• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.471-477
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• 2022

In this study, an educational program was developed that allows students who take data science courses in the high school credit system to explore related fields after learning data science education. Accordingly, the existing research and requirements for data science education were analyzed, a learning plan was designed, and an educational program was developed in accordance with a step-by-step educational program. In addition, since there is no research on data science education for the high school credit system in existing studies, the research was conducted in the stages of problem definition, data collection, data preprocessing, data analysis, data visualization, and simulation, and referred to studies on data science education that have been conducted in existing schools. Through this study, it is expected that research on data science education in the high school credit system will become more active.

• Journal of Korean Clinical Nursing Research
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• v.29 no.3
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• pp.283-295
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• 2023

Purpose: The purpose of this study was to provide basic data to understand the organizational culture of nurses by categorizing nurses' experience of coping with bullying in the workplace through Q methodology and analyzing the characteristics of each type, and to induce correct policy measures and interventions to create an atmosphere created in the nursing clinical field to be more advanced and positive. Methods: To form the Q population, focus group interviews were conducted with nurses working for more than six months at two general hospitals in Seoul and Gyeonggi. Interviews were conducted by 12 nurses introduced to participants who can provide researchers with a wealth of information on workplace bullying experiences without filtration. In addition, the Q population was extracted by reviewing the results. Based on the results derived from this, 38 Q statements in total were extracted. Forty clinical nurses were required to classify Q sample statements, and the data collected through this were analyzed using the pc-QUANAL program. Results: As a result of the analysis, a total of five types of clinical nurses' experiences of coping with bullying in the workplace were identified: 'tense emotion-based tolerance response,' 'positive thinking-based self-effort response', 'individualistic thinking-based passive response', 'support system-based emotional expression response' and 'active response centered on problem-solving'. Conclusion: The derived response types are expected to be guidelines for suggesting strategies to eradicate bullying in the workplace at the organizational level, individual level, prevention level, and organizational culture level.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.197-205
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• 2023

This study is to apply the Havruta teaching-learning method to college students' major classes and analyze the relationship between the effectiveness evaluation of communication skills and sub-factors using a structural equation model. As a result of the study, the communication ability score was different before and after Havruta teaching-learning, and it was found that after Havruta teaching-learning was higher than before Havruta teaching-learning. The path effect was found to be significant in all of the total, direct, and indirect effects among latent variables, except for the relationship between interpretation ability, role-playing ability, and goal-setting ability in the direct effect. In this study, it was found that the Havruta teaching-learning method not only improves creativity and thinking ability, but also improves self-directed learning ability. In addition, it was reconfirmed that it is a teaching-learning method that can develop social skills and communication skills as well as problem-solving skills while experiencing opinions different from one's own. As a result, research on a thorough student-centered teaching-learning method suitable for the Homo Machina era must be continued and its application in the educational field must be implemented.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.329-349
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• 2023

With the rapid development of information and communication technology, the speed of data production has increased rapidly, and this is represented by the concept of big data. Discussions on quality and reliability are also underway for big data whose data scale has rapidly increased in a short period of time. On the other hand, small data is minimal data of excellent quality and means data necessary for a specific problem situation. In the field of culture and arts, data of various types and topics exist, and research using big data technology is being conducted. However, research on whether basic information about culture and arts institutions is accurately provided and utilized is insufficient. The basic information of an institution can be an essential basis used in most big data analysis and becomes a starting point for identifying an institution. This study collected data dealing with the basic information of culture and arts institutions to define common metadata and constructed small data in the form of a knowledge graph linking institutions around common metadata. This can be a way to explore the types and characteristics of culture and arts institutions in an integrated way.

• Journal of Hydrogen and New Energy
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• v.34 no.6
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• pp.673-681
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• 2023

Hydrocarbon-based polymer membranes to replace perfluorinated polymer membranes are being continuously researched. However, hydrocarbon-based membranes have a problem in that they are less durable than fluorine-based membranes. In this study, we sought to compare the annealing effect to improve the durability of sulfonated poly(ether ether ketone) (SPEEK). After membranes formation, thermogravimetric analysis and tensile strength were measured to compare changes in membranes properties due to annealing. After manufacturing the membrane and electrode assembly (MEA), the initial performance and chemical durability was compared with unit cell operation. During the 24-hour annealing process, the strength increased due to the increase in-S-O-S-crosslinking, and the sulfonic acid group decreased, leading to a decrease in I-V performance. By annealing, the hydrogen permeability was reduced to less than 1/10 of that of the nafion membrane, and as a result, open circuit voltage (OCV) and durability was improved. The SPEEK membranes annealed for 24 hours showed higher durability than the nafion 211 membranes of the same thickness.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• 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.