• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.319-326
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• 2012

In this paper, we studied a design-based learning for Computational Thinking in Computational Literacy. The design-based learning for computational thinking in computational literacy education started from a MIT media laboratory in 2011. We revised the design-based learning and applied it to educational field. We considered educational strategies and derived the implications, after teaching fourth grade gifted students. Moreover we conducted and analyzed a questionnaire survey, observations and interviews. As the result, the design-based learning in computational literacy is effective for creative computational thinking that students create their ideas and make a meaningful artifacts from it. We expect that this study provides the basic data to apply a design-based learning for computational thinking to Computer education.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.313-318
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• 2021

The advent of the Deep Learning has applied to many industrial and general applications having an impact on our lives these days. Certain type of machine learning model is needed to be designed for a specific problem of field. Recently, there are many instances to solve the various COVID-19 related problems using deep learning model. Therefore, in this paper, a deep learning model for predicting number of outpatients of a hospital in advance is suggested. The suggested deep learning model is designed by using the Keras in Jupyter Notebook. The prediction result is being analyzed with the real data in graph, as well as the loss rate with some validation data to verify either for the underfitting or the overfitting.

• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.209-217
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• 2019

Due to the development of artificial intelligence and image recognition technology that play important roles in the field of 4th industry, office automation systems and unmanned automation systems are rapidly spreading in human society. The proposed algorithm first finds the variances of the differences between the tile values constituting the learning characters and the experimental character and then recognizes the experimental character according to the distribution of the three learning characters with the smallest variances. In more detail, for 100 learning data characters and 10 experimental data characters, each character is defined as the number of black pixels belonging to 15 tile areas. For each character constituting the experimental data, the variance of the differences of the tile values of 100 learning data characters is obtained and then arranged in the ascending order. After that, three learning data characters with the minimum variance values are selected, and the final recognition result for the given experimental character is selected according to the distribution of these character types. Moreover, we compare the recognition result with the result made by a neural network of basic structure. It is confirmed that satisfactory recognition results are obtained through the processes that subdivide the learning characters and experiment characters into tile sizes and then select the recognition result using variances.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.359-368
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• 2021

Purpose: The purpose of this study was to investigate the mediating effect of self-leadership on the relationship between media literacy and learning agility in nursing students based on their experiences in online classes during the Coronavirus Disease-19 pandemic. Methods: A descriptive survey was conducted among 165 nursing students from four universities in Busan. Data were collected from June 2 to 13, 2021, and was analyzed using a t-test, one-way ANOVA, Pearson's correlation coefficients, and stepwise multiple regression with SPSS/WIN 26.0. Results: Significant relationships were found between learning agility and media literacy (r=.62, p<.001), between learning agility and self-leadership (r=.58, p<.001), and between media literacy and self-leadership (r=.53, p<.001). Additionally, self-leadership had a partial mediating effect on the relationship between media literacy and learning agility (Z=4.30, p<.001); its explanatory power was 46.0%. Conclusion: These results indicate that interventions to increase the level of media literacy, along with self-leadership, are necessary to improve the level of learning agility of nursing students who will be essential human resources in a rapidly changing healthcare field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4308-4325
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• 2021

The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.193-197
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• 2021

The pandemic situation caused by COVID-19 has a large and small impact on our society socially, economically, psychologically, and other aspects. In order to prevent the spread of COVID-19, various countries, including Korea, have entered into long-term home care and distance learning systems. However, distance learning experiments conducted in many countries have raised whether face-to-face education can be replaced by distance learning. Therefore, in this study, public opinion, social perception, and field trends were analyzed based on media reports on distance learning. For this purpose, 2,600 articles from 11 newspapers and four broadcasters related to distance learning were collected in this study. Based on this data, keyword trend analysis, topic modeling analysis, sentiment analysis were performed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.693-696
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• 2021

There are various algorithms in reinforcement learning, and the algorithm used differs depending on the field. Even in games, specific algorithms are used when developing AI (artificial intelligence) using reinforcement learning. Different algorithms have different learning methods, so artificial intelligence is created differently. Therefore, the developer has to choose the appropriate algorithm to implement the AI for the purpose. To do that, the developer needs to know the algorithm's learning method and which algorithms are effective for which AI. Therefore, this paper compares the learning methods of three algorithms, SAC, PPO, and POCA, which are algorithms used to implement game AI. These algorithms are practical to apply to which types of AI implementations.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• Journal of Science Education
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• v.43 no.2
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• pp.239-257
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• 2019

This study attempts to understand the context of shared learning in the trading zone formed by teachers from different backgrounds and the process in which this shared learning varies in the educational context, focusing on the case of 'Woodwork Science Education Study Group.' To do this, data was collected through in-depth interviews with eight teachers who participated in the 'Woodworking Science Education Research Group' and analyzed their responses based on grounded theory. As a result, the causal conditions of the teachers' research group were 'various contexts of entering the trading zone' and the central phenomenon was 'encounter with learning in the trading zone.' Contextual conditions affecting this phenomenon were 'woodwork as a boundary object and individual transfiguration experience,' and action/interaction strategy was 'various efforts and influences in the field.' The intervention condition was 'practical effort and experience in educational field.' Final result in this model is 'the new practice of learning shared in the trading zone.' In selective coating, it was found that the practice of the teacher's research group appears as four types of' 'Extracurricular creative experience type,' 'career education type,' 'curricula education type,' and 'school management type.' The results of this study suggest that the shared learning and antonymous practice among teachers in the teachers' research group as trading zone do not only meet their learning needs but also lead to various teaching practices in the individual teachers' context of education and improve the diversity and quality of education.