• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Information Systems Review
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• v.19 no.2
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• pp.115-136
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• 2017

Online communities have become substantial aspects of people's daily lives. However, only a few communities succeed and attract the majority of users, whereas the vast majority struggle for survival. When various communities coexist, important factors should be identified and examined to maintain attraction and achieve success. The concept of coexistence as been extensively explored in organizational ecology literature. However, given the similarities and differences between online communities and traditional organizations, the direct application of organizational theories to online contexts should be cautiously explored. In this study, we follow the roadmap proposed by Davis et al. (2007) in conducting agent-based modeling and simulation study to develop a novel theory based on the previous literature. In the case of two coexisting communities, we find that community size and participation costs can significantly affect the development of a community. A large community can attract a high number of active members who frequently log in. By contrast, low participation costs can encourage the reading and posting behaviors of members. We also observe the important influence of the distribution of interests on the topic trends of communities. A community composed of a population that focuses on only one topic can quickly converge on the topic regardless of whether the initial topic is broad or focused. This simulation model provides theoretical implications to literature and practical guidance to operators of online communities.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• Journal of Advanced Technology Convergence
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• v.3 no.1
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• pp.51-58
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• 2024

The reality of this study is that, in accordance with the development trend of human body art, art makeup and semi-permanent makeup are emerging as promising industries in the beauty industry among beauticians, and awareness of professional skill improvement is gradually increasing over time. Accordingly, interest in semi-permanent makeup has increased not only among beauticians who specialize in beauty industry or learn semi-permanent makeup, but also at beauty academies where they learn many beauty techniques, and this trending technology has been promoted at international beauty competitions by holding skill competitions for beauty technicians who specialize in semi-permanent techniques. As a venue for exchanging information about education, it is expected that synergistic effects such as dissemination of the education system can be expected. Korea's rapid industrial development has brought about great changes in the supply and demand of professional and detailed skilled manpower and in the formation of manpower in terms of technical level according to industrial development, and the skills and professional skills of beauty beauticians have improved due to the excellence of the professional education qualifications of beauticians and high skill evaluations. This had a significant impact on self-development and led to a re-recognition of the importance of efforts to achieve skilled skills.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• 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 Platform Technology
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• v.12 no.1
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• pp.55-66
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• 2024

The K-POP market has become influential not only in culture but also in society as a whole, including diplomacy and environmental movements. As a result, various papers have been conducted based on machine learning to identify the success factors of idols by utilizing traditional data such as music and recordings. However, there is a limitation that previous studies have not reflected the influence of new media platforms such as Instagram releases, YouTube shorts, TikTok, Twitter, etc. on the popularity of idols. Therefore, it is difficult to clarify the causal relationship of recent idol success factors because the existing studies do not consider the daily changing media trends. To solve these problems, this paper proposes a data collection system and analysis methodology for idol-related data. By developing a container-based real-time data collection automation system that reflects the specificity of idol data, we secure the stability and scalability of idol data collection and compare and analyze the clusters of successful idols through a K-Means clustering-based outlier detection model. As a result, we were able to identify commonalities among successful idols such as gender, time of success after album release, and association with new media. Through this, it is expected that we can finally plan optimal comeback promotions for each idol, album type, and comeback period to improve the chances of idol success.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.1
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• pp.35-45
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• 2020

This study focused on the park as a space to support the behavior of urban citizens in modern society. Modern city parks are not spaces that play a specific role but are used by many people, so their function and meaning may change depending on the user's behavior. In addition, current online data may determine the selection of parks to visit or the usage of parks. Therefore, this study analyzed the change of behavior in Yeouido Park, Yeouido Hangang Park, and Yangjae Citizen's Forest from 2000 to 2018 by utilizing a time series analysis. The analysis method used Big Data techniques such as text mining and social network analysis. The summary of the study is as follows. The usage behavior of Yeouido Park has changed over time to "Ride" (Dynamic Behavior) for the first period (I), "Take" (Information Communication Service Behavior) for the second period (II), "See" (Communicative Behavior) for the third period (III), and "Eat" (Energy Source Behavior) for the fourth period (IV). In the case of Yangjae Citizens' Forest, the usage behavior has changed over time to "Walk" (Dynamic Behavior) for the first, second, and third periods (I), (II), (III) and "Play" (Dynamic Behavior) for the fourth period (IV). Looking at the factors affecting behavior, Yeouido Park was had various factors related to sports, leisure, culture, art, and spare time compared to Yangjae Citizens' Forest. The differences in Yangjae Citizens' Forest that affected its main usage behavior were various elements of natural resources. Second, the behavior of the target areas was found to be focused on certain main behaviors over time and played a role in selecting or limiting future behaviors. These results indicate that the space and facilities of the target areas had not been utilized evenly, as various behaviors have not occurred, however, a certain main behavior has appeared in the target areas. This study has great significance in that it analyzes the usage of urban parks using Big Data techniques, and determined that urban parks are transformed into play spaces where consumption progressed beyond the role of rest and walking. The behavior occurring in modern urban parks is changing in quantity and content. Therefore, through various types of discussions based on the results of the behavior collected through Big Data, we can better understand how citizens are using city parks. This study found that the behavior associated with static behavior in both parks had a great impact on other behaviors.

• Journal of Technology Innovation
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• v.19 no.3
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• pp.1-29
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• 2011

Korean government built "2040 Science and Technology Future Vision" in order to show positive future scenarios and suggest a long-term guideline for a progress in science and technology. The S&T Future Vision was built based on an analysis of global megatrends and a prospect of domestic social change. After building S&T Future Vision, the "Government R&E Strategy"s was established as a follow-up action plan. The Government R&D Strategy consists of lists of future emerging technologies for future leadership, government R&D investment status and investment portfolio plans. Exploring future emerging technologies aggressively and making a governmental R&D strategic policy are requirements for national competitiveness, leadership in the world. Therefore search and selection for future emerging technologies is getting more and more important recently. Generally qualitative methodologies have been used such as expert-panel discussion method and portfolio analysis with expert valuation method in order to explore future technologies. These experts-based qualitative methodologies are well defined but lacking in some objectivity because size of expert-panels has limitations. We suggest a quantitative methodology, science mapping method to compensate this shortcoming in this study. There is another limitation related governmental R&D strategy which is that general R&D portfolios are static until a point of technology realization. We also propose a dynamic R&D investment portfolio which present different portfolios at a intermediate point and a point of technology realization. We expect this try with science mapping method and a dynamic R&D portfolio could strengthen strategic aspect of government R&D policy.

• Journal of the Korean Institute of Gas
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• v.5 no.4 s.16
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• pp.1-7
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• 2001

This paper presents a 'consequence analysis' for vapor cloud explosions caused by heavy gas leakages from commercially used storage tanks at petrochemical plants. Particularly, this paper emphasizes on evaluating the results of various vapor cloud explosion accidents from Butane storage tanks. Also this paper analyses the impact of variables on the accidents in order to acquire the optimum conditions for variables. $SuperChems^{TM}$ Professional Edition was applied to analyse the impact (If atmospheric and other variables in the situation where vapor cloud continuously disperses from the ground level. Under the assumption that practical operating conditions are selected as a standard condition, and Butane leaks from the storage tank for 15 minutes, the results show that the maximum distance of LFL (Lower Flammable Limit) was 52 meters and overpressure by the vapor cloud explosion was 1 psi at 128.2 meters. It is observed that the impact of the variables on accidental Butane storage tank leakage mainly varied upon atmospheric stability, wind velocity, pipe line size, visible length, etc., and changes in the simulation result occurred as the variables varied. The maximum distance of the LFL (Lower Flammable Limit) increased as the visible length became shorter, the size of the leak became larger, the wind velocity was decreased, and the climatic conditions became more stable. Thus, by analysing the variables that influence the simulation results of explosions of Butane storage tanks containing heavy gases, I am presenting the most appropriate method for 'consequence analysis' and the selection of standards for suitable values of variables, to obtain the most optimal conditions for the best results.