• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1749-1755
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• 2021

Virtual reality is actively applied in the fields of games, entertainment, communication, sports, and architecture. In particular, many virtual reality-based education systems are being developed in the field of education, creating efficient learning effects. In addition, virtual reality-based education is used in areas such as maintenance, fighter control, medical care, and firefighting as it can maximize the educational effect through the mastery process of the function itself through the curriculum as well as indirect experiences of dangerous situations that are difficult to experience. However, due to technical limitations, lack of contents, and lack of theoretical research, the level of application of military education and training is still insufficient. This paper aim to contribute to the development of a virtual reality-based education system as a military training system by developing a high-quality drop training system applicable to military group descent training, studying key technologies and implementation methods necessary for development.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.67-74
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• 2022

The purpose of this study was to develop and apply a mental simulation practice program using standardized patients for nursing students and to verify the effectiveness. This study is a single-group pre- and post-design study, and a structured questionnaire was provided to 186 nursing students at a university in J for data collection. The collected data were analyzed using SPSS/WIN 27.0 program. As a result of the study, the mental simulation practice education program using standardized patients showed the subjects' communication ability (t=-2.575, p=001), learner self-efficacy (t=-2.228, p=.026) and problem-solving ability (t=-2.298, p=.017) was found to be effective. As a result of this study, it is necessary to develop and apply a simulation practice education program that creates an environment similar to the actual situation and applies various cases using the necessary resources to improve the field adaptation ability of nursing students.

• Journal of Microbiology and Biotechnology
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• v.32 no.6
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• pp.699-708
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• 2022

Antibiotic exposure during pregnancy have an adversely effects on offspring behavior and development. However, its mechanism is still poorly understood. To uncover this, we added ceftriaxone sodium to the drinking water of rats during pregnancy and conducted three-chamber sociability test, open-field test, and Morris water maze test in 3- and 6-week-old offspring. The antibiotic group offspring showed lower sociability and spatial learning and memory than control. To determine the role of the gut microbiota and their metabolites in the changes in offspring behavior, fecal samples of 6-week-old offspring rats were sequenced. The composition of dominant gut microbial taxa differed between the control and antibiotic groups. KEGG pathway analysis showed that S24-7 exerted its effects through the metabolic pathways including mineral absorption, protein digestion and absorption, Valine, leucine, and isoleucine biosynthesis. Correlation analysis showed that S24-7 abundance was negatively correlated with the level of VEGF, and metabolites associated with S24-7-including 3-aminobutanoic acid, dacarbazine, L-leucine, 3-ketosphinganine, 1-methylnicotinamide, and N-acetyl-L-glutamate-were also significantly correlated with VEGF levels. The findings suggest that antibiotic exposure during pregnancy, specifically ceftriaxone sodium, will adversely affects the behavior of offspring rats due to the imbalance of gut microbiota, especially S24-7, via VEGF and various metabolic pathways.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.202-215
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• 2022

The purpose of this study is to identify gamification techniques and characteristics of digital marketing based on the main information communication, learning, and play of the current consumer group, and to present effective gamification digital marketing plans for the MZ generation. The summary of the research process is as follows. First, the characteristics and definitions of MZ generation and gamification were described and the concept was clarified. Second, domestic and foreign gamification cases were compared and analyzed. Studies show that we should be wary of gamification digital marketing, which fails to reflect the characteristics of the fun-seeking MZ generation by failing to organically connect the mechanisms and structures of gamification, focusing only on visible elements, such as Point, Badge, and Leaderboard. In addition, customers who lose the fun of obtaining rewards and leave because they feel that the rewards (points, badges, leaderboards) they provide are worthless should be prevented.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.77-84
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• 2021

In this paper, we propose a system that uses image data and beacon data to classify authorized and unauthorized perosn who are allowed to enter a group facility. The image data collected through the IP camera uses YOLOv4 to extract a person object, and collects beacon signal data (UUID, RSSI) through an application to compose a fingerprinting-based radio map. Beacon extracts user location data after CNN-LSTM-based learning in order to improve location accuracy by supplementing signal instability. As a result of this paper, it showed an accuracy of 93.47%. In the future, it can be expected to fusion with the access authentication process such as QR code that has been used due to the COVID-19, track people who haven't through the authentication process.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.563-571
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• 2022

This study aimed to examine the effects of simulation-based neonatal nursing care education on the communication competence, self-efficacy, clinical competency. A one-group pre-and post test design was used. A total of 122 students participate. Data was collected from May 3 to June 4, 2021, using self-report questionnaires only for students who understood the purpose of the study and gave written consent to participate. For the simulation-based neonatal nursing care education, a total of three steps of preparation for scenario implementation, scenario implementation, and debriefing were applied in groups of 3-4 people of 4.5 hours. As a result of this study, it was found that the simulation-based neonatal nursing care education had statistically significant improvement in communication competence, self-efficacy, clinical competency. Through the results of this study, it was confirmed that simulation-based education in the nursing care of children can be an effective teaching-learning method that can supplement the observation-oriented clinical practice of child nursing for nursing students.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Human Ecology Research
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• v.60 no.4
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• pp.484-496
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• 2022

The purpose of this study was implementing the high-school 'Food Safety and Health' curriculum using the workbook, and to evaluate the educational impact on, and satisfaction of student who participated in class. A total of 109 lessons were undertaken in home economics classes by referring to the 'Food Safety and Health' workbook for 1,154 students attending seven high schools located in seven cities and provinces across the Korea. In order to examine the effects of classes by referring to workbooks, pre- and post-evaluations were conducted by devising a questionnaire about dietary behavior associated with food safety, creative problem-solving abilities, community capacities, and social cooperation capacities. The results of the analysis of the collected data from 674 students who participated in the pre- and post-evaluations are as follows. First, according to the results of the paired t-test conducted to examine the effects of using the workbook in classes on dietary behavior, significant positive changes were observed in the dietary behavior related to food safety, creative problem-solving skills, community consciousness, and social cooperation capabilities. Second, as a result of the students' evaluation of classes by referring to the 'Food Safety and Health' workbook, both satisfaction and interest in the class using the workbook were high, and the difficulty level was deemed to be appropriate. Additionally, it was found that the students actively participated in learning activities. The reason for this appears to be that the aforementioned workbook consisted of various student activities such as experiments, practical exercises, and group activities aimed at strengthening the link between life and education, thus enabling increased student participation.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.