• Journal of Digital Convergence
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• v.19 no.1
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• pp.1-10
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• 2021

The purpose of the study is to investigate the impact and analyze the effect of smart learning based class to on the students with low academic achievement level. The study performed in G University in 2018 among students taking calculus II class. It includes 16 students with low academic achievement level, whose grades were under C in the previous calculus I class. They belonged to special class consisted of very low academic achievement level and had to pass calculus II. 3D and AR were actively used in the class. The result shows that they got visual understanding of space, which revealed through analyzing SNS, mid-term and final examination, lecture evaluation. Also, smart learning based mathematics class utilizing smartphone's application elevated academic achievement level and influenced positively on the interest and attitude toward mathematics regardless of previous academic achievement level.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.73-83
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• 2023

This study is a descriptive survey to develop a gamification e-learning class based on self-determination theory and to check its effectiveness. The data collection period was from March 1 to June 15, 2023, and 59 students at G University in G Metropolitan City were surveyed on class participation, learning immersion, and teaching presence before and after the course. IBM SPSS/Win 26.0 was used to analyze the collected data, and descriptive statistics, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) were conducted. The results showed that the self-determination-based gamification class significantly improved students' class participation, learning engagement, and teaching presence (p<.05). An analysis of covariance (ANCOVA) was conducted to determine whether the general characteristics of the participants affected the results of the post-test, and gender affected the post-test results of learning engagement, with an effect of 7.9%. Based on the results of this study, it can be seen that self-determination-based gamification e-learning class is effective in improving learners' class participation, learning engagement, and teaching presence. As the demand for e-learning in universities is expanding, self-determination-based gamification e-learning classes should be developed in various fields of liberal arts and majors.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.78-80
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• 2022

최근 딥러닝을 이용한 영상 분석은 자율주행, 감시카메라 등 다양한 서비스에 필수적으로 활용되고 있으며 실시간 처리 및 보안 요소를 만족하기 위해 기존의 클라우드 컴퓨팅 방식의 단점을 개선한 클라우드 엣지 컴퓨팅 방식을 적용하는 사례가 크게 증가하고 있다. 하지만 사용자 및 단말과 가까운 위치에서 딥러닝 추론을 진행하는 클라우드 엣지 서버는 클라우드 서버와 비교하여 컴퓨팅 자원이 충분하지 않을 경우가 많으며 기존의 딥러닝 모델을 그대로 클라우드 엣지 환경에 적용하는 것은 자원 활용 측면에서 여러가지 문제점들을 갖고 있다. 따라서 본 논문에서는 마이크로서비스 구조를 통해 자원을 보다 유연하게 활용할 수 있도록 개선된 딥러닝 모델로 대규모의 클라이언트 요청을 처리 가능한 동영상 데이터 추론 서비스인 G-Edge AI 추론 서비스 개발에 대해 설명한다.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.312-322
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• 2010

The aim of this study is to find the effect of action learning on the problem solving process of nursing students during the clinical practice of nursing management. A total of 99 senior nursing students participated in this study. Data was collected from May 2006 to October 2007 and statistical analysis for paired t-test was performed on the data using SPSS/WIN 14.0. The results of the data show that there was a significant increase in the problem solving process for nursing students after the implementation of action learning(t=-4.718, p=.000). In the problem solving process, there was a significant increase in definition of problem(t=-4.123, p=.004), design of problem solution(t=-2.973, p=.002), execution of problem solution(t=-3.264, p=.000) and investigation of problem solving(t=-3.677, p=.000). The only exception in the problem solving process was detection of problem(t=-1.858, p=.066). Therefore, action learning provides nursing students a new alternative for improving the problem solving process and clinical adaptability after graduating from nursing school.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.283-292
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• 2021

As universities have operated non-face-to-face semesters due to COVID-19, although instructors applying flipped learning to their classes also have changed it into non-face-to-face ways, there is still a lack of exploratory research on effectiveness of the new form of flipped learning. In this study, we explored the effectiveness of the non-face-to-face flipped learning by analyzing students' learning experiences throughout FGI and survey. By doing so, we sought to provide in-depth insights for successful implications of non-face-to-face flipped learning classes ultimately. The findings showed that many learners positively evaluated non-face-to-face flipped learning in terms of interactions, including quizzes, team activities, and interpersonal interactions (e.g., Q&A, feedback) with professors in non-face-to-face flipped learning classes. The result of the survey also showed significant differences in the pre-post test regarding learner's perceived learning achievement. Based on these findings, the implications were discussed.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.1-12
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• 2023

The purpose of the study is to analyze the impact of blended learning's external classroom formats and internal teaching strategies, which has been implemented in university classes due to COVID-19, on students' academic achievement and learners' perceptions, as well as to provide insights into the desirable direction of online education. The study was conducted during the 1st semester of 2022 at G University, targeting students taking Calculus I. The experimental group consisted of 117 students, while the control group consisted of 707 students. Blended learning, involving a combination of face-to-face classes, online classes, and mixed teaching methods, was implemented, and academic achievement and learner perceptions were assessed. The research findings indicate that compared to solely online classes, adopting a blended learning approach with online classes before the midterm and face-to-face classes afterwards resulted in a decline in academic achievement. The unprepared and simplistic external format of blended learning was found to be ineffective, however, a blended learning model consisting solely of online classes, incorporating a mix of asynchronous and synchronous instruction, demonstrated positive learner perceptions. Additionally, utilizing technology in the teaching strategies yielded positive outcome.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.79-86
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• 2024

In this paper, we propose a virtual learning platform based on various interactions that occur during real class activities, rather than the existing content delivery-oriented learning metaverse platform. In this study, we provide a learning environment that combines AI and a virtual environment to solve problems by talking to real-time AI. Also, we applied G-learning techinques to improve class immersion. The Virtual Edu platform developed through this study provides an effective learning experience combining self-directed learning, simulation of interest through games, and PBL teaching method. And we propose a new educational method that improves student participation learning effectiveness. Experiment, we test performance on learninng activity based on real-time video classroom. As a result, it was found that the class progressing stably.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.224-229
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• 2020

We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.

• Convergence Security Journal
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• v.21 no.3
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• pp.57-66
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• 2021

As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.