• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.81-87
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• 2023

Intrusion detection systems that learn metadata of network packets have been proposed recently. However these approaches require time to analyze packets to generate metadata for model learning, and time to pre-process metadata before learning. In addition, models that have learned specific metadata cannot detect intrusion by using original packets flowing into the network as they are. To address the problem, this paper propose a natural language processing-based intrusion detection system that detects intrusions by learning the packet payload as a single sentence without an additional conversion process. To verify the performance of our approach, we utilized the UNSW-NB15 and Transformer models. First, the PCAP files of the dataset were labeled, and then two Transformer (BERT, DistilBERT) models were trained directly in the form of sentences to analyze the detection performance. The experimental results showed that the binary classification accuracy was 99.03% and 99.05%, respectively, which is similar or superior to the detection performance of the techniques proposed in previous studies. Multi-class classification showed better performance with 86.63% and 86.36%, respectively.

• Advances in nano research
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• v.15 no.5
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• pp.441-449
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• 2023

Heavy metals, widely present in the environment, have become significant pollutants due to their excessive use in industries and technology. Their non-degradable nature poses a persistent environmental problem, leading to potential acute or chronic poisoning from prolonged exposure. Recent research has focused on separating heavy metals, particularly from industrial and mining sources. Industries such as metal plating, mining operations, tanning, wood and chipboard production, industrial paint and textile manufacturing, as well as oil refining, are major contributors of heavy metals in water sources. Therefore, removing heavy metals from water is crucial, especially for safe water supply in swimming and water sports. Iron oxide nanoparticles have proven to be highly effective adsorbents for water contaminants, and efforts have been made to enhance their efficiency and absorption capabilities through surface modifications. Nanoparticles synthesized using plant extracts can effectively bind with heavy metal ions by modifying the nanoparticle surface with plant components, thereby increasing the efficiency of heavy metal removal. This study focuses on removing lead from industrial wastewater using environmentally friendly, cost-effective iron nanoparticles synthesized with Genovese basil extract. The synthesis of nanoparticles is confirmed through analysis using Transmission Electron Microscope (TEM) and X-ray diffraction, validating their spherical shape and nanometer-scale dimensions. The method used in this study has a low detection limit of 0.031 ppm for measuring lead concentration, making it suitable for ensuring water safety in swimming and water sports.

• Journal of Environmental Science International
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• v.32 no.11
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• pp.767-775
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• 2023

This study was conducted to lay the foundation for considering the qualitative aspects of environmental resources by calculating the weight of each environmental resource to calculate the total amount of environmental resources in Jeju. By comparing and analyzing the results of the expert survey conducted in 2011 and the results of the expert survey conducted in 2011 and 2022, changes in experts' perceptions and implications over time were derived. In addition, based on the results of the recent survey, the weight according to the relative importance was calculated to lay the foundation for calculating the total amount of environmental resources in Jeju. The results of this study are expected to provide basic data necessary for the successful institutionalization of the total environmental resource system by providing a scientific basis for the calculation of the total environmental resource. As a result of comparing the survey conducted in 2011 to the survey conducted in 2022 to establish a total environmental resource management plan in Jeju Special Self-Governing Province, there was a difference in the relative importance of the environmental resource category. Although the ranking between categories did not change, it was confirmed that the relative importance of the natural and local resource environment decreased and the relative importance of the living environment field increased significantly. Over time, the importance of plants and wildlife increased, the importance of landscapes and topographic geology decreased, the importance of wetlands and caves increased, and the importance of Gotjawal, natural monuments, and cultural history decreased. In the living environment category, the importance of water pollution increased significantly, and in the humanities and social environment category, the importance of population increased and the importance of industry decreased. It is judged that most changes in item importance are largely influenced by changes in the background of the times and overall perception. It was confirmed that the importance of plants, wildlife, wetlands, and caves with relatively high awareness and the importance of water pollution, which is emerging as a regional problem, have all increased significantly due to structural problems of population age.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.131-136
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• 2023

Local youth outmigration is increasingly growing. Various studies are being conducted to identify the factors contributing to this problem, but there is a lack of research analyzing each region individually. Therefore, this study aims to analyze the factors influencing youth outmigration in Busan and predict the risk levels of youth population outflow using machine learning techniques. By utilizing district-level data collected from the KOSIS, we divided the population into three groups based on age (the early 20s, late 20s, and early 30s) and employed Decision Tree and Random Forest algorithms to classify and predict the risk levels of youth population outmigration. The results indicate that the predictive model for youth outmigration risk levels achieves the highest accuracies of 0.93, 0.75, and 0.63 for each age group, respectively.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.941-946
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• 2023

To improve the lift, cruise speed, and range of eVTOL aircraft, which are being considered as future transportation vehicles, this paper introduces the concepts of Tilt Rotor and Tandem Wing to the aircraft. We developed an aircraft and conducted flight experiments to obtain flight videos and flight logs. The results of the analysis of the flight videos and flight logs showed that the aircraft's moment was excessively forward and the attitude was not recovered. To address this problem, we modified the wing incidence angles and surface areas in XFLR5 to obtain the optimal pitching moment coefficients to ensure vertical stability. We then analyzed the redesigned aircraft, developed using CATIA, through XFLR5. The results of this study provide valuable insights, suggesting that the incorporation of Tilt Rotor and Tandem Wing designs can contribute to achieving stable pitching moment coefficients. This innovative approach offers a promising avenue to significantly enhance vertical stability in UAM vehicles, paving the way for future advancements in the field.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.317-332
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• 2023

Mathematical modeling activities hold the potential for diverse applications, involving the transformation of real-life situations into mathematical models to facilitate problem-solving. In order to assess the cognitive, affective, and social dimensions of students' engagement in mathematical modeling activities, this study conducted sessions with ten groups of fifth-grade elementary school students. The ensuing processes and outcomes were thoroughly analyzed. As a result, each group effectively applied mathematical concepts and principles in creating mathematical models and gathering essential information to address real-world tasks. This led to notable shifts in interest, enhanced mathematical proficiency, and altered attitudes towards mathematics, all while promoting increased collaboration and communication among group members. Based on these analytical findings, the study offers valuable pedagogical insights and practical guidance for effectively implementing mathematical modeling activities.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.310-323
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• 2023

Urbanization and income distribution issues are global interest, and the results of studies on the impact of urbanization on income inequality are different for each country and period. This study analyzes the impact of urbanization on income inequality using regional data from 2000-2021. In particular, serial correlation, spatial dependence, and common factor effects of the Gini coefficient are confirmed and analyzed through a dynamic spatial panel regression model. As a result, urbanization has a positive effect on reducing income inequality. Therefore, it is necessary to continuously promote regional urbanization to improve the income distribution problem. Areas with already high urbanization rates should reduce income inequality by narrowing the wage gap by expanding training opportunities for low-skilled workers, and need to come up with measures to prevent counter-urbanization.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.92-101
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• 2023

To solve the problem of climate change, carbon neutrality has now become a necessity rather than an option. Hydrogen is not only a energy storage that can supplement the intermittent production of renewable energy, but is also considered a good alternative in the field of utilization as it does not emit carbon dioxide after reaction. In order to revitalize hydrogen vehicles, one of the fields of hydrogen utilization, the construction of hydrogen station infrastructure must be preceded. Prioritization of risk factors is necessary for efficient operation and risk assessment of hydrogen stations, but due to the short operation period of domestic hydrogen stations, there is a lack of frequency data on accidents and their reliability is low. In this study, we aim to identify the causes and consequences of deviations in hydrogen stations through HAZOP analysis. Additionally, we intend to analyze them using Fuzzy-AHP. Through this, we intend to derive the decision values for the causes of deviations in hydrogen stations and apply them to hydrogen accident cases and risk assessments to confirm the reliability and utility of the data.

• The Journal of Korean Academic Society of Nursing Education
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• v.30 no.1
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• pp.29-38
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• 2024

Purpose: This study aimed to investigate student nurses' satisfaction by type of clinical practicum and to determine predictors of clinical competence in pediatric nursing. Methods: A total of 189 Junior and Senior student nurses across seven colleges in the Busan Metropolitan City were enrolled in the study. The participants completed a structured questionnaire containing items about their learning satisfaction with different types of pediatric nursing practicums and their clinical competence. Data were analyzed using the mean, standard deviation, independent t-test, ANOVA, and multiple regression analysis. Results: Regarding satisfaction with each type of clinical practicum, the mean satisfaction score (out of 10) was 8.18±2.26 for on-site clinical rotations and 7.35±2.20 for alternative practicums. Among the different types of alternative practicum approaches, those with a satisfaction score of 7 or higher included fundamental nursing skills, watching videos, simulation etc., while those with a satisfaction score of less than 6 were virtual simulation and problem-based learning. The predictors of clinical competence in pediatric nursing were learning satisfaction with practice, school year, and alternative practicum, accounting for 35.0% of the variance in clinical competency. Conclusion: It would be helpful to combine on-site clinical rotations with alternative practicum approaches and to develop various alternative practice programs using simulation practice, virtual reality, immersive interactive systems, and standardized patients to enhance students' clinical competency.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.384-391
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• 2023

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.