• Journal of Environmental Health Sciences
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• v.46 no.6
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• pp.627-635
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• 2020

Objectives: Indoor air quality has become more important aspeople spend most of their times indoors. Since students spend most of their times at home or at school, they are more likely to be exposed to indoor air pollutants. Ventilation in school classrooms can affect health and learning performance. In this study, ventilation deficiency was evaluated in school classrooms using Monte Carlo simulation. Methods: This study used sensor-based monitoring for six months to measure carbon dioxide (CO2) concentrations in classrooms in elementary, middle, and high schools. The volume of the classroom and the number of students were investigated, and the students' body surface area was used to calculate the CO2 emission rate. The distribution of ventilation rates was estimated by measured CO2 concentration and a mass-balance model using Monte Carlo simulation. Results: In the elementary, middle, and high schools, the average CO2 concentrations exceeded 1000 ppm, indicating that the ventilation rates were insufficient. The ventilation rates were deficient from July to August and in December, but showed relatively high ventilation rates in October. Forty-three percent of elementary schools, 56% of middle schools, and 62% of high schools showed insufficient ventilation rates. Conclusions: The ventilation rates calculated in elementary, middle and high schools were found to be quite insufficient. Therefore, proper management is needed to overcome the lack of ventilation and improve air quality.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.99-108
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• 2021

To determine clinical nursing instructors' teaching efficacy, students' clinical practice satisfaction, and confirm between correlation, and develop a plan for operating nursing education efficiently for clinical practice. Clinical practice could create an optimal learning situation. We applied CNITEs and CPS to measure clinical nursing instructor teaching efficacy and clinical practice satisfaction. The differences in teaching efficacy by the general characteristics were measured and analyzed; the higher the level of the participants' education, position, clinical career, and clinical teaching career, the higher their teaching efficacy. The higher the age at clinical practice, the higher the clinical efficacy of clinical practitioners with clinical career and higher education level students were more satisfied with the practice subject and nursing instruction than other categories. Therefore, in order to increase the satisfaction of nursing students' practice in the clinical field, we hope to improve various things that can be used not only teaching efficacy but also in clinical practice satisfaction.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.205-215
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• 2022

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.305-332
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• 2022

This preliminary study examined the degree programs in data science at the School of Information in the States. The focus of this study was the data science degrees offered at the School of Information awarded by the 64 Library and Information Science (LIS) programs accredited by the American Library Association (ALA) in 2022. In addition, this study examined the degrees, majors, minors, specialized tracks, and certificates in data science, as well as the potential careers after earning a data science degree. Overall, eight Schools of Information (iSchools) offered 12 data science degrees. Data science courses at the School of Information focus on topics such as introduction to data science, information retrieval, data mining, database, data and humanities, machine learning, metadata, research methods, data analysis and visualization, internship/capstone, ethics and security, user, policy, and curation and management. Most schools did not offer traditional LIS courses. After earning the data science degree in the School of Information, the potential careers included data scientists, data engineers and data analysts. The researcher hopes the findings of this study can be used as a starting point to discuss the directions of data science programs from the perspectives of the information field, specifically the degrees, majors, minors, specialized tracks and certificates in data science.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.207-220
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• 2021

The purpose of the study is to investigate the application and utility of emergency rescue education and derive limitations, improvements and development directions of university education based on the field experience of 119 emergency medical technician(EMT)s. The research subjects were six new 119 emergency medical technician(EMT)s within three years of starting their first-aid service in the field. After conducting in-depth narrative interviews, the analysis was performed using Colaizzi method. The 82 formulated meanings were derived from significant statements. From formulated meanings, 23 themes, 4 theme clusters, 2 categories were identified. The four theme clusters were 'The effectiveness of university education', 'The limitations of university education', 'The direction of improvement in educational methodology' and 'The direction of improvement in educational contents. University education has been helpful overall, but limitations are observed at the same time, suggesting that it should be developed through the improvement of educational methodologies (i.e. problem-based learning, field case review, education through role-playing, simulation education, strengthening skill ect.) and educational content (i.e. training tailored to the field, education focused on trauma or cardiac arrest, expansion of triage education in disaster management, reinforcement of education on-site safety, education on special patients, diverse guidance and faculty for different perspectives).

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1643-1652
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• 2022

Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.121-127
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• 2022

Smart mirror is an IoT device that attaches a display and an embedded computer to the mirror and provides various information to the useer along with the mirror function. This paper went beyond the form of dealing with smart mirrors only stand alone device the provide information to users, and constructed a network in which smart mirrors are connected, and proposed and implemented a CoMirror system that allows users to talk and share information with other smart mirror users. The CoMirror system has a structure in which several CoMirror clients are connected on one CoMirror server. The CoMirror client consists of Raspberry Pi, a mirror film, a touch pad, a display device, an web camera, etc. The server has functions such as face learning and recognition, user management, a relay role for exchanging messages between clients, and setting up for video call. Users can communicate with other CoMirror users via the server, such as text, image, and audio messages, as well as 1:1 video call.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.457-468
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• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.