• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.1058-1068
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• 2013

The purpose of this study was to examine how participation in R-Learning university club management program affects to R-Learning professionalism of pre-teachers in field of early childhood education related to knowledge, function, and attitude. Upon investigation for knowledge part, those answers: 'I know the role of teachers when education based on robot, 'I know how much education based on robot affects to development of early childhood', and 'I know the necessary of education based on robot' appear highly. 'I can give lessons by connecting robot and computer' for function part, and 'I think using robot for class positively' for attitude part show highly. Also, professionalism of the pre-teachers improved after participating in R-running club, especially, function and attitude part. Thus, R-Learning university club management program is effective by the research.

• Journal of the Korea Convergence Society
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• v.1 no.1
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• pp.1-15
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• 2010

The Education has responsibility for predicting the social changes and cultivating global talent which the society needs. The ministry of education, science and technology in govern ment has been the concerns on social educational changes and thus built the '5 31 educational reform policy' in 1995 by the educational reform committee. As a solution of a social change, this paper reviews the three-phase educational information policies, and e-learning and u-learning which are the main technologies in educational information. Also, the technologies of e-learning can be divided into m-learning, t-learning, u-learning, r-learning, game-based learning according to the contents mass media. Among them, this paper introduces the concept of robot-learning, called r-learning, and compares it with u-learning.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.85-90
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• 2023

Data analysis methodology in the healthcare field is shifting from traditional statistics-oriented research methods to predictive research using machine learning. In this study, we survey various machine learning tools, and compare several programming models, which utilize R and Spark, for applying R, a statistical tool widely used in the health care field, to machine learning. In addition, we compare the performance of linear regression model using scala, which is the basic languages of Spark and R. As a result of the experiment, the learning execution time when using SparkR increased by 10 to 20% compared to Scala. Considering the presented performance degradation, SparkR's distributed processing was confirmed as useful in R as the traditional statistical analysis tool that could be used as it is.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1177-1183
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• 2018

Recent developments in machine learning have attracted a lot of attention for techniques such as machine learning and deep learning that implement artificial intelligence. In this paper, binary malicious code using deep learning based R-CNN is imaged and the feature is extracted from the image to classify the family. In this paper, two steps are used in deep learning to image malicious code using CNN. And classify the characteristics of the family of malicious codes using R-CNN. Generate malicious code as an image, extract features, classify the family, and automatically classify the evolution of malicious code. The detection rate of the proposed method is 93.4% and the accuracy is 98.6%. In addition, the CNN processing speed for image processing of malicious code is 23.3 ms, and the R-CNN processing speed is 4ms to classify one sample.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.469-481
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• 2018

DL (Deep Learning) is getting popular in various fields to implement artificial intelligence that resembles human learning and cognition. DL based on complicate structure of the ANN (Artificial Neural Network) requires computing power and computation cost. Variety of DL models with improved performance have been developed with powerful computer specification. The main purpose of this paper is to detect buildings from aerial images and evaluate performance of Mask R-CNN (Region-based Convolutional Neural Network) developed by FAIR (Facebook AI Research) team recently. Mask R-CNN is a R-CNN that is evaluated to be one of the best ANN models in terms of performance for semantic segmentation with pixel-level accuracy. The performance of the DL models is determined by training ability as well as architecture of the ANN. In this paper, we characteristics of the Mask R-CNN with various types of the images and evaluate possibility of the generalization which is the ultimate goal of the DL. As for future study, it is expected that reliability and generalization of DL will be improved by using a variety of spatial information data for training of the DL models.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.317-317
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• 2021

효율적인 물관리를 위한 댐 유입량 대한 연구는 필수적이다. 본 연구에서는 다양한 머신러닝 알고리즘을 통해 40년동안의 기상 및 댐 유입량 데이터를 이용하여 소양강댐 유입량을 예측하였으며, 그 중 고유량과 저유량예측에 적합한 알고리즘을 각각 선정하여 머신러닝 알고리즘을 결합한 CombML을 개발하였다. 의사 결정 트리 (DT), 멀티 레이어 퍼셉트론 (MLP), 랜덤 포레스트(RF), 그래디언트 부스팅 (GB), RNN-LSTM 및 CNN-LSTM 알고리즘이 사용되었으며, 그 중 가장 정확도가 높은 모형과 고유량이 아닌 경우에서 특별히 예측 정확도가 높은 모형을 결합하여 결합 머신러닝 알고리즘 (CombML)을 개발 및 평가하였다. 사용된 알고리즘 중 MLP가 NSE 0.812, RMSE 77.218 m3/s, MAE 29.034 m3/s, R 0.924, R2 0.817로 댐 유입량 예측에서 최상의 결과를 보여주었으며, 댐 유입량이 100 m3/s 이하인 경우 앙상블 모델 (RF, GB) 이 댐 유입 예측에서 MLP보다 더 나은 성능을 보였다. 따라서, 유입량이 100 m3/s 이상 시의 평균 일일 강수량인 16 mm를 기준으로 강수가 16mm 이하인 경우 앙상블 방법 (RF 및 GB)을 사용하고 강수가 16 mm 이상인 경우 MLP를 사용하여 댐 유입을 예측하기 위해 두 가지 복합 머신러닝(CombML) 모델 (RF_MLP 및 GB_MLP)을 개발하였다. 그 결과 RF_MLP에서 NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, R2 0.859, GB_MLP의 경우 NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, R2 0.831로 CombML이 댐 유입을 가장 정확하게 예측하는 것으로 평가되었다. 본 연구를 통해 하천 유황을 고려한 여러 머신러닝 알고리즘의 결합을 통한 유입량 예측 결과, 알고리즘 결합 시 예측 모형의 정확도가 개선되는 것이 확인되었으며, 이는 추후 효율적인 물관리에 이용될 수 있을 것으로 판단된다.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.656-666
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• 2022

The purpose of this study is to evaluate the effect on the grit, learning reality, and learning satisfaction for nursing students using the flipped learning teaching method. We developed a flipped learning educational program using ADDIE model for nursing students and evaluated the program effect. As a result of this study, the grit (t=-3.07, p=.003), the learning presence (t=-4.87, p<.001) and the learning satisfaction (t=-5.18, p<.001) significantly increased after flip learning method application. The Grit shown to have a significant positive correlation with learning presence (r = .47, p<.001), and learning satisfaction (r = .26, p<.005). The learning presence shown to have a significant positive correlation with learning satisfaction (r = .548, p<.001). The flipped learning teaching method may improve the grit, learning reality, and learning satisfaction. Various efforts will be needed to lay the foundation for flipped learning teaching methods in the field of nursing education in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.81-90
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• 2016

The objective of this study was to investigate the effect of an education method applying the flipped learning technique for college students. Both self-directed learning readiness and educational performance before and after applying the flipped learning were examined. After applying the flipped learning technique, teacher-student interaction, learning satisfaction, and learning motivation were identified. The correlation of each variable was examined after applying the flipped learning technique to investigate its influence on learning motivation. A total of 68 second-year nursing students enrolled in E University were analyzed. A difference between before and after applying the flipped learning was analyzed by the paired t-test; a correlation between the variables was analyzed via Pearson's correlation coefficient; and an influence on the dependent variable learning motivation was analyzed using the stepwise multiple regression analysis. The results showed that self-directed learning readiness increased before and after applying the flipped learning technique with statistical significance, and the difference of educational performance was not significant. After an education session applying the flipped learning technique, a learning motivation demonstrated a significantly positive correlation with self-directed learning readiness (r=0.33, p=.006), college student educational performance (r=0.51, p<.001), teacher-student interaction (r=0.72, p<.001), and learning satisfaction (r=0.79, p<.001). A significantly positive correlation was also observed between the other variables. Factors influencing learning motivation were learning satisfaction and teacher-student interaction. The explanatory power for learning motivation in the regression model considering these two variables was 71.3% (F=80.66, p<.001). Therefore, to enhance learning motivation in applying the flipped learning technique, it is necessary to increase learning satisfaction and to establish a strategy that further vitalizes the teacher-student interaction.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.541-549
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• 2019

The purpose of this study was to test the effects of flip learning-based simulation practice education on the learning flow, learning confidence, communication skills, and clinical competence ability of nursing student. This study used a one group, pre-post test design. We collected the data from 65 4th grade nursing students. Flip learning-based simulation practice education was conducted from March 5th to April 17th, 2019. The collected data were analyzed using SPSS WIN 21.0 program. The result of study were follows. After the flip learning-based simulation practice education, there were significant increased in learning flow(t=-7.548, p<.001), learning confidence(t=-9.163, p<.001), communication skills(t=-6.506, p<.001) and clinical competence(t=-6.733, p<.001). After the flip learning-based simulation practice, clinical performance was found to be positively correlated with learning flow(r=.627, p<.001), learning confidence(r=.513, p<.001) and communication skills(r=.328, p<.008). learning flow and learning confidence(r=.528, p<.001), communication skills and learning flow(r=.332, p<.007) also showed a positive correlation. Therefore, flip learning-based simulation practice education for nursing student could be effective nursing education method.