• Journal of Aerospace System Engineering
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• v.17 no.6
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• pp.27-34
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• 2023

This paper presents the application of a magnetic gear to the electric propulsion system for an aircraft. Since high torque is required in aircraft electric propulsion systems, combining a speed reducer can amplify the torque. However, mechanical gears have issues, such as friction, vibration, and heat generation, which lead to maintenance challenges. In the case of a direct-drive motor that does not use mechanical gears, the size and weight of the motor increase to achieve high torque. This paper proposes the application of a magnetic gear to solve the maintenance issues of mechanical gears and the weight increase problem of direct-drive motors in aircraft electric propulsion systems. In this paper, a magnetic gear suitable for aircraft electric propulsion systems is designed, and it is compared with a direct-drive motor in terms of performance and the feasibility of applying the magnetic gear is verified.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1345-1352
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• 2023

With the development of virtual reality (VR) and blockchain technology, Metaverse is being used in various fields such as games, education, and social networking. At the same time, shipments of wearable devices such as smartwatches are growing every year, becoming more and more integrated into people's daily lives. This study presents a new possibility of reflecting the user's body signals measured through the combination of the two phenomena in the metaverse character. Various biometric information such as the user's heart rate and amount of exercise collected through the smartwatch are reflected on the character in the metaverse, allowing the user's physical condition to be reflected in the virtual world. Through this, Metaverse is expected to provide a new experience that can be called 'extended reality' beyond simple virtual reality, improve user's satisfaction with Metaverse, and suggest a direction for the development of smartwatches.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.107-112
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• 2024

One More Pumpkin won the grand prize at the 2023 Dubai International AI Film Festival, and new possibilities were also opened through the International AI and Metaverse Film Festival (GAMFF), which was held for the first time in Korea. Generative works began to stand out in earnest, with 527 diverse works from 42 countries at home and abroad using AI and metaverse technology submitted to this contest. AI is being used in a variety of fields, including the creation and implementation of digital characters through combination with VFX, improving the efficiency of video production, and managing the overall video production process. This contributes to saving human and material resources required for production and significantly improving the quality of produced videos. However, generative AI also has ambiguity in copyright attribution, ethical issues inherent in the learned dataset, and technical limitations that fall short of the level of human emotion and creativity. Accordingly, this study suggests implications at the level of production, screening, and use, as generative AI may have an impact in more areas in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.4
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• pp.48-58
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• 2009

Glycosyl hydrolases (GH, EC 3.2.1.-) are key enzymes which can hydrolyze the glycosidic bonds between two or more carbohydrates, or between a carbohydrate and a non-carbohydrate moiety. The new enzyme nomenclature of glycoside hydrolases is based on their amino acid sequence similarity and structural features. Here, we examined the glycosyl hydrolase family(GHF) genes reported from earthworm species. Among overall 115 GHFs, 12 GHFs could be identified from earthworm species through CAZy database. Of 12 GHF group genes, five genes including GHF2, 5, 17, 18, 20 are thought to be potent for industrial applications. The alignment of these genes with same genes from other animal species exhibited high sequence homology and some important amino acid residues necessary for enzyme activity appeared to be conserved. These genes can be utilized as a pest control agent or applicable to the food industry, clinical therapeutics and organic wastes disposition.

• Explosives and Blasting
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• v.42 no.3
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• pp.9-22
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• 2024

As buildings constructed in the 1980s during a period of rapid urbanization and economic growth have aged, the demand for demolition, especially of reinforced concrete structures, has increased. In large-scale structures such as industrial buildings, a mixed approach utilizing both mechanical demolition and explosive demolition methods is being employed. As the demand for demolition rises, so do safety concerns, making structural stability during demolition a crucial issue. In this study, drones and LiDAR were used to collect actual structural data, which was then used to build a simulation model. The analysis method employed was a combination of the Finite Element Method (FEM) and the Discrete Element Method (DEM), known as the Combined Finite-Discrete Element Method (FDEM), which was used to perform dynamic structural analysis during various demolition phases. The results were compared and analyzed with the commercial software ELS to assess its applicability.

• Resources Recycling
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• v.33 no.4
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• pp.29-35
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• 2024

The recycling of waste resources, such as spent catalysts, primarily involves leaching and extracting metal components via smelting. These metal components are then recovered as salts, such as sulfates and nitrates. When crystallization occurs during the calcination of the recovered salts, the salts are converted into oxides, which are then reduced to form metals or ceramic materials. Common reducing agents used in oxide reduction include hydrogen and carbon, and metal powders are obtained upon reduction. Carbide synthesis can occur if the recycled element is a transition metal and carbon is used as the reducing agent. Despite being ceramic materials, transition metal carbides exhibit excellent conductivity owing to their metallic bonding. Recently, MXene, a two-dimensional transition metal carbide, has gained attention for electromagnetic wave shielding, secondary battery electrodes, and water purification owing to its electrical conductivity and large surface area. This study developed a process for synthesizing high-value MXene materials from waste resources. The properties of these MXenes were evaluated to confirm the potential of using waste resources as raw materials for MXenes.

• Analyses & Alternatives
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• v.8 no.3
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• pp.125-150
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• 2024

This paper explores the integration of artificial intelligence and causal inference in social science research, focusing on causal deep learning. We examine key theories including Pearl's Structural Causal Model, Rubin's Potential Outcomes Framework, and Schölkopf's Causal Representation Learning. Methodologies such as structural causal models with deep learning, counterfactual reasoning, and causal discovery algorithms are discussed. The paper presents applications in social media analysis, economic policy, public health, and education, demonstrating how causal deep learning enables nuanced understanding of complex social phenomena. Key challenges addressed include model complexity, causal identification, interpretability, and ethical considerations like fairness and privacy. Future research directions include developing new AI architectures, real-time causal inference, and multi-domain generalization. While limitations exist, causal deep learning shows significant potential for enhancing social science research and informing evidence-based policy-making, contributing to addressing complex social challenges globally.

• The Journal of Learner-Centered Curriculum and Instruction (JLCCI)
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• v.23 no.1
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• pp.75-105
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• 2019

As energy transition accelerates and transition politics intensifies, the strategy and pathway of energy transition are becoming an issue. And there is a growing interest in energy democracy as a discourse criticizing market-led energy transition and seeking fundamental restructuring of energy system. However, the imaginations of energy democracy are different from each other as a social movement discourse and a criterion for political evaluation of energy transition. This study aims to analyze the issues of energy democracy and reinterpret them from the perspective of the Commons. As various social movements are connected, energy democracy includes elements of localization, decentralization, liberalization, commoning and socialization that can conflict with each other in terms of transition strategy. In addition, the imagination of the subject of energy transition is diverging between investors, consumers, workers, and energy citizens. Thinking about energy infrastructure as the Commons in this situation helps to understand the critical issues of energy democracy and to imagine new transition experiments. Energy democracy implies that the new commons are being created across the scale of energy infrastructure in the contention of the transition to a decentralized renewable energy system.