• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.673-681
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• 2021

This study is a case study of artificial intelligence education subjects in the graduate school of education. The main educational contents consisted of understanding and practice of machine learning, data analysis, actual artificial intelligence using Entries, artificial intelligence and physical computing. As a result of the survey on the educational effect after the application of the curriculum, it was found that the students preferred the use of the Entry AI block and the use of the Blacksmith board as a physical computing tool as the priority applied to the elementary education field. In addition, the data analysis area is effective in linking math data and graph education. As a physical computing tool, Husky Lens is useful for scalability by using image processing functions for self-driving car maker education. Suggestions for desirable AI education include training courses by level and reinforcement of data collection and analysis education.

• Korean Medical Education Review
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• v.23 no.2
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• pp.80-89
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• 2021

Professional identity formation (PIF) is an essential concept in professional education. Many scholars have explored conceptual frameworks of PIF and conducted empirical studies to advance an understanding of the construct in medical education. Despite its importance, it is unclear what educational approaches and assessment practices are actually implemented in medical education settings. Therefore, we conducted a literature review of empirical studies reporting educational practices for medical learners' PIF. We searched the Web of Science database using keywords and chose 37 papers for analysis based on inclusion and exclusion criteria. Thematic analysis was conducted. Most empirical papers (92%) were from North America and Western Europe and used qualitative research methods, including mixed methods (99%). The papers reported the use of reflection activities and elective courses for specific purposes, such as art as an educational activity. Patient and healthcare experiences were also found to be a central theme in medical learners' PIF. Through an iterative analysis of the key themes that emerged from the PIF studies, we derived the following key concepts and implications: (1) the importance of creating informal and incidental learning environments, (2) ordinary yet authentic patient experiences, (3) a climate of psychosocial safety in a learning environment embracing individual learners' background and emotional development, and (4) the reconceptualization of PIF education and assessment. In conclusion, research on PIF should be diversified to include various cultural and social contexts. Theoretical frameworks should also be diversified and developed beyond Kegan's developmental framework to accommodate the nonlinear and dynamic nature of PIF.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.118-127
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• 2021

This study aims to examine satisfaction of nursing students with online classes during first semester of 2020 after COVID-19 pandemic and the difference in satisfaction according to general and online-related characteristics. An online survey was conducted for all nursing students, and subsequently 627 responses were analyzed by t-test and ANOVA with SPSS WIN. Result reveals that students ability to use IT devices was above average, and most of them used laptop computers. Pre-recorded video lecture format was used the most, and improvement of online content was demanded the highest. Overall satisfaction with online classes was scored 3.0/5.0, with the highest satisfaction for anytime and anywhere learning, and the lowest satisfaction in recommending online classes to others. There were significant differences between self-evaluation on own grade, ability to use IT devices, format of online classes, and satisfaction about online classes. Through this study, it would be possible to suggest a plan to increase satisfaction of online class and basic data to establish university policy for online classes after COVID-19.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.485-506
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• 2020

The purpose of this study is to analyze of the effect in Mathematics Teachers beliefs on their students beliefs by Latent Class Regression Model (LCRM). For this analysis, the study used the findings and surveys of Kang, Hong (2020) who developed a belief profile by analyzing the mathematical beliefs of 60 high school teachers and 1,850 second-year high school students learning from them through the Latent Class Analysis (LCA). As a result It was observed that 'Nature of Mathematics', 'Mathematic Teaching' and 'Mathematical Ability' of mathematics teachers beliefs influence the mathematical beliefs of students. The teacher's belief of 'Nature of Mathematics' statistically significant effects on students' beliefs in 'School Mathematics', 'Problem Solving', 'Mathematics Learning'. The teacher's belief of 'Teaching Mathematics', 'Mathematical Ability' statistically significant effects on students' beliefs in 'School Mathematics', 'Problem Solving', 'Self-Concept'. The results of this study can give a preview of the phenomenon in which teacher's mathematical beliefs are reproduced into student's mathematical beliefs. In addition, the results of observation of this study can be used to the contents that can achieve the purpose of reorientation for mathematics teachers.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.140-147
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• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1330-1339
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• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.409-419
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• 2020

The study selected the K Sand Art Lifelong Learning Instructor Circle as an example, focusing on learner interest-inducing factor and educational effects of Sand Art techniques, which are gaining interest in lifelong education. First, the factors that increase the interest-inducing effect of learners are to recognize sand art techniques as sand games, to tell stories and tones, to express them in sandboxes, and to express subjects of interest to learners. Second, in the field of lifelong education, sand art techniques are educationally effective in psychological therapy, improving concentration, improving self-confidence, developing expressiveness and creativity, and developing five senses. Third, sand art techniques are applied to all generations in the field of lifelong education, and sand art techniques can be used in civic participation education, basic literacy education, and culture and arts education among the six classes of lifelong education. Fourth, future tasks will be to support the training of sand art experts, support sand art materials and equipment, and spread sand art programs that visit various targets.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of Korean Clinical Health Science
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• v.10 no.1
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• pp.1543-1552
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• 2022

Purpose: It is intended to investigate the satisfaction of dental hygiene students with non-face-to-face online classes and use them as basic data for successful lecture design and operation. Methods: The data collected in this study were analyzed using the lBM SPSS Statistics 21 program. The general characteristics of the study subjects were frequency analysis, non-face-to-face online class satisfaction, and test satisfaction were frequency analysis and technical statistics. Through the independent sample T test, a t-test was conducted to find out whether there was an average difference in online class and test satisfaction according to grade. Results: The advantages of non-face-to-face online classes were that repetitive learning was possible (57.7%), the disadvantage was that there was a lack of real-time communication (74.9%), and the most efficient teaching method was a mixed form of online and face-to-face classes (64.9%). The satisfaction level of online classes was 2.69 points for 'self-directed learning habits,' which was the highest compared to the overall average of 2.55 points, and 2.09 points for 'difficulty in interaction between instructors and learners in online classes.'Non-face-to-face test satisfaction was 2.68 points for 'short test time gives fairness to test results,' higher than the overall average of 2.45 points, and 2.07 points for 'no difficulty accessing the test.'In terms of satisfaction with the non-face-to-face test according to the grade, it was found that the third grade showed a more negative attitude than the second grade in terms of sexual fairness (p<0.05). Conclusions: Through the above results, non-face-to-face online classes require various content development and some mixed classes considering the level of students, and instructors' efforts to improve the quality of classes for interaction between instructors and learners are needed.