• Journal of Industrial Convergence
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• v.18 no.3
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• pp.99-107
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• 2020

This study is a comparative study before and after a single group to identify the effect of convergence class operation on the self-directed learning ability, problem-solving ability and major satisfaction of nursing students in the social-tailored curriculum. The survey was conducted on 79 nursing students from C University. Self-directed learning ability, problem-solving ability and major satisfaction level have been improved since application before applying convergence class. Through this study, we hope to open and operate various curricula in nursing colleges that meet the needs of the community, provide students with various opportunities, cultivate self-directed learning skills, problem-solving skills, and major satisfaction so that learners can solve problems themselves in order to maximize their effects, and develop various contents in the university.

• Journal of Energy Engineering
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• v.27 no.2
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• pp.26-31
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• 2018

This paper introduces a study to improve the thermal efficiency of the district heating user control facility based on reinforcement learning. As an example, it is proposed a general method of constructing a deep Q learning network(DQN) using deep Q learning, which is a reinforcement learning algorithm that does not specify a model. In addition, it is also introduced the big data platform system and the integrated heat management system which are specialized in energy field applied in processing huge amount of data processing from IoT sensor installed in many thermal energy control facilities.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.103-108
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• 2018

This study aims to investigate the Convergence effects of a class using action learning on 4C core competencies of college students. A survey was conducted before and after the action learning class with the same questionnaire for some dental hygiene students in Busan. The collected data were analyzed by using the paired t-test with SPSS 24.0 program. As a result of comparing communication ability, critical thinking propensity, creative problem-solving ability, and cooperative self-efficacy scores, the score of each area after the class was higher compared to before the class, and significant outcomes were shown in the sub-factors as well(p<0.001). In conclusion, it was found that the class using action learning affects the 4C core competencies required. It is urgent to change the teaching method to self-directed learning rather than knowledge-oriented learning.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.415-423
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• 2018

In recent years, Computer-based learning, such as machine learning and deep learning in the computer field, is attracting attention. They start learning from the lowest level and propagate the result to the highest level to calculate the final result. Research literature has shown that systematic learning and growth can yield good results. However, systematic models based on systematic models are hard to find, compared to various and extensive research attempts. To this end, this paper proposes the first TNT(Transitive Nested Triangle)model, which is a growth and fusion model that can be used in various aspects. This model can be said to be a recursive model in which each function formed through geometric forms an organic hierarchical relationship, and the result is used again as they grow and converge to the top. That is, it is an analytical method called 'Horizontal Sibling Merges and Upward Convergence'. This model is applicable to various aspects. In this study, we focus on explaining the TNT model.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.336-339
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• 2022

In this study, we examine the cases of instructors who conducted subject management by understanding the learning patterns of convergence major students taking programming courses Therefore, we intend to find implications for the operation of SW curriculum for convergence majors in the future. In the programming class of the convergence major, students of various grades and majors take the course, and a survey was conducted to understand their learning patterns in a non-face-to-face environment. The instructor studied whether it would be possible to induce learners' participation in class even when face-to-face communication was not possible, and tried to operate the class by understanding the learning propensity of the learners. As there are many students who have maintained successful experiences in self-directed learning amid COVID-19, weekly assignments were set so that they could solve their own problems independently, and almost all students submitted assignments. This study is meaningful in that it studied students' learning patterns, task performance, and programming achievement by operating 'Machine Learning' subject to students of the convergence major in a non-face-to-face situation due to COVID-19.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.54-62
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• 2022

This exploratory study aims to identify effect on core nursing competency of nursing students who experienced convergence practice due to COVID-19. The subjects of this study surveyed 123 senior nursing students in U city with a structured self-report questionnaire analyzed by the SPSS 22.0 software, t-test, One-way ANOVA, correlation, and multiple regression analysis. The average scores for nursing competence scale, self-directed learning ability, learning satisfaction, learning outcomes were 3.99, 3.71, 4.11 and 4.25 out of 5.00. The factors affecting of students' nursing competencies were self-directed learning ability, learning outcomes with 29 percent being explained by these variables. Learning satisfaction did not affect core nursing competency. Through this study we found that high quality educational environment should be prepared to improve these limitations. it is considered that a clear and systematic standard for the educational environment and evaluation of clinical practice is needed.

• 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.

• Convergence Security Journal
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• v.22 no.4
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• pp.129-133
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• 2022

Deep learning frameworks are still evolving, and there are various frameworks. Typical deep learning frameworks include TensorFlow, PyTorch, and Keras. The Deepram framework utilizes optimization models in image classification through image learning. In this paper, we use the TensorFlow and PyTorch frameworks, which are most widely used in the deep learning image recognition field, to proceed with image learning, and compare and analyze the results derived in this process to know the optimized framework. was made.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.397-403
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• 2015

The purpose of this study is to identify factors affecting flow state of the cooperative learning on nursing students live in convergence era. Data was collected a total of six weeks to two nursing freshman class 93 people from April to June 2015. Data were analyzed using descriptive statistics, pearson's correlation coefficients and factors affecting the flow state was used for the multiple regression with the SPSS/WIN 18.0 program. Flow state of the cooperative learning was correlated with major satisfaction and learning satisfaction. The results of major satisfaction and learning satisfaction were significant predictors with 65.4% of the variance in flow state of the cooperative learning, learning satisfaction was confirmed by the affecting factor the flow state. This presents a basis for teaching method applied to maximize the flow state.