establish a risk assessment framework that accurately evaluated and manage the multi-dimension of marine environment including seawater and sediment based on toxic data. In the study, we review the characteristics and effects of marine environmental microplastic and suggest risk assessment framework (draft) based on the distribution and impact of marine environmental microplastics. Although, the characteristics of environmental microplastic are very widely but the most abundant toxic data are concentrated on unique shape and type, and there are also large gaps of test organism between laboratory-exposed organisms and resident species. Great limitations with respect to toxicity data quality also exist for traditional effect assessment methods, which in reliability of the resulting risk characterizations. However, considering the fact that the international community's movement on microplastics management is gradually strengthening and the pollution level of microplastics in marine environment is increasing, further research on environmental relevant risk assessment technique should be proposed based on the characteristics of microplastics in the marine environment.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.387-401
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• 2023

The Earth Pressure Balanced (EPB) Shield Tunnel Boring Machine (TBM) is widely employed for constructing urban underground spaces due to its minimal vibration and low noise levels. The injection of additives offers several advantages, including maintaining shield chamber pressure, reducing shear strength, minimizing cutter wear, and decreasing the permeability of the excavated soil. This technique is known as soil conditioning and involves the application of additives such as foam, polymer, and bentonite slurry. In this study, weathered granite soil commonly encountered at domestic tunnel sites was used as a soil specimen. Foam and polymer were applied as additives to assess the rheological properties of conditioned soils. The workability was evaluated through slump tests, while the rheological properties were assessed through laboratory pressurized vane shear tests conducted under the same conditions. Specially, the polymer was applied under specific conditions with low workability with high slump values, with the aim of evaluating the impact of polymer application. The test results revealed that with an increase in the Foam Injection Ratio (FIR), the slump value also increased, while the torque, peak strength, yield stress, apparent viscosity, and thixotropic area decreased. Conversely, an increase in the Polymer Injection Ratio (PIR) led to results opposite to those of FIR. Additionally, a correlation between the slump value and yield stress was proposed. When comparing conditions with only foam applied to those with both foam and polymer applied, even with similar slump values, the yield stress was found to be lower in the latter conditions.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.2
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• pp.33-42
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• 2023

Throughout all industries, eco-friendliness is being promoted worldwide with focus on suppressing the environmental impact. With recent international environment policies and regulations supported by government, the electric vehicles demand is expected to increase rapidly. Battery system itself perform an essential role in EVs technology that is arranged in cells, modules, and packs, and each of them are connected mechanically and electrically. A multifaceted approach is necessary for battery pack bonding technologies. In this paper, pros and cons of applicable bonding technologies, such as resistance welding, laser and ultrasonic bonding used in constructing electric vehicle battery packs were compared. Each bonding technique has different advantages and limitations. Therefore, several criteria must be considered when determining which bonding technology is suitable for a battery cell. In particular, the shape and production scale of battery cells are seen as important factors in selecting a bonding method. While dealing with the types and components of battery cells, package bonding technologies and general issues, we will review suitable bonding technologies and suggest future directions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.533-543
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• 2023

This study explores the influence of the COVID-19 pandemic on the Korean art market and contrasts the classic hedonic method of art price prediction with the Artificial Neural Network technique. The empirical analysis of this paper utilizes 14,639 observations of Korean art auction data from 2015 to 2021. There are three types of variables in this study: artist-related, artwork-related, and sales-related. Previous studies have suggested that these three types of variables influence art prices. The empirical findings in this research are in twofold. First, in terms of RMSE and R², the Artificial Neural Network outperforms the hedonic model. Both techniques discover that sales and artwork variables have a greater impact than artist-related attributes. Second, when the primary factors of art price are controlled, Korean art prices are found to fall dramatically in 2020, shortly following the onset of COVID-19, but to rebound in 2021. The main lesson in this study is that the Artificial Neural Network enhances art price prediction and reduces information asymmetry in the Korean art market even in the face of unanticipated turmoil such as the COVID-19 outbreak.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1057-1063
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• 2023

The urban railway sleeper floating track, the subject of this study, is an anti-vibration track to reduce vibration transmitted to the structure. currently, the replacement cycle of resilience pad for sleeper floating tracks is set and operated based on load. however, most previous studies were conducted on load-based structural safety aspects, such as fatigue life evaluation of sleeper anti-vibration pads and increase in track impact coefficient and track support stiffness due to increase in spring stiffness. therefore, in this study, we measure the vibration acceleration of the ballast for each analysis section and use the results of 7 million fatigue tests to calculate the spring stiffness of the resilience pad for each section. the spring stiffness of the resilience pad calculated for each section was set as the analysis data and the concrete vibration acceleration was derived analytically. the adequacy of analysis modeling was verified as the analyzed concrete bed vibration acceleration for each section was within the field-measured concrete bed vibration acceleration range. using the vibration acceleration curve according to the derived spring stiffness change, the spring stiffness of the resilience pad is estimated from the measured vibration acceleration. therefore, we would like to present a technique that can estimate the spring stiffness of resilience pad of a running track using the vibration acceleration of the measured concrete bed.