• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• Korean Chemical Engineering Research
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• v.62 no.2
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• pp.173-180
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• 2024

Under ordinary atmospheric circumstances, the gravimetric technique was used to measure the solubility of L-cysteine (L-Cys) and L-alanine (L-Ala) in various solvents, including methyl alcohol, ethyl acetate, and mixtures of the two, in the range o 283.15 K to 323.15 K. Both individual solvents and their combinations showed a rise in the solubility of L-Cys and L-Ala with increasing temperature, according to the analyzed data but when analyzed at a constant temperature in the selected mixed solvents, the solubility declined with decreasing of initial mole fractions of methyl alcohol. To further assess, the relative utility of the four solubility models, we fitted the solubility data using the Jouyban-Acree (J-A), van't Hoff-Jouyban-Acree (V-J-A), Apelblat-Jouyban-Acree (A-J-A), and Ma models followed by evaluation of the values of the RAD information criteria and the RMSD were. The dissolution was also found to be an entropy-driven spontaneous mixing process in the solvents since the thermodynamic parameters of the solvents were determined using the van't Hoff model. In order to support the industrial crystallization of L-cysteine and L-alanine and contribute to future theoretical research, we have determined the experimental solubility, correlation equations, and thermodynamic parameters of the selected amino acids during the dissolution process.

• Journal of the Korean Society of Clothing and Textiles
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• v.48 no.1
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• pp.108-119
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• 2024

In this study, we aimed to propose enhancements to the dimensions and design of walking aids tailored for elderly women. Specifically, we focused on wheeled walking assistance devices and aligned each structural component with the appropriate human body dimensions to suggest appropriate product dimensions organized by size clusters, aiming to maximize the practicality of the results. We extracted essential factors required for product design, including human body size elements. The dimension extraction method was clustered to establish connections between key human body parameters-such as height, weight, and age groups-and product dimensions. We conducted a comparative analysis of walking aid product dimensions according to the design elements and sizes of models currently available in the market. The outcomes of this study offer objective, data-driven insights into areas where existing models on the market could benefit from improvement and we anticipate that the findings of this study will provide a solid, quantitative foundation for individuals when selecting the most suitable model for their needs.

• IMMUNE NETWORK
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• v.20 no.1
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• pp.2.1-2.19
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• 2020

Acute viral infection or vaccination generates highly functional memory CD8 T cells following the Ag resolution. In contrast, persistent antigenic stimulation in chronic viral infection and cancer leads to a state of T-cell dysfunction termed T-cell exhaustion. We and other have recently identified a novel subset of exhausted CD8 T cells that act as stem cells for maintaining virus-specific CD8 T cells in a mouse model of chronic lymphocytic choriomeningitis virus infection. This stem cell-like CD8 T-cell subset has been also observed in both mouse and human tumor models. Most importantly, in both chronic viral infection and tumor models, the proliferative burst of Ag-specific CD8 T cells driven by PD-1-directed immunotherapy comes exclusively from this stem cell-like CD8 T-cell subset. Therefore, a better understanding of the mechanisms how CD8 T-cell subsets are regulated during chronic viral infection and cancer is required to improve the current immunotherapies that restore the function of exhausted CD8 T cells. In this review, we discuss the differentiation of virus-specific CD8 T cells during chronic viral infection, the characteristics and function of CD8 T-cell subsets, and the therapeutic intervention of PD-1-directed immunotherapy in cancer.

• ETRI Journal
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• v.46 no.1
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• pp.127-136
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• 2024

The Korean language has written (formal) and spoken (phonetic) forms that differ in their application, which can lead to confusion, especially when dealing with numbers and embedded Western words and phrases. This fact makes it difficult to automate Korean speech recognition models due to the need for a complete transcription training dataset. Because such datasets are frequently constructed using broadcast audio and their accompanying transcriptions, they do not follow a discrete rule-based matching pattern. Furthermore, these mismatches are exacerbated over time due to changing tacit policies. To mitigate this problem, we introduce a data-driven Korean spoken-to-written transcription conversion technique that enhances the automatic conversion of numbers and Western phrases to improve automatic translation model performance.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.2
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• pp.82-99
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• 2024

The aim of this study is to propose strategies for in-flight OTT services by examining differences in binge-watching patterns through online video streaming services based on age. Additionally, it investigates how the moderating effects of need for cognition and critical media literacy influence the relationship between age and binge-watching. Data from the 2020 Korean Media Panel Survey conducted by the Korea Information Society Development Institute were utilized, with moderating effects analyzed using Process Macro Model 1. Results indicate that as age increases, the frequency of binge-watching content rises while the duration decreases. Moreover, moderating effects of need for cognition and critical media literacy in the age-binge-watching relationship were confirmed. This study analyzed binge-watching behaviors among online video streaming service users, confirming the influence of age, binge-watching habits, need for cognition, and critical media literacy. Theoretical and practical implications include insights for in-flight service providers, content marketers, and online video streaming service operators.

• Structural Engineering and Mechanics
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• v.91 no.2
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• pp.227-238
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• 2024

Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double 𝑙₁ regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double 𝑙₁ regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method's higher accuracy and strong robustness to noises compared with other traditional regularization methods.

• Smart Structures and Systems
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• v.34 no.2
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• pp.87-96
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• 2024

Smart materials, such as magnetorheological (MR) fluid, have received considerable research attention in recent years due to their unique capabilities. MR fluid, which possesses a magnetic field controllable viscosity, has been extensively studied for vehicular applications with the aim of synthesizing optimal MR fluids, designing optimal MR dampers, and developing control strategies. However, a comprehensive study that primarily focuses on developing a cost-effective semi-active suspension system for a commercial vehicle in a developing nation is still lacking. This study addresses this gap by synthesizing an in-house MR fluid and studying its rheological properties. Subsequently, a novel single-sensor-based controller is developed and closed-loop simulations are conducted on a quarter-car semi-active model. Finally, the overall semi-active quarter-car suspension system is experimentally tested using a suspension test rig. The performance of the proposed system in terms of ride comfort and road holding is evaluated and is compared with simple control strategies. The dynamic range of the developed semi-active MR damper is found to be around 2.3, indicating a significant MR effect. The results suggest an intermediate response using the proposed acceleration-driven controller (ADV) at lower frequencies and similar performance to that of the skyhook controller at higher frequencies. The cost-effective methodology proposed in this study is effective and can be adapted for other semi-active engineering applications.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1230-1237
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• 2024

Personalized learning is a critical factor in optimizing performance on construction sites. Traditional pedagogical methods often adhere to a one-size-fits-all approach, failing to provide the nuanced adaptation required to cater to diverse knowledge needs, roles, and learning preferences. While advancements in technology have led to improvements in personalized learning within construction education, the crucial connection between instructors' roles and training enviornment to personalized learning success remains largely unexplored. To address these gaps, this research proposes a novel learning approach utilizing multi-agent, context-specific AI agents within construction virtual environments. This study aims to pioneer an innovative approach leveraging the Large Language Model's capabilities with prompt engineering to make domain-specific conversations. Through the integration of AI-driven conversations in a realistic 3D environment, users will interact with domain-specific agents, receiving personalized safety guidance and information. The system's performance is assessed using the five evaluation criteria including learnability, interaction, communication, relevancy and visualization. The results revealed that the proposed approach has the potential to significantly enhance safety learning in the construction industry, which may lead to improve practices and reduction in accidents on diverse construction sites.

• Educational Technology International
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• v.25 no.2
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• pp.231-260
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• 2024

This study suggests that educational recommender systems should be explainable and extend beyond the commercially driven algorithms that primarily rely on user preferences and purchase behaviors. Instead, we propose a recommendation method that considers how and why people learn by employing the relative importance of various learner variables. To develop a recommendation algorithm, 100 adult participants used 4 to 6 foreign language learning mobile applications(apps), generating a dataset of 557 user perception reports. Using this data, we designed and developed a recommender system based on the importance weights of 14 learner variables, categorized into four groups: (a) demographic information, (b) motivational orientation for language learning (instrumental vs. integrative), (c) learning styles, and (d) learning experience. The results based on RandomForestRegressor model revealed that language learning motivation, learning styles (specifically information processing), and usage frequency were significantly more influential than general demographic factors in predicting learners' evaluation of the apps. Furthermore, learners' perception of the recommender system revealed that the recommender system was relevant and engaging, effectively meeting their needs and assisting them in selecting appropriate language learning apps. Overall, this study demonstrates the potential of educational recommender systems that consider learners' motivation, experience, and learning styles.