• Korean Medical Education Review
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• v.24 no.3
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• pp.250-260
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• 2022

Evaluating the effectiveness of educational programs involves measuring learning processes as well as outcomes. It is essential to study cohorts of students and graduates to evaluate the long-term effects of educational programs with data generated both during education and after graduation. The purpose of this study was to establish cohorts of students and graduates to evaluate their performance, thereby providing a basis for evaluating the social accountability of medical education. In this study, student and graduate cohorts were built for both students currently enrolled and graduates at Inje University College of Medicine (IUCM). A model involving the process of cohort establishment and an evaluation indicator framework was developed. In the process of cohort establishment, the following steps were conducted: defining the goals and objectives of the student and graduate cohorts, organizing a cohort committee, developing regulations, registering cohorts, acquiring consent, and building a database. A framework of evaluation indicators according to the graduate roles of IUCM was developed by adapting Kirkpatrick's evaluation model. Next, items to be collected in student and graduate cohorts were selected, and the current status of existing data was analyzed. Moreover, a preliminary analysis was conducted, including analyses of the evaluation indicators and graduates' performance. This study suggests that it is necessary to include additional evaluation indicators considering students' learning environment and well-being in student cohorts and to develop strategies or methods for graduates to continue participating in data collection for a long-term study.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.211-217
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• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient outcomes.

• Korean Journal of Applied Biomechanics
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• v.33 no.2
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• pp.62-72
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• 2023

Objective: Although balance training has been used as an effective ankle injury rehabilitation program to restore neuromuscular deficits in patients with chronic ankle instability, it is not effectively used in terms of motor learning. Attentional focusing can be an effective method for improving ankle kinematics to prevent recurrent ankle injuries. This study aimed to 1) evaluate the effects of attentional focus, including internal and external focus, and 2) determine a more effective focusing method for patients with chronic ankle instability to learn balance tasks. Method: Twenty-four patients with chronic ankle instability were randomly assigned to three groups (external focus, internal focus, and no feedback) and underwent four weeks of progressive balance training. The three-dimensional ankle kinematics of each patient were measured before and after training as the main outcomes. Ensemble curve analysis, discrete point analysis, and post hoc pairwise comparisons were performed to identify interactions between groups and time. Results: The results showed that (1) the external focus group was more dorsiflexed and everted than the internal focus group; (2) the external focus group was more dorsiflexed than the no feedback group; and (3) the no feedback group was more dorsiflexed than the internal focus group. Conclusion: Because dorsiflexion and eversion are ankle motions that oppose the mechanism of lateral ankle sprain, using the external focus method during balance training may be more effective in modifying these motions, thereby reducing the risk of ankle sprain.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.415-421
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• 2023

In this study, the piezoresistive properties of cementitious composites enhanced with carbon nanotubes for improved electrical conductivity were analyzed using a deep learning-based transformer algorithm. Experimental execution was performed in parallel for acquisition of training data. Previous studies on mixture design, specimen fabrication, chemical composition analysis, and piezoresistive performance testing are also reviewed in this paper. Notably, specimens in which fly ash substituted 50% of the binder material were fabricated and evaluated in this study, in addition to carbon nanotube-infused specimens, thereby exploring the potential enhancement of piezoresistive characteristics in conductive cementitious materials. The experimental results showed more stable piezoresistive responses in specimens with fly-ash substituted binder. The transformer model was trained using 80% of the gathered data, with the remaining 20% employed for validation. The analytical outcomes were generally consistent with empirical measurements, yielding an average absolute error and root mean square error between 0.069 to 0.074 and 0.124 to 0.132, respectively.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.133-139
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• 2024

In response to the challenges posed by a super-aged society, local authorities are conducting educational programs on smartphone usage tailored for the elderly. However, obstacles such as the limitations of one-to-many education and suboptimal learning outcomes for the elderly have hindered the efficacy of smartphone education. This study suggests an educational service intended for direct application in offline settings, considering the identified problems. Through the utilization of generative AI, the proposed app identifies specific challenges encountered by users during actual smartphone use, offering personalized exercises to facilitate customized and repetitive learning experiences for individual users. When integrated with existing local government education initiatives, this app is anticipated to enhance the efficiency of smartphone education by providing personalized, one-on-one training that is efficient in terms of time and content.

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.363-370
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• 2024

The escalating frequency and intensity of natural disasters and extreme weather events due to climate change have caused increasingly severe damage to societal infrastructure and buildings. Government agencies and private companies are actively working to evaluate these damages, but existing technologies and methodologies often fall short of meeting the practical demands for accurate assessment and prediction. This study proposes a novel approach to assess building damage resulting from natural disasters, focusing on typhoons-one of the most devastating natural hazards experienced in the country. The methodology leverages deep learning algorithms to evaluate typhoon-related damage, providing a comprehensive framework for assessment. The framework and outcomes of this research can provide foundational data for the evaluation of natural disaster-induced damage over the entire life cycle of buildings and can be applied in various other industries and research areas for assessing risk of damage.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.167-177
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• 2024

In nursing education, there has been efforts for enhancing the quality, with a growing interest in the utilization of digital technologies. In clinical training of nursing curriculum, the emphasis on digital technology is pronounced, as it has the potential to offer learners effective and accessible educational experience while enabling the integrated management of individualized learning outcomes. This study developed a digital nursing education platform, allowing educators and learners to select functionalities based on the educational content and characteristics of the learning tools. Additionally, the user interface was designed to facilitate learners' accurate understanding and execution of assigned tasks and objectives. The detailed design and implementation process of the platform are elaborated and then the validation of its usefulness was provided based on feedback from ten educators who are responsible for diverse subjects. The high usability of the digital nursing practicum education platform was confirmed, with potential implications for significant improvements in learner performance. The potential of this digital platform is to lead to innovative shifts in educational methodologies within the field of integrative nursing education.