• Korean Journal of Acupuncture
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• v.39 no.4
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• pp.184-190
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• 2022

Objectives : In our previous study, we developed the prototype of a lesson plan for meridian and acupuncture clinical skills education by applying the rapid prototyping to instructional systems design. The present study aimed to develop a teaching-learning manual, including the lesson plans, practice notes, and instructions for devices. We also aimed to present a guideline on how to use the manual in class. Methods : The manual and materials for teachers and learners were developed based on the solutions and the prototype derived from our previous study. Practical classes on meridian and acupuncture points consist of four major subjects, and the lesson plan and practice note were designed according to each topic. Results : Flipped learning, George's five-step method, peer role-play, and peer-led objective structured clinical examination (OSCE) were applied as main methodologies in the meridian and acupuncture points practical class. The teaching-learning manual, including practice notes, detailed lesson plan, OSCE checklist, and instruction manual for devices, was developed to be utilized at each stage of the learning activity. Conclusions : The application of the teaching-learning manual is expected to provide effective clinical skills education, strengthen learners' communication skills, establish professional identity, assess learners' performance, and provide immediate feedback. The educational effect of the manual for the existing class should be identified, and its feasibility should be verified by implementing it on another group. This manual could be helpful in designing classes for other subjects of Korean medicine, especially for clinical skills education.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.467-473
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• 2021

The purpose of this study is to confirm the effect of health assessment classes applied with flip learning on self-directed learning ability, critical thinking disposition, and academic self-efficacy of nursing students. This is a comparative study before and after a single group, targeting sophomore students taking a health assessment by applying flip learning at a nursing college in K city, Gyeongsangbuk-do. The final analysis consisted of 104 subjects, and the pre-post difference was analyzed by a paired sample test. As a result, self-directed learning ability (t=-3.23, p<.01), critical thinking disposition (t=6.381, p<.001), and academic self-efficacy (t=-4.62, p<.001) were all statistically significantly increased. Based on the results of this study, it was confirmed that the flip-learning method is an effective program to enhance the self-directed learning ability, critical thinking ability, and academic self-efficacy of nursing students. In the long run, the application of the flipped learning learner will play a role in improving the educational environment and strengthening the abilities of students.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.684-705
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• 2022

A new trend has been emerging in recent years, with video game live streaming becoming a meeting ground for gamers, as well as a marketing strategy for game developers. In line with this trend, the emergence of the "Let's Play" culture has significantly changed the manner in which people enjoyed video games. In order to academically explore this new experience, this study seeks to answer the following research questions: (1) Does engaging in video game streaming offer the same feeling as playing the game? (2) If so, what are the factors that affect the feeling of telepresence from viewers' perspective? and (3) How does the feeling of telepresence affect viewers' learning experience of the streamed game? We generated and empirically tested a comprehensive research model based on the telepresence and consumer learning theories. The research findings revealed that the authenticity and pleasantness of the streamer and the interaction of viewers positively affect telepresence, which in turn is positively associated with the gained knowledge and a positive attitude toward the streamed game. Based on the research findings, various practical implications are discussed for game developers as well as platform providers.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2610-2624
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• 2024

Liquid metal heat pipes play a critical role in various high-temperature applications, with their optimization being pivotal to achieving optimal thermal performance. In this study, a deep learning based genetic algorithm is suggested to optimize the operating conditions of liquid metal heat pipes. The optimization performance was investigated in both single and multi-variable optimization schemes, considering the operating conditions of heat load, inclination angle, and filling ratio. The single-variable optimization indicated reasonable performance for various conditions, reinforcing the potential applicability of the optimization method across a broad spectrum of high-temperature industries. The multi-variable optimization revealed an almost congruent performance level to single-variable optimization, suggesting that the robustness of optimization method is not compromised with additional variables. Furthermore, the generalization performance of the optimization method was investigated by conducting an experimental investigation, proving a similar performance. This study underlines the potential of optimizing the operating condition of heat pipes, with significant consequences in sectors such as high temperature field, thereby offering a pathway to more efficient, cost-effective thermal solutions.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.95-105
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• 2013

The music education improves the creative talent, social skills and academic achievement of the students. For the efficient music education, it is requested to develop the collaborative educational learning tools, especially electronic collaborators suitable to the leaner's study patterns and speed. In this paper, we propose a new method to develop a PC-based self learning software for the flute performance using templates and descriptors to make the contents form and substance. Our proposed method can allow user to modify the descriptors to match the contents to his level. We implemented a PC-based self learning software for the flute performance compactly and a feasibility test showed the efficiency of our proposed method to construct a self learning tool to play the flute and the tool can be utilized for the beginner to learn playing flute.

• Smart Media Journal
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• v.9 no.4
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• pp.36-43
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• 2020

The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1415-1419
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• 1990

A novel type of a play-back servo system with high precision is designed using an iterative learning control method by employing the model algorithmic control concept together with an inverse model. A sufficient condition is also provided for the convergency. It is shown by simulation that the proposed control algorithm yields a good performance even in the presence of a periodic load disturbance and proved by experiments using microprocessor-based play-back servo system.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.261-266
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• 2018

This study aimed to provide an overview of artificial intelligence based music composition programs. The artificial intelligence-based composition program has shown remarkable growth as the development of deep neural network theory and the improvement of big data processing technology. Accordingly, artificial intelligence based composition programs for composing classical music and pop music have been proposed variously in academia and industry. But there are several limitations: devaluation in general populations, missing valuable materials, lack of relevant laws, technology-led industries exclusive to the arts, and so on. When effective measures are taken against these limitations, artificial intelligence based technology will play a significant role in fostering national competitiveness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.