• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.269-278
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• 2021

This study was conducted to develop a learning competency scales customized for learning programs conducted by Center for Teaching & Learning at A University. To achieve this purpose, a preliminary study was set up, which consists of three competency groups (basic competency, intensity competency, application competency) and 13 learning competency factors through a review of previous studies. In order to verify the reliability and validity of the provisional learning competency scales, an online survey was conducted on A university students in September 2020, The collected questionnaire data were organized and exploratory factor analysis and confirmatory factor analysis were conducted. As a result of exploratory factor analysis, 13 learning competency was reduced to 10 as the three competency groups were maintained. As a result of the confirmatory factor analysis, the model was found to be good, Also, as a result of analyzing the reliability of the confirmed learning competency factors, all 10 factors showed a good level of .7 or more. The learning competency scales developed through this study can be used as basic data for performance evaluation and development of new programs of CTL learning program.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of the Korean GEO-environmental Society
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• v.22 no.12
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• pp.5-13
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• 2021

Expandable steel pipe piles have been developed to ensure stability and reduce construction costs during underground floor remodeling and extension work. Expandable steel pipe piles are more economical and stable than micropiles. Extensible steel pipe pile is a method of improving the performance of steel pipes by expanding steel pipes underground. In this paper, the changes in buckling strength according to the shape of steel pipes in an extended steel pipe pile were identified, a numerical analysis model was developed to determine the expended part effect of bumps due to steel pipe expansion, and the optimal steel pipe expansion was calculated through material tests. The larger the expansion diameter of the steel pipe and the greater the number of expanded part, the greater the buckling strength. Numerical results showed that the number of expanded part has a greater effect on buckling strength than the expansion rate. When the expansion rate is more than 1.2 times, it can be seen that as the number of expanded part increases, the effect of increasing buckling strength increases significantly. It was also noted that the expanded part effect of the bumps occur significantly when the extension angle is less than 45° and the expansion rate is 1.3 times higher. When the steel pipe is failure, the expanded rate is 20 to 32%, averaging 25.4%. Through the material test, it was analyzed that it is desirable to limit the maximum expansion rate for performing steel pipes to 16%.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.185-191
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• 2021

The purpose of this study is to develop middle school English classes using Problem-Based Learning(PBL) based on flipped learning and to examine its effects. Recently, various attempts to combine flipped learning and PBL have been made; however, many studies have not been applied to middle and high school curriculums yet. The attempt of this study is expected to have theoretical and practical significance. The instructional model was derived from the review of previous studies, and the development of instructional program followed the general design procedure(analysis-design-development-implement-evaluation), and its validity was secured with the advice of related experts. To verify the effectiveness of the program, the English academic achievement test and the English key competency test were conducted before and after the program. Changes in English academic achievement were analyzed by the paired-sample t-test, and the effect of key competency and the level of achievement test performance (high vs, low) on the pre-post score change was analyzed by the mixed effects repeated measures ANOVA. As a result of the analysis, both academic achievement and key competencies increased, and the low-level students in the pre-academic achievement test showed more improvements. In conclusion, the PBL class based on flipped learning is effective in improving the English academic achievement and key competencies of middle school students, and in particular, it is shown to be an effective teaching method for students with low academic achievement.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.35-43
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• 2021

Recently, there has been a growing interest in zero-shot cross-lingual transfer, which leverages cross-lingual language models (CLLMs) to perform downstream tasks that are not trained in a specific language. In this paper, we point out the limitations of the data-centric aspect of quality estimation (QE), and perform zero-shot cross-lingual transfer even in environments where it is difficult to construct QE data. Few studies have dealt with zero-shots in QE, and after fine-tuning the English-German QE dataset, we perform zero-shot transfer leveraging CLLMs. We conduct comparative analysis between various CLLMs. We also perform zero-shot transfer on language pairs with different sized resources and analyze results based on the linguistic characteristics of each language. Experimental results showed the highest performance in multilingual BART and multillingual BERT, and we induced QE to be performed even when QE learning for a specific language pair was not performed at all.

• Journal of Wetlands Research
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• v.24 no.2
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• pp.115-122
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• 2022

As the impermeable area of soil increases due to urbanization, the water circulation system of the city is deteriorating. The existing guidelines for low impact development (LID) facilities installed to solve these water problems or in previous studies, engineering aspects are more prominent than landscaping aspects. This study attempted to present an engineering and landscaping model for reducing pollutants by identifying the effects of vegetation on rainfall outflows and pollutant reduction in bioretention and the economic aspects of planting. Based on the results of artificial rainfall monitoring at Jeonju Seogok Park and the literature on vegetation rainfall runoff and pollutant reduction performance, the best vegetation for reducing pollution compared to cost was Lythrum salicaria L and Salix gracilistyla Miq. was the best vegetation for carbon storage. If you insist to design plants with only these two plantation, there is no choice but to take risks such as biodiversity. Herbaceous plants such as Lythrum salicaria L can be replaced by death of the plants or pests if considered planting various plants. The initial planting cost could expensive, but it is also necessary to mix and plant Salix gracilistyla Miq, which are woody plants that are advantageous in terms of maintenance, according to the surrounding environment and conditions. Based on the conclusions drawn in this study, it can be a reference material when considering the reduction of pollution by species and carbon storage of vegetation in LID facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.3
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• pp.239-265
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• 2022

The purpose of this study is to examine the status of RDA adoption in Korea and the perception of university librarians on RDA adoption. For this purpose, the adoption status of two libraries that adopted RDA among domestic libraries was analyzed. In addition, interviews were conducted with eight university librarians, and narrative responses related to the adoption of RDA were collected and analyzed. As a result of the interview survey, the research participants recognized that the concept of RDA is very difficult and abstract, there are few implementation cases where RDA is applied, and that it would be very difficult to adopt RDA due to the personnel and budget conditions of the local library. The RDA adoption method recognized by the research participants is to improve RDA awareness, conduct RDA education, prepare guidelines for constructing hybrid bibliographic records when RDA is adopted, operate an RDA pilot institution, and establish a performance model. In addition, research participants need coordination with companies for implementation, prefer to change a specific point in time rather than batch retroactive conversion, discover success stories, establish RDA-related online channels, build and utilize national authority DB, and use the national budget and system support. In this study, based on the research results, a plan to adopt RDA for university libraries in Korea was presented.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.