• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.187-193
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• 2022

This study aimed to find out the learning outcomes of the program belonging to law and ethics, which is the category of the 4th cycle nursing certification evaluation, and to find out the direction of health care laws and regulations belonging to the related category accordingly. In the 4th cycle, 4 sub-competences were presented, and "learning outcomes are to practice nursing in accordance with law and ethics". Planning is recommended. In particular, health care laws and regulations are an important subject in the national examination for nurses, and the number of students who failed in the recent national examination for nurses was 1st to 2nd. However, if these subjects are recognized only as memorization-oriented national tests, the learning outcomes of the Nursing Education Accreditation and Evaluation Institute cannot be achieved. In particular, with the demand for high-level nursing care as a nurse and the expansion of work, related responsibilities and duties are increasing, so it is necessary to recognize the importance of achieving learning outcomes for health care laws and regulations and to make efforts to link learning outcomes.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.349-362
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• 2024

We aimed to evaluate the effectiveness of ensemble optimal interpolation (EnOI) in improving the analysis of significant wave height (SWH) within wave models using satellite-derived SWH data. Satellite observations revealed higher SWH in mid-latitude regions (30° to 60° in both hemispheres) due to stronger winds, whereas equatorial and coastal areas exhibited lower wave heights, attributed to calmer winds and land interactions. Root mean square error (RMSE) analysis of the control experiment without data assimilation revealed significant discrepancies in high-latitude areas, underscoring the need for enhanced analysis techniques. Data assimilation experiments demonstrated substantial RMSE reductions, particularly in high-latitude regions, underscoring the effectiveness of the technique in enhancing the quality of analysis fields. Sensitivity experiments with varying ensemble sizes showed modest global improvements in analysis fields with larger ensembles. Sensitivity experiments based on different decorrelation length scales demonstrated significant RMSE improvements at larger scales, particularly in the Southern Ocean and Northwest Pacific. However, some areas exhibited slight RMSE increases, suggesting the need for region-specific tuning of assimilation parameters. Reducing the observation error covariance improved analysis quality in certain regions, including the equator, but generally degraded it in others. Rescaling background error covariance (BEC) resulted in overall improvements in analysis fields, though sensitivity to regional variability persisted. These findings underscore the importance of data assimilation, parameter tuning, and BEC rescaling in enhancing the quality and reliability of wave analysis fields, emphasizing the necessity of region-specific adjustments to optimize assimilation performance. These insights are valuable for understanding ocean dynamics, improving navigation, and supporting coastal management practices.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.271-283
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• 2024

The study seeks to examine the alignment between the Model of Human Occupation (MOHO) and the Six Principles of the Individuals with Disabilities Education Act (IDEA) through qualitative analysis. The study utilizes a qualitative methodology that entails a comprehensive review of the existing literature to establish connections between MOHO and Individuals with Disabilities Education Act (IDEA) principles, with a specific focus on collaborative special education environments. Data collection involves examining academic literature on MOHO, Individuals with Disabilities Education Act (IDEA) principles, and the partnership between occupational therapists and special education teachers. Thematic analysis is employed to identify recurrent themes and relationships, offering valuable insights into the theoretical foundations of MOHO and its compatibility with the Individuals with Disabilities Education Act (IDEA)The Model of Human Occupation (MOHO) highlights the significance of active engagement and meaningful participation in inclusive education. It promotes the development of independence and self-determination in occupational performance for children with special needs. Moreover, MOHO stresses the importance of offering tailored support and adjustments for these children.

• Journal of the Korea Institute of Building Construction
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• v.24 no.4
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• pp.471-482
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• 2024

Modular construction, a prominent method in the evolving construction industry, necessitates robust quality control for successful implementation. This study investigates the potential of 3D scanning technology for enhancing quality control processes in modular building construction. Through an IPA analysis of major construction projects across factory production, transportation, and on-site stages, the study evaluates the current state of 3D scanning application in modular construction quality control. Results indicate a high demand for 3D scanning data across various quality control aspects. However, certain limitations in technology and practical application were identified. The findings of this research contribute to the advancement of 3D scanning technology in modular construction and inform future research on cutting-edge quality control strategies.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Safety and Health at Work
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• v.15 no.2
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• pp.208-212
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• 2024

Background: Traffic accidents involving professional drivers have serious societal repercussions. Unique occupational stressors and health risks exacerbate the likelihood of traffic accidents among professional drivers. This study explores the association between presenteeism-impaired work performance due to working while unwell-and traffic accident risk among professional taxi drivers in Japan. Methods: A prospective cohort study was conducted from June 2022 to February 2023, involving taxi drivers from a single company in Fukuoka Prefecture, Japan. Presenteeism was assessed using the Work Functioning Impairment Scale (WFun). Primary outcome involved the number of self-reported minor traffic accidents. The incidence rate ratio (IRR) of minor traffic accident occurrences was estimated using a Poisson regression analysis, adjusted for confounders including sex, age, and driving experience. Results: Of 838 targeted drivers, 435 were included in the analysis. Higher baseline work functioning impairment was associated with a significant trend of increasing IRR of minor traffic accidents (p for trend = 0.045). A dose-response relationship was seen between the degree of presenteeism and incidence rate of minor traffic accidents. Conclusion: Higher levels of presenteeism were associated with an increased risk of traffic accidents among taxi drivers. The findings underscore the need for socio-economic support and prioritized health management to mitigate traffic accident risk among professional drivers. This study highlights the importance of managing non-critical health issues alongside serious health conditions for safer driving practices among professional drivers in Japan.

• Smart Media Journal
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• v.13 no.7
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• Journal of Platform Technology
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• v.12 no.3
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• pp.55-61
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• 2024

As the digital environment becomes more complex and cyber attacks become more sophisticated, the importance of data protection is emerging. As various security threats such as data leakage, system intrusion, and authentication bypass increase, secure key management is emerging. Key Management System (KMS) manages the entire encryption key life cycle procedure and is used in various industries. There is a need for a key management system that considers requirements suitable for the environment of various industries including public and finance. The purpose of this paper is to derive the characteristics of the key management system for each industry by comparing and analyzing key management systems used in representative industries. As for the research method, information was collected through literature and technical document analysis and case analysis, and comparative analysis was conducted by industry sector. The results of this paper will be able to provide a practical guide when introducing or developing a key management system suitable for the industrial environment. The limitations are that the analyzed industrial field was insufficient and experimental verification was insufficient. Therefore, in future studies, we intend to conduct specific performance tests through experiments, including key management systems in various fields.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.119-126
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• 2024

This study aims to evaluate the feasibility of an early warning system for college dropout by machine learning the main patterns that affect college student dropout and to suggest ways to implement a system that can actively prevent it. For this purpose, a performance comparison experiment was conducted using five types of machine learning-based algorithms using data from the Korean Educational Longitudinal Study, 2005, conducted by the Korea Educational Development Institute. As a result of the experiment, the identification accuracy rate of students with the intention to drop out was up to 94.0% when using Random Forest, and the recall rate of students with the intention of dropping out was up to 77.0% when using Logistic Regression. It was measured. Lastly, based on the highest prediction model, we will provide counseling and management to students who are likely to drop out, and in particular, we will apply factors showing high importance by characteristic to the counseling method model. This study seeks to implement a model using IT technology to solve the career problems faced by college students, as dropout causes great costs to universities and individuals.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.41-48
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• 2024

The construction industry is a significant contributor to carbon emissions, with its life cycle emissions posing significant environmental challenges. Despite its increasing importance, embodied carbon (EC) generated from the construction process is often ignored. Modular construction (MC), characterized by a combination of off-site manufacturing and on-site assembly, has been recognized for its potential to contribute to environmental benefits. However, there is still a lack of systematic explanation of urban high-rise MC. This study aims to identify whether and to what extent high-rise MC can achieve EC reductions and lay the foundation for effective carbon reductuons in the construction industry. To achieve this, the study develops a multi-level EC measurement framework for assessing EC during the construction process, using a real case to quantify the EC and determine carbon reduction performance. The innovation is a more comprehensive understanding of the boundaries of EC, as MC includes the amount of superstructure work and decoration integration. The results show that although the MC will increase EC from the transportation stage due to heavier modules, it achieves a net reduction in total EC by reducing on-site machinery energy consumption and waste rates. In conclusion, this study contributes to a better understanding of the EC emissions associated with high-rise MC, offering a valuable measurement framework for global regions evaluating the EC impacts of high-rise MC in similar contexts.