• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.197-211
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• 2021

The emergence of knowledge-sharing live streamers provides more diversified content to the live streaming platform. Analysis of the factors affecting the intention to use knowledge sharing live streaming users can allow the live streaming platform to understand better the adoption characteristics of users who follow this type of content. Help platform operators provide better services and help live streaming platforms innovate. Based on the TAM model, this research uses questionnaire surveys and structural equation models to construct a conceptual model of the influencing factors of users' intentions in the knowledge sharing live streaming and conduct an empirical analysis on the influencing factor models. The results of data analysis show that a significant influence of users' attitudes of knowledge sharing live streaming is perceived usefulness, followed by flow experience; perceived value has a positive impact on users' attitudes and intention to use, and the positive influence of users attitude significantly affect the user's intention.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.539-550
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• 2020

This study establishes the scientific basis for the use of digital learning materials through the education-neurological research method and derives implications for education based on education-neurological understandings. The main findings of the education-neurological analysis of digital learning materials are as follows: First, various sensory stimuli go through multiple sensory neurons and deep sections of the upper sphere and make possible the cooperative processing of information. Second, indirect experience from digital learning materials helps students understand the learning contents vividly through the mirror neuron system. Third, positive emotions originating from digital learning materials promote functions of dopamine, the reticular activating system, frontal-striatal circuit, cerebrum cortex. Based on the findings, the study suggests the following educational implications. First of all, when selecting digital learning materials, teachers should consider expression forms, learning contents, the flow of classes, and the adverse effects of digital learning materials. Next, it is effective to utilize digital learning materials in the lecture for provoking curiosity and enjoyment, maintaining interest and effort, and reviewing what students learned.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.40-50
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• 2021

Science museum are dealing with exhibits on field of changing science and technology, and previous research suggested that exhibits replacement should carried out at least every 5 years. In order to efficiently replace exhibits within a limited budget, various studies analyzed visitors' preferences to exhibits. Recently, studies use various technologies to collect the data on visitors' preferences automatically, but almost of studies had a high dependency on their visitors such as visitors needed to carry specific sub-devices in the museums for gathering data. As complementing the limitations of previous research, this study introduces the improved method which is able to automatically collect and quantify visitors' preferences to exhibits using TensorFlow, a deep learning technology. By the proposed analysis method, it was possible to collect 2,520 data of visitors' experience on exhibits in totality. Based on collected data, attraction power and holding power indicating the preference of visitors on exhibits were able to be calculated. The result also confirmed antecedent research conclusion that the attraction power and holding power of the exhibit which consists of 3 dimensional structures work are higher than other exhibits. As a conclusion, the proposed method will provide more convenient data collection method for detecting visitors' preference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1832-1837
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• 2022

In the SW education of non-majors who encounter liberal arts education experience difficulties in the SW development environment and understanding they encounter for the first time, relevance to their major, and convergence thinking ability. In order to compensate for the difficulties of non-major learners in liberal arts education, a relatively easily accessible software was used to utilize a demonstration-oriented model that can be applied to beginners in SW education. In order to understand the logical flow of applications and problem solving used in real life, we proposed a convergence SW teaching method that combines repeated implementation through demonstration by the instructor and imitation of the learner, and learning indicators to increase the learning satisfaction and achievement of the learner. In the experiment applying the teaching and learning method proposed in this paper, meaningful results were shown when evaluating the learning effect, academic achievement, learning satisfaction, and teaching and learning method aspects of SW education.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.258-267
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• 2016

Three-dimensional integrated circuits (3D ICs) experience die-to-die variations in addition to the already challenging within-die variations. This adds an additional design complexity and makes variation estimation and full-chip optimization even more challenging. In this paper, we show that the industry standard on-chip variation (AOCV) tables cannot be applied directly to 3D paths that are spanning multiple dies. We develop a new machine learning-based model and methodology for an accurate variation estimation of logic paths in 3D designs. Our model makes use of key parameters extracted from existing GDSII 3D IC design and sign-off simulation database. Thus, it requires no runtime overhead when compared to AOCV analysis while achieving an average accuracy of 90% in variation evaluation. By using our model in a full-chip variation-aware 3D IC physical design flow, we obtain up to 16% improvement in critical path delay under variations, which is verified with detailed Monte Carlo simulations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.97-115
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• 2023

In South Korea, chronic kidney disease(CKD) impacts around 4.6 million adults, leading to a high reliance on hemodialysis. For effective dialysis, vascular access is crucial, with decisions about vascular surgeries often made during dialysis sessions. Anticipating these needs could improve dialysis quality and patient comfort. This study investigates the use of Artificial Intelligence(AI) to predict the timing of surgeries for dialysis vessels, an area not extensively researched. We've developed an AI algorithm using predictive maintenance methods, transitioning from machine learning to a more advanced deep learning approach with Long Short-Term Memory(LSTM) models. The algorithm processes variables such as venous pressure, blood flow, and patient age, demonstrating high effectiveness with metrics exceeding 0.91. By shortening the data collection intervals, a more refined model can be obtained. Implementing this AI in clinical practice could notably enhance patient experience and the quality of medical services in dialysis, marking a significant advancement in the treatment of CKD.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.179-191
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• 2011

This study aims to elucidate the relationship between the characteristics of adult learners and flow in cyber-class along with relationships among flow, stress, and problem-solving ability. The research subjects were 1044 enrolled students at Cyber University located in Seoul through voluntary on-line questionnaire. The analysis is following: The components of flow on cyber-class including enjoyment, engagement, focused attention, and time-distortion show the significant difference upon the characteristics of adult learners such as school grade, age, marital status, and number of registered classes. In addition, the flow on cyber-class has the negative relationship with stress and the positive relationship with problem-solving ability. To improve the level of flow on cyber-class, it is important to develop the new on-line class and class materials with the consideration of characteristics and diverse backgrounds of adult learners. The incorporation of various interactive evaluation can also improve the flow level of adult learners in cyber class. Finally, the learning counselling service might be essential for adult learners to experience flow on cyber-class.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.69-78
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• 2016

The purpose of this study is to analyze convergent effects of team interaction and team metacognition of participants on a continuous participation in the university learning community. We developed 19 items of team interaction and 17 items of team metacognition through literature review. The subjects were 113 students who participated in learning community in A university. The results are as follows. First, team interaction level and team metacognition level can affect a continuous participation in learning community. The higher team interaction is and the lower team metacognition is, the higher continuous participation is. Second, among team interaction factors that affect a continuous participation in learning community, the more number of learning is and the more encouragement of one another is, the higher continuous participation is. But the less participation of members is, the less flow to learning is, and the less learning time is, the lower a continuous participation is. Third, among team metacognition factors that affect a continuous participation in learning community, the more number of learning is, the higher continuous participation is. But the more use of various learning tools is and the more learning time is, the lower continuous participation is. Based on these results, the convergent ways of support for continuous participation in the university learning community are as follows. First, supporting system is needed to induce students to experience the positive atmosphere of learning community by increasing number of learning to facilitate team interaction and urging them to encourage one another. Second, providing the effective utilization method is necessary for students to fully acknowledge the necessity and value of team metacognition activity.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.421-440
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• 2010

Our country has excessive amount of learning per hour compared with Japan and Singapore. And as there is no consistence for definition of time between grades, it deteriorates understanding of students. Our country teaches students focusing on time algorism whereas Japan and Singapore teaches their students focusing on flow of time. In composing of mathematics textbook in Korea, Japan and Singapore, textbook of our country contains far more of learning compared with the amount designated in textbooks. Textbooks of Japan contains less teaching elements, but instead it contains much activities to expedite time sense As time is distributed in activities of students, it is more important to construct textbooks with experience of students rather than algorism approaches. In addition, textbooks of Singapore contains various examples and clarified concepts compared with those of our country. Like above, time teaching deployment methods of Japan and Singapore gives us good lessons for teaching time in our country, and it is expected be good reference for future development of our textbooks.