• Journal of the Korean earth science society
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• v.44 no.2
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• pp.169-183
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• 2023

This study analyzed students' polar literacy in an effort to promote polar education based on its high educational value. The polar literacy test items developed for this study consisted of questions about knowledge, skills, attitudes, and beliefs about the polar region, as well as background variables of students. The final test items, which were revised and supplemented several times through the preliminary test, were applied to 323 eighth graders in South Korea. We analyzed the response characteristics of the polar literacy questions for all students. Students were grouped into those with a global citizenship perspective and those with a pragmatic perspective, according to the viewpoint of polar issues and their polar literacy. Analysis showed that the students had a high understanding of climate change and living things in the polar regions, but had a very low understanding of ice, which is a key component of the polar regions. Moreover, they were unable to approach the Earth system thinking when dealing with polar issues. In addition, the global citizenship group had a higher intellectual understanding and deeper sympathy of the polar problem than the pragmatic group. This study is meaningful in that the survey results present a specific direction for future polar education.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.

• The Korean Journal of Coaching Psychology
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• v.7 no.2
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• pp.21-46
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• 2023

The current study aimed to develop and evaluate the effectiveness of a group coaching program to improve college students' career exploration behaviors based on the self-affirmation theory. Participants were undergraduate students at a University in Seoul and volunteered in the program during the break. The group coaching program was conducted twice a week during three weeks, a total of six times, and each session lasted 2 to 2.5 hours. In this study, the effect of self-affirmation embedded in the group coaching program on various psychological variables and college students' career exploration behaviors were examined. A mixed-randomized analysis was conducted to compare the pre, post, and follow-up tests of the experimental, comparison, and no-treatment control groups, each consisting of 18 participants. The results showed that participants in the experimental group coaching condition showed more significant improvements in psychological resources, career self-efficacy, openness, problem-focused coping, and career exploration behaviors compared to the comparison and control groups, and these results were maintained even after the program ended. The implications of this study for coaching theory and practice, as well as limitations and future research directions, were discussed.

• Journal of the Korean Society of Safety
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• v.38 no.3
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• pp.77-84
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• 2023

Welding and cutting, which are representative tasks in handling firearms at industrial sites, are the basis for production and maintenance processes across all industries. They are also essential in the root industry. Specifically, they are widely used in the manufacturing industry, including equipment industries such as shipbuilding, automobiles, and chemicals, and subsequent maintenance work and general facility repair. However, such hot work carries a high fire risk owing to sparks scattering and inadequate management, resulting in a high occurrence of accidents. In response, the government and relevant organizations have recently revised the Occupational Safety and Health Act to prevent accidents during hot work. These revisions impose more stringent regulations than before, which are expected to help prevent actual fire accidents. However, whether the fire observer system, which is the core element of the revision, would be practically applied and maintained is unclear. Therefore, this study compared the fire observer system in the revised Occupational Safety and Health Act with those in the laws and systems of developed countries, conducted interviews with safety and health experts to assess the suitability of the new system for fire observer operations, and improvement plans were derived accordingly. Therefore, the laws and systems of developed countries grant more authority to fire observers compared with those of Korea. Moreover, professional training in handling emergency is required. Interviews with safety and health experts revealed that regardless of company size, the same operating standards were applied, and standards for deploying fire observers in various locations were unclear. Furthermore, there was a lack of professional education and training, and the role and authority of fire observers were limited. These findings revealed a problem in this sector. The results of this study are expected to serve as basic data for establishing a practical system for placing fire observers and supplementing laws, guidelines, and systems for preventing fire accidents.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of Korean Home Economics Education Association
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• v.34 no.4
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• pp.77-92
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• 2022

Recently, digital transformation in the financial industry has been accelerated, and it has become an important task to improve the level of utilization of Internet banking by elderly consumers, who are vulnerable to Internet use. Accordingly, this study analyzed 3,101 respondents in their 60s or older from the 11th year of the Media Panel Survey to identify demographic, experiential, and psychological factors that affect the self-efficacy of elderly consumers' usage of Internet banking. The main research findings are as follows. First, gender, education, occupation, and income were identified as demographic variables. Second, the Internet shopping experience was identified as an experiential factor. Also, concerns about information security, digital literacy, and high will for problem-solving were identified as psychological factors. Third, as a result of the moderating effect analysis on whether the experiential and psychological factors have different influences according to the group divided into the 60s and 70s, the effect on self-efficacy in the usage of the Internet was classified by age. The results of this study will be able to enrich the discussions related to the intention to utilize technology among elderly consumers by empirically revealing that there are characteristics that cause differences in financial behavior even within one group called the elderly.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.195-206
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• 2023

Most of the Micro-mobility parking in Korea use Dockless system. However, Dockless can result in cluttering, infrastructure deficiencies, and safety challenges as has been observed in cities. It is necessary to introduce a Station Parking system in order to solve the drawbacks of the dockless, but the introduction without engineering has low accessibility and induces side effects. In this study, to decide optimal location about number of the Micro-mobility Station, we has been applied the MCLP model about the coverage range, usage demand, usage time in order to classify the type of Micro-mobility Station. For the MCLP, User Date input to reflect realistic demand in Bundang new town, Korea. The result show that the optimal number of facilities in 400 m was 146, and the coverage ratio was 99.83 %, which was most suitable coverage for solving the parking problem. We also classified the demand into 4 levels and the usage time into 3 levels, and by crossing them, we were able to classify the Parking lot types into 12 types. It is possible to propose strategic policies in the installation and operation of Micro-mobility Parking System.