• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.34-34
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• 2023

The Chah-Nimeh reservoirs, which are a sort of natural lakes located in the border of Iran and Afghanistan, are the main drinking and agricultural water resources of Sistan arid region. Considering the occurrence of intense seasonal wind, locally known as levar wind, this study aims to explore the possibility to provide a TSM (Total Suspended Matter) monitoring model of Chah-Nimeh reservoirs using multi-temporal satellite images and in-situ wind speed data. The results show that a strong correlation between TSM concentration and wind speed are present. The developed empirical model indicated high performance in retrieving spatiotemporal distribution of the TSM concentration with R²=0.98 and RMSE=0.92g/m³. Following this observation, we also consider a machine learning-based model to predicts the average TSM using only wind speed. We connect our in-situ wind speed data to the TSM data generated from the inversion of multi-temporal satellite imagery to train a neural network based mode l(Wind2TSM-Net). Examining Wind2TSM-Net model indicates this model can retrieve the TSM accurately utilizing only wind speed (R²=0.88 and RMSE=1.97g/m³). Moreover, this results of this study show tha the TSM concentration can be estimated using only in situ wind speed data independent of the satellite images. Specifically, such model can supply a temporally persistent means of monitoring TSM that is not limited by the temporal resolution of imagery or the cloud cover problem in the optical remote sensing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.155-163
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• 2024

This study was conducted to determine the relationship between nursing students' empathy, communication skills, and major immersion, and to identify factors that affect major immersion. The subjects of the study were 247 nursing students from one university in the G province, and data collection was conducted from November 20 to December 1, 2023. Data analysis was performed using SPSS 22.0 using descriptive statistics, t-test, one-way ANOVA, Scheffe test, Pearson correlation coefficient, and multiple regression analysis. The subject's empathy ability was 3.49 points, communication ability was 3.64 points, and major immersion was 3.61 points. Major immersion has a positive correlation with empathy and communication ability, and the factors affecting major immersion are major satisfaction and communication ability, with an explanatory power of 42.9%. To improve nursing students' immersion in their major, it is necessary to set the direction of learning activities and develop and apply various extracurricular programs.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• The Korean Journal of Nuclear Medicine
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• v.38 no.1
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• pp.21-29
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• 2004

Purpose: Much evidence suggests long-term cigarette smoking alters coronary vascular endothelial response. On this study, we applied nonnegative matrix factorization (NMF), an unsupervised learning algorithm, to CO-less $H_2^{15}O-PET$ to investigate coronary endothelial dysfunction caused by smoking noninvasively. Materials and methods: This study enrolled eighteen young male volunteers consisting of 9 smokers $(23.8{\pm}1.1\;yr;\;6.5{\pm}2.5$ pack-years) and 9 nonsmokers $(23.8{\pm}2.9 yr)$. They do not have any cardiovascular risk factor or disease history. Myocardial $H_2^{15}O-PET$ was performed at rest, during cold ($5^{\circ}C$) pressor stimulation and during adenosine infusion. Left ventricular blood pool and myocardium were segmented on dynamic PET data by NMF method. Myocardial blood flow (MBF) was calculated from input and tissue functions by a single compartmental model with correction of partial volume and spillover effects. Results: There were no significant difference in resting MBF between the two groups (Smokers: 1.43 0.41 ml/g/min and non-smokers: $1.37{\pm}0.41$ ml/g/min p=NS). during cold pressor stimulation, MBF in smokers was significantly lower than 4hat in non-smokers ($1.25{\pm}0.34$ ml/g/min vs $1.59{\pm}0.29$ ml/gmin; p=0.019). The difference in the ratio of cold pressor MBF to resting MBF between the two groups was also significant (p=0.024; $90{\pm}24%$ in smokers and $122{\pm}28%$ in non-smokers.). During adenosine infusion, however, hyperemic MBF did not differ significantly between smokers and non-smokers ($5.81{\pm}1.99$ ml/g/min vs $5.11{\pm}1.31$ ml/g/min ; p=NS). Conclusion: in smokers, MBF during cold pressor stimulation was significantly lower compared wi4h nonsmokers, reflecting smoking-Induced endothelial dysfunction. However, there was no significant difference in MBF during adenosine-induced hyperemia between the two groups.

• Journal of Korean Home Economics Education Association
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• v.21 no.3
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• pp.45-64
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• 2009

The purpose of this study was to develope issue-centered teaching learning process plan for environment-friendly housing education and to apply it to the housing section of Technology Home Economics in a middle school. PRO-CON cooperative group model was used for the teaching learning process plans of 2-session lessons according to the ADDIE model. In the development stage, 7 activity materials and 20 teaching learning materials (4 reading texts, 12 pictures and photos, & 5 moving pictures) were developed for 2-session lessons. The plans applied to the 7 classes, 222 students, in the third grade of the G middle school in Gyeonggi-do during July 10th-17th, 2008. The results showed that the final pro-con was influenced by the rationals of juries' pro-con of each team and the representative's discussion besides one's environmental perspective. The intention to implement environment-friendly housing activities was significantly increased between before and after the lessons. The contents, methods, goals, and process of the 2-session lessons were evaluated over medium to somewhat higher levels. Those evaluations except methods and general satisfaction with the lessons were differed by sex, students' and their families' interests in environments but not by the type of housing. These results might support that pro-con cooperative group model of controversial issues on parking lot would be appropriate to environment-friendly housing lessons and could apply to broad issues on housing and various schools in other areas.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.2
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• pp.202-214
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• 2015

Purpose: The purpose of this study was to develop extracurricular coaching programs to improve communication skills and leadership for nursing students and evaluate the effects of the programs. Methods: The 8-week extracurricular coaching program was developed based on the Joo, Whitmore and Hong models. A quasi-experimental design was used. The subjects were selected by two full-time nursing professors training students at one university in city G. The subjects were chosen from among the advisees of these two professors. Of the students who participated in this study, 29 were in the experimental group and 27 were in the control group. Data was analyzed through t-test and Mann Whitney U-test. Results: The experimental group showed significantly higher post-test scores in communication skills, communication as a nursing outcome, observation of communication, leadership, and leadership as a nursing outcome than those of the control group. However, the experimental group did not reveal significantly higher post-test scores in the number of leadership activities using a portfolio than those of the control group. Conclusion: This extracurricular coaching program can help cultivate important, basic grounding as well as achieve nursing student learning outcomes upon graduation.

• Journal of Korea Game Society
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• v.12 no.1
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• pp.25-32
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• 2012

In recently, one of the most interests is a field related with serious game. Most game users perform the first for the executing of tutorial before they use a serious game for the first time. But in most case, developers do not recognize importance of tutorial and they do not consider the type of user preference tutorial and make tutorials only as developer's intentions. In our study, we carry out investigation and analysis about type of providing tutorials in representative serious games. Also, we do a survey of users' preferences for types of tutorials. If developers use the result of our study and we can expect more convenient and good quality of tutorial development.