• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.241-251
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• 2019

In the present study, we investigated the effect of microcurrent against cognitive impairment in Alzheimer's disease (AD) mice model. The cognitive impairment was induced by intracerebroventricularly injection of amyloid beta ($A{\beta}$) to ICR mouse brain, and four kinds of micorocurrent wave were applied to AD mice. We observed the improved cognitive ability in microcurrent-applied AD mice through novel object recognition test and Morris water maze test, compared to $A{\beta}$-injected control group. The contents of malondialdehyde generated by $A{\beta}$ in the brain were also reduced by microcurrent application. These effects of microcurrent were related to the modulation of $A{\beta}$ producing and brain-derived neurotrophic factor (BDNF). Microcurrent down-regulated ${\beta}$-secretase, presenilin 1, and presenilin 2 which were related amyloidogenic pathway, and up-regulated human brain-derived neurotrophic factor in the mice brain, especially Wave4 group [STEP FORM wave form (0, 1.5, 3, 5V), wave superposition]. These results suggest that microcurrent application could provide help for improvement learning and memory ability, at least partly.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.353-364
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• 2021

Since the mid-twentieth century, geology has gradually evolved as an interdisciplinary context in South Korea. The journal of Economic and Environmental Geology (EEG) has a long history of over 52 years and published interdisciplinary articles based on geology. In this study, we performed a literature review using topic modeling based on Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to identify geological topics, historical trends (classic topics and emerging topics), and association by analyzing titles, keywords, and abstracts of 2,571 publications in EEG during 1968-2020. The results showed that 8 topics ('petrology and geochemistry', 'hydrology and hydrogeology', 'economic geology', 'volcanology', 'soil contaminant and remediation', 'general and structural geology', 'geophysics and geophysical exploration', and 'clay mineral') were identified in the EEG. Before 1994, classic topics ('economic geology', 'volcanology', and 'general and structure geology') were dominant research trends. After 1994, emerging topics ('hydrology and hydrogeology', 'soil contaminant and remediation', 'clay mineral') have arisen, and its portion has gradually increased. The result of association analysis showed that EEG tends to be more comprehensive based on 'economic geology'. Our results provide understanding of how geological research topics branch out and merge with other fields using a useful literature review tool for geological research in South Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.

• Knowledge Management Research
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• v.23 no.1
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• pp.89-109
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• 2022

Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.59-68
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• 2009

This paper aims to explore the factors of learner's flow and to develop and validate a scale to measure the flow in Massive Multiplayer Online Role Playing Game(MMORPG) for education. First of all, potential factors were drawn through literature review. The potential stage comprises 6 factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment) and 16 subfactors. With total 48 items developed. a survey was carried out among 293 elementary learners who had been participating in a commercial MMORPG for English skills to measure their flow in the MMORPG by utilizing the potential scale. Using the responses collected from 288 respondents, exploratory factor analysis, reliability analysis, and confirmatory factor analysis were performed. The expository factor analysis showed that items within each sub-factors could be bound into one factor. That is, the variables evaluating learner's flow were divided into six factors(learner's psychological characteristics, learner's skill, importance of game, environment for learner, instructional design, and instructional environment). And these factors were interpreted consisting of 16 sub-ones. Reliability estimates indicated that the evaluation tool had good internal consistency. The confirmatory factor analysis did confirm the model suggested by the expository factor analysis. Over fit measures(CFI, NFI, NNFI) showed the good suitability of the model. Findings of this study confirmed the validity and reliability of the scale to measure learner's flow in MMORPG.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.119-129
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• 2024

The cutaneous lymphatic system in humans plays a crucial role in draining interstitial fluid and activating the immune system. Environmental factors, such as ultraviolet light and natural aging, often affect structural changes of such lymphatic vessels, causing skin dysfunction. However, some limitations still exist because of no alternatives to animal testing. To better understand the skin lymphatic system, a biomimetic microfluidic platform, skin-lymph-on-a-chip, was fabricated to develop a novel in vitro skin lymphatic model of humans and to investigate the molecular and physiological changes involved in lymphangiogenesis, the formation of lymphatic vessels. Briefly, the platform involved co-culturing differentiated primary normal human epidermal keratinocytes (NHEKs) and dermal lymphatic endothelial cells (HDLECs) in vitro. Based on our system, Lymphanax^TM, which is a condensed Panax ginseng root extract obtained through thermal conversion for 21 days, was applied to evaluate the lymphangiogenic effect, and the changes in molecular factors were analyzed using a deep-learning-based algorithm. Lymphanax^TM promoted healthy lymphangiogenesis in skin-lymphon-a-chip and indirectly affected HDELCs as its components rarely penetrated differentiated NHEKs in the chip. Overall, this study provides a new perspective on Lymphanax^TM and its effects using an innovative in vitro system.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.141-153
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• 2024

The 2022 revised science curriculum aims to develop the ability to solve scientific problems arising in daily life and society based on convergent thinking stimulated through participation in research activities using artificial intelligence (AI). Therefore, we developed a science-AI convergence education program that combines the science curriculum with artificial intelligence and employed it in convergence classes for high school students. The aim of the science-AI convergence class was for students to qualitatively understand the movement of a damped pendulum and build an AI model to predict the position of the pendulum using the block coding platform KNIME. Individual in-depth interviews were conducted to understand and interpret the learners' experiences. Based on Giorgi's phenomenological research methodology, we described the learners' learning processes and changes, challenges and limitations of the class. The students collected data and built the AI model. They expected to be able to predict the surrounding phenomena based on their experimental results and perceived the convergence class positively. On the other hand, they still perceived an with the unfamiliarity of platform, difficulty in understanding the principle of AI, and limitations of the teaching method that they had to follow, as well as limitations of the course content. Based on this, we discussed the strengths and limitations of the science-AI convergence class and made suggestions for science-AI convergence education. This study is expected to provide implications for developing science-AI convergence curricula and implementing them in the field.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.117-133
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• 2023

This study investigates the perceptions, Stages of Concern (SoC), and Levels of Use (LoU) regarding process-based assessment among Home Economics (HE) teachers to determine the necessary support for its implementation in schools. Data were gathered from a survey administered to HE teachers. The results are as follows. First, HE teachers viewed process-based assessment favorably, valuing its multifaceted evaluation approach over result-based assessment. The feedback from process-based assessments was seen as an opportunity for reflection for both educators and students. While some teachers expressed uncertainty about the optimal timing of implementation, they generally demonstrated a sound understanding of the feedback concept within the assessment process. HE teachers were predominantly concerned with their own professional expertise and the learning outcomes of their students. The majority of HE teachers have utilized process-based assessments for at least one semester. None deemed it irrelevant to their practice or showed disinterest in its adoption. Those who had yet to implement it were either in the first(introduction) or the second(preparation) stages.