• Research in Mathematical Education
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• v.18 no.4
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• pp.273-288
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• 2014

In recent years, coding education has been globally emphasized and the Free Semester System will be executed to the public schools in Korea from 2016. With the introduction of the Free Semester System and the rising demand of Computational Thinking (CT) capacity, this research aims to design 'learning environment' in which learners can design and construct mathematical objects through computers and print them out through 3D printers. Furthermore, it will design learning mathematics by constructing the figurate number patterns from 'soma cubes' in the playing context and connecting those to algebraic and combinatorial patterns, which will allow students to experience mathematical connectivity. It is expected that the activities of designing figurate number patterns suggested in this research will not only strengthen CT capacity in relation to mathematical thinking but also serve as a meaningful program for the Free Semester System in terms of career experience as 3D printers can be widely used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1080-1099
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• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.1
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• pp.83-90
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• 2002

This paper proposed the strategy learning method by means of the fusion of Back-Propagation neural network and Q learning algorithm for two-person, deterministic janggi board game. The learning process is accomplished simply through the playing each other. The system consists of two parts of move generator and search kernel. The one consists of move generator generating the moves on the board, the other consists of back-propagation and Q learning plus $\alpha$$\beta$ search algorithm in an attempt to learn the evaluation function. while temporal difference learns the discrepancy between the adjacent rewards, Q learning acquires the optimal policies even when there is no prior knowledge of effects of its moves on the environment through the learning of the evaluation function for the augmented rewards. Depended on the evaluation function through lots of games through the learning procedure it proved that the percentage won is linearly proportional to the portion of learning in general.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.522-533
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• 2014

The capability of the digital communication would need to be strengthened for leveraging collaborative knowledge building and problem solving skills of the middle aged people. It was developed and implemented a smart learning model by utilizing the formative intervention based on the logic of change laboratory to target learners of 'K organization', As a results, smart learning model was composited several activities and supporting systems such as learning instructions of Smart Pad, communication games and SNS, using self-diagnosis and making posters and role-playing video by the internet applications. This research is significant that it finds efficient method to fit design of smart learning and the needs of target learners by using them as testbed which is mixed with different background and digital communication experiences.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.123-133
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• 2019

The purpose of this study is to establish Smart Education Environment for children's personalized learning based on the data are accumulated by Smart Device which is one of element on Smart Education. In this study, we propose the operational improvement plan for the free choice activity in the 5-year-old kindergarten and also implement the Free Choice Activity(FCA) management system for children to select and to evaluate the play plans for themselves. Children participating in this study have fun the whole time for the process of self-planning, the playing activities and the self-assessment of playing. As a result, it is confirmed that children participate actively in decision-making of interesting areas through the smart device than the traditional education environment before. A single teacher using FCA management system with smart device in this study can get useful information without difficulty of individual child's interests, learning and the statistics of children in the classroom.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.335-344
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• 2011

The purpose of this paper is to develop RPG based learning content for English low-achievers at elementary school and to verify the educational effectiveness of this content. Design strategies were elicited by reviewing curriculum and literature. Educational RPG was developed based on ADDIE model. The researchers implemented them in four 5th-grade English low-achievers for two months. Free writing, interviews and observations of the homeroom teacher were conducted to verify the effectiveness of the content. As a result, English low-achieving students became interested in RPG itself, raised their expectations and became more satisfied in learning. When students completed missions while playing games, they gained satisfaction and were challenged by the new missions. This had a positive effect on learning motivation, or "continuing motivation" as mentioned by Keller.

• Korean Journal of Human Ecology
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• v.14 no.6
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• pp.961-972
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• 2005

The purpose of this study is to investigate the improvement of English proficiency of children in the ESL and EFL learning style classrooms through the experiment method. The results of this research are as follows: first, the scores of listening and speaking and the perception of alphabets in the ESL program are higher than that in the EFL program. This means that learning in the ESL style classroom is the better way to improve English skills than in the EFL style classroom, which is common in Korea. Second, there is no difference in the English listening and speaking skills and the perception of the English alphabets between the two gender groups in the ESL & EFL style classrooms. These results suggest that the target language may be used in the English classrooms by the teachers and the students with the materials, books, and equipment are English. Teachers are expected to be in charge of playing decisive roles as demonstrators of speech, models and correctors of pronunciation and providers of materials including TV, VCR, CD players, and cassette recorders, etc.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.63-74
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• 2020

Recently, research on automating game tests using artificial intelligence agents instead of humans is attracting attention. This paper aims to collect play data from human and artificial intelligence and analyze their similarity as a preliminary study for game balancing automation. At this time, constraints were added at the learning stage in order to create artificial intelligence that can play similar to humans. Play datas obtained 14 people and 60 artificial intelligence by playing Flippy bird games 10 times each. The collected datas compared and analyzed for movement trajectory, action position, and dead position using the cosine similarity method. As a result of the analysis, an artificial intelligence agent with a similarity of 0.9 or more with humans was found.