• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.595-617
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• 2010

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner's cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.107-119
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• 2016

This research was explored the effects of the application of the team-based classes on the self-directed learning, the academic self-efficacy, and the level of learning satisfaction of the nursing students. Study subjects are 104 nursing students from one university from G region. The data were analyzed by t-test, one-way ANOVA, paired t-test, Pearson's correlation coefficients. With regard to the differences between the levels of the learning satisfaction, the differences were shown (t=4.410, p<.05). Regarding the self-directed learning, it rose to the average of 3.50 points after the application of the team-based learning from the average of 3.39 points. It showed a difference (t=-2.083, p=<.05). And the academic self-efficacy rose to 3.65 points from the 3.12 points after the application of the self-based learning. And it showed a difference (t=-14.175, p=<.001). The level of the learning satisfaction was shown to be over the middle with the average of 3.73 points. And the self-directed learning after the application of the team-based learning showed an academic self-efficacy (r=.512, p<.001), a level of the learning satisfaction (r=.421, p<.001), and a positive relationship. It was proven that the team-based learning can cultivate the capabilities for effectively solving the clinical problems.

• Journal of Environmental Health Sciences
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• v.28 no.2
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• pp.23-39
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• 2002

This study focuses on the structure of geographical conditions, the riverbed, the meterological and atmospheric examination, the ecological environment, the food chain and the ecosystem, in order to establish a basic plan for preparing a natural learning area of environmental ecosystem in Bukchun and its surroundings, Gyeongju. The results could be summarized as follows. Bukchun is a first grade which extends 6km along the road from Bomun bridge to the junction of Hyungsangang. The basin area is 7.10$\textrm{km}^2$ and the slope is 1/200~1/300. Gyeongju has good atmospheric conditions, i.e. SO$_2$0.011 ~0.017ppm, CO 0.8~ 1.5ppm, NO$_2$0.013~0.019ppm, $O_3$0.013~0.020ppm, TSP 85~142$\mu\textrm{g}$/㎥, PM-10 47~90$\mu\textrm{g}$/㎥ and Pb 0.057 ~0.129$\mu\textrm{g}$/㎥, which is below the annual and daily averages, and is little lower than those of Pohang and Ulsan. The ecosystem of Bukchun is based on the structure of the food chain, which includes birds such as the grey and white herons at the top of the food chain. This study also considers the development of the river's in terms of culture, environment and ecology concept.

• Journal for History of Mathematics
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• v.19 no.4
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• pp.117-132
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• 2006

For a meaningful learning of the Construction Unit in 7-B Grade, this study aims to develop teaming materials on the basis of Clairaut's $El{\`{e}}ments$ de $G{\`{e}}om{\`{e}}trie$, which is grounded on a natural generation derived from the history of mathematics and emphasizes students' inquiry activity and reflective thinking activity, and to analyze the characteristics of learning process shown in classes which use the application of teaming materials. Six students were sampled by gender and performance and an interpretive case study was conducted. Construction was specified so as to be consciously executed with emphasis on an analysis to enable one to discover construction techniques for oneself from a standpoint of problem solving, a justification to reveal the validity of construction, and a step of reflection to generalize the results of construction.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.43.1-43.1
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• 2019

In this study, we apply a deep learning model to denoising solar magnetograms. For this, we design a model based on conditional generative adversarial network, which is one of the deep learning algorithms, for the image-to-image translation from a single magnetogram to a denoised magnetogram. For the single magnetogram, we use SDO/HMI line-of-sight magnetograms at the center of solar disk. For the denoised magnetogram, we make 21-frame-stacked magnetograms at the center of solar disk considering solar rotation. We train a model using 7004 paris of the single and denoised magnetograms from 2013 January to 2013 October and test the model using 1432 pairs from 2013 November to 2013 December. Our results from this study are as follows. First, our model successfully denoise SDO/HMI magnetograms and the denoised magnetograms from our model are similar to the stacked magnetograms. Second, the average pixel-to-pixel correlation coefficient value between denoised magnetograms from our model and stacked magnetogrmas is larger than 0.93. Third, the average noise level of denoised magnetograms from our model is greatly reduced from 10.29 G to 3.89 G, and it is consistent with or smaller than that of stacked magnetograms 4.11 G. Our results can be applied to many scientific field in which the integration of many frames are used to improve the signal-to-noise ratio.

• Journal of Engineering Education Research
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• v.24 no.3
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• pp.21-31
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• 2021

Flipped learning, which involves listening to lectures at home and performing dynamic group-based problem-solving activities in the classroom, is recently evaluated as a learner-centered teaching method, and interest and applications in engineering education are increasing. Therefore, this study aims to provide practical guidelines for successful application through empirical research analysis on the use of flipped learning in domestic engineering education. Through the selection criteria and keyword search, a systematic review of 36 articles was conducted. As a result of the analysis, flipped learning research in engineering education has increased sharply since 2016, focusing on academic journals and reporting its application cases and effects. Most of the research supported that flipped learning was effective not only for learners' learning activities(e.g., academic achievement, satisfaction, engagement, learning-flow, interaction), but also for individualized learning and securing sufficient practice time. It was often used in major classes with 15 to less than 50 students, especially in computer-related major courses. Most of them consisted of watching lecture videos, active learning activities, and lectures by instructors, and showed differences in management strategies for each class type. Based on the analysis results, suggestions for effective flipped learning management in future engineering education were presented.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.49.3-49.3
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• 2020

I will give an overview of the recent work in deriving cosmological constraints from deep learning methods applied to the large scale distribution of galaxies. I will specifically highlight the success of convolutional neural networks in linking the morphology of the large scale matter distribution to dark energy parameters and modified gravity scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Electronics and Telecommunications Trends
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• v.34 no.2
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• pp.40-50
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• 2019

Considerable accuracy improvements in deep learning have recently been achieved in many applications that require large amounts of computation and expensive memory. However, recent advanced techniques for compacting and accelerating the deep learning model have been developed for deployment in lightweight devices with constrained resources. Lightweight deep learning techniques can be categorized into two schemes: lightweight deep learning algorithms (model simplification and efficient convolutional filters) in nature and transferring models into compact/small ones (model compression and knowledge distillation). In this report, we briefly summarize various lightweight deep learning techniques and possible research directions.

• Smart Media Journal
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• v.12 no.11
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• pp.113-124
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• 2023

With the advent of 5G and B5G (Beyond 5G) networks, network virtualization technology that can overcome the limitations of existing networks is attracting attention. The purpose of network virtualization is to provide solutions for efficient network resource utilization and various services. Existing heuristic-based VNE (Virtual Network Embedding) techniques have been studied, but the flexibility is limited. Therefore, in this paper, we propose a GNN-based network slicing classification scheme to meet various service requirements and a RL-based VNE scheme for optimal resource allocation. The proposed method performs optimal VNE using an Actor-Critic network. Finally, to evaluate the performance of the proposed technique, we compare it with Node Rank, MCST-VNE, and GCN-VNE techniques. Through performance analysis, it was shown that the GNN and RL-based VNE techniques are better than the existing techniques in terms of acceptance rate and resource efficiency.