• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.33-41
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• 2023

The application of seismic separation joints that can improve the deformation capacity of piping is an effective way to improve seismic performance. Seismic separation joints capable of axial expansion and bending deformation are installed where deformation is expected and used for the purpose of safely protecting the piping. Bellows are flexible and have low stiffness, so they can be used as seismic separation joints because they have excellent ability to respond to relatively large deformation. In this study, the seismic performance and limit state for bending deformation of 2-ply and 3-ply bellows specimens were evaluated. Seismic performance was evaluated by applying an increasing cyclic load to consider low-cycle fatigue due to seismic load. In order to confirm the margin for the limit state of the evaluated seismic performance, an experiment was conducted in which a cyclic loading of constant amplitude was applied. As a result of the experiment, it was confirmed that the bellows specimen was made of stainless steel and had a high elongation, so that the 2-ply bellows specimen had the limit performance of resisting within 3 cycles even at the maximum forced displacement of the 3-ply bellows specimen.

• Journal of Korea Entertainment Industry Association
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• v.13 no.7
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• pp.469-477
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• 2019

The purpose of this study was to develop Team Based Learning (TBL) course for students majoring skin care and analyze the effectiveness as well as derive improvement plans. TBL focuses on putting pre-class learning about learning content and the time saved by it into practice activities that apply what is learned. The ADDIE model and the TBL model were applied as the developmental research methodology, and the 'skin care' subject, which was previously taught by lecture base, was redesigned as a TBL class. In addition, the study developed weekly based pre-class learning materials, quiz items for checking pre-class learning, and the team activities' plan. Then, an experimental study was conducted with 43 university students and the effects of TBL instruction were analyzed as follows. First, students who participated in the TBL class showed higher achievement than those who participated in the lecture class, which is a comparative group, especially in the academic achievement that evaluated the acquisition and application of the major concept. Second, there was no significant difference in pretest and posttest results on problem solving ability and cooperative self-efficacy for TBL students. Third, overall satisfaction with TBL class was 4.0, which is high. The discussion of these findings was described, and three suggestions for improving and researching TBL classes were presented.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.717-736
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• 2023

The purpose of this study is to develop a statistical program using data and artificial intelligence prediction models and apply it to one class in the sixth grade of elementary school to see if it is effective in improving students' statistical literacy. Based on the analysis of problems in today's elementary school statistical education, a total of 15 sessions of the program was developed to encourage elementary students to experience the entire process of statistical problem solving and to make correct predictions by incorporating data, the core in the era of the Fourth Industrial Revolution into AI education. The biggest features of this program are the recognition of the importance of data, which are the key elements of artificial intelligence education, and the collection and analysis activities that take into account context using real-life data provided by public data platforms. In addition, since it consists of activities to predict the future based on data by using engineering tools such as entry and easy statistics, and creating an artificial intelligence prediction model, it is composed of a program focused on the ability to develop communication skills, information processing capabilities, and critical thinking skills. As a result of applying this program, not only did the program positively affect the statistical literacy of elementary school students, but we also observed students' interest, critical inquiry, and mathematical communication in the entire process of statistical problem solving.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.2
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• pp.1-12
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• 2023

In the current era of the global mobile smart device revolution, electronic devices are required in all spaces that people interact with. The establishment of the internet of things (IoT) among smart devices has been recognized as a crucial objective to advance towards creating a comfortable and sustainable future society. In-mold electronic (IME) processes have gained significant industrial significance due to their ability to utilize conventional high-volume methods, which involve printing functional inks on 2D substrates, thermoforming them into 3D shapes, and injection-molded, manufacturing low-cost, lightweight, and functional components or devices. In this article, we provide an overview of IME and its latest advances in application. We review biomimetic nanomaterials for constructing self-supporting biosensor electronic materials on the body, energy storage devices, self-powered devices, and bio-monitoring technology from the perspective of in-mold electronic devices. We anticipate that IME device technology will play a critical role in establishing a human-machine interface (HMI) by converging with the rapidly growing flexible printed electronics technology, which is an integral component of the fourth industrial revolution.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.65-74
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• 2024

Objective : This study assessed ChatGPT, an artificial intelligence system based on a large language model, for its ability to pass the National Korean Occupational Therapy Licensure Examination (NKOTLE). Methods : Using NKOTLE questions from 2018 to 2022, provided by the Korea Health and Medical Personnel Examination Institute, this study employed English prompts to determine the accuracy of ChatGPT in providing correct answers. Two researchers independently conducted the entire process, and the average accuracy of both researchers was used to determine whether ChatGPT passed over the 5-year period. The degree of agreement between ChatGPT answers of the two researchers was assessed. Results : ChatGPT passed the 2020 examination but failed to pass the other 4 years' examination. Specifically, its accuracy in questions related to medical regulations ranged from 25% to 57%, whereas its accuracy in other questions exceeded 60%. ChatGPT exhibited a strong agreement between researchers, except for medical regulation questions, and this agreement was significantly correlated with accuracy. Conclusion : There are still limitations to the application of ChatGPT to answer questions influenced by language or culture. Future studies should explore its potential as an educational tool for students majoring in occupational therapy through optimized prompts and continuous learning from the data.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.1
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• pp.1-10
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• 2024

In this study, a quercetin benton gel (QUERPLEX) that stabilized quercetin was prepared using organo hectorite, and its efficacy was confirmed. In addition, a comparative study was conducted on the stability and effectiveness of applying this to sunscreen cosmetics. It was confirmed that QUERPLEX remained stable without showing crystal precipitation and growth for 4 weeks. As a result of measuring antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl (DPPH), it showed antioxidant activity depending on the concentration, and showed a radical elimination ability of 70% or more at a concentration of 2,500 ppm or more, confirming a significant effect. Anti-inflammatory activity experiments using RAW 264.7 cells confirmed that NO production decreased in a concentration-dependent manner by reducing NO production by 8% at 25 ㎍/mL, 23% at 50 ㎍/mL, and 84% at 100 ㎍/mL. As a result of confirming the stability of the formulation according to the method of quercetin in the sunscreen formulation, the stability of the formulation was improved when quercetin was added directly to the formulation. It also improved the UV protection index on in vitro and in vivo, which is expected to have the potential as a component that can have a new boosting effect on UV protection. These results suggest that organo hectorite is very effective as a quercetin carrier and that it can be applied in cosmetic formulations by not only expressing the efficacy of quercetin but also bringing about additional effects.