• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.1
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• pp.91-104
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• 2008

In tunnels, safety factor concept has been suggested to estimate their stability quantitatively. It is merely limited in the framework of mechanical analysis. However safety factor concept has not been applied in hydro-mechanical coupled analyses due to their modelling complexity. Recently studies on this topic are being actively made. In this study, induced drainage modelling methods for hydro-mechanical coupled analyses are compared and analyzed to estimate safety factor of a subsea tunnel exactly. To this end, methods both controlling hydraulic characteristic of shotcrete and using a drainage well are considered. Sensitivity analysis were carried out on rock class, thickness of shotcrete, and hydraulic properties of rock mass. As the results of this study, it turned out that the induced drainage modelling using a drainage well would give more reliable results than that of controlling hydraulic characteristic of shotcrete in estimating tunnel stability in hydro-mechanical coupled analyses.

• Journal of Research and Publication Ethics
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• v.5 no.2
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• pp.1-6
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• 2024

Purpose: PBM is emerging as a major management system for securing corporate productivity and enhancing competitiveness, and various studies are being conducted. The purpose of this study is to analyze research trends published in KCI-listed journals and papers since 1999 to understand the current status of research and provide basic data for more extensive research and development of performance management in the future. Research design, data and methodology: A detailed examination of research trends was conducted through the analysis of abstracts from 154 research papers on PBM. To facilitate a comprehensive analysis of these trends, LDA topic modelling was employed. Results: First, it should be noted that research on PBM is not limited to the area of HRM. Instead, PBM research is expanding to encompass comprehensive personnel systems. Second, the results of topic modeling analysis show that although the initial focus of research was on human resource management, there is now a growing interest in fairness and organizational culture in the entire organization. Conclusions: PBM is becoming a dominant paradigm as it shifts from HR systems to organizational fairness and culture. This suggests that future research should consider both quantitative and qualitative aspects of PBM to improve corporate performance while prioritizing organizational fairness and culture.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.376-387
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• 2017

This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.40-56
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• 2022

This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package "rvest." The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. "LIS Recruitment," "LIS Issues," "Other Discussion," "LIS Education," "LIS Research," "LIS Exams," "General Information related to Library," "LIS Admission," "Library and Professional Activities," and "Information Communication Technology (ICT)." It was found that the majority of the posts belonged to "LIS Exam," followed by "Other Discussions" and "General Information related to the Library."

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.1321-1321
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• 2017

Recently the attention to the 4th industrial revolution has been increasing. In the 4th industrial revolution era, the boundaries of physical space, digital space, and biological space are becoming blurred since the active convergence between various fields There are a lot of issues on the 4th industrial revolution such as artificial intelligence, internet of thing, big data, and cyber physical system. Accordingly, this study aims to analyse the main issues of the 4th industrial revolution. Data mining such as topic modelling method is used for the analysis. This study is expected to be helpful for the researcher and policy maker of the 4th industrial revolution.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.517-528
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• 2016

This study attempted to explore and examine a new user experience (UX) research method for IoT products which are becoming widely used but lack practical user research. While user experience research has been traditionally opted for survey or observation methods, this paper utilized big data analysis method for user online reviews on an intelligent agent IoT product, Amazon's Echo. The results of topic modelling analysis extracted user experience elements such as features, conversational interaction, and updates. In addition, regression analysis showed that the topic of updates was the most influential determinant of user satisfaction. The main implication of this study is the new introduction of big data analysis method into the user experience research for the intelligent agent IoT products.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.153-161
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• 1999

For composing the structure model of national maritime power system by system structural modelling, in this study, the 50 basic factors are selected by survey of the extensive and thorough literatures on maritime, sea, maritime power and sea power. And the basic factors are classified into 36 component factors by cluster method. The 9 attributes are extracted by the application of the principle component analysis method, one of the factor analysis method in system engineering, to component factors. We defined the attributes composing the national maritime power system by integration the result of this study and existed our studies relate to this topic. Which are showed in table 8. and we showed the structure model of national maritime power system in figure 3. In table 8, the 9 attributes are as follows: the fundamental power of maritime, shipping and port power, naval power, fishing power, shipbuilding power, the power of ocean research and development, dependency on seaborne trade, the protection power of ocean environment and the will and inclination of government.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.731-740
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• 2021

This study applied LDA topic modeling analysis to collect and analyze news media big data related to face masks in the three waves of the COVID-19 pandemic in Korea. The results empirically show that media reports focused on mask production and distribution policies in the first wave and the mandatory mask wearing in the second wave. In contrast, more reports on trivial, gossipy events consist of the media coverage in the second and third waves. The findings imply that Korea's governmental interventions to address the shortage of face masks and to regulate mask wearing were successful relatively in a short time. In contrast, the study also reports that there may be relative less number of science-based news reports like the ones on the effectiveness of face masks or different levels of filter types. This study exemplifies how a big data analysis can be applied to evaluate and enhance public health communication.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.275-294
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• 2019

In order to understand the recent trends in mathematics education research papers, data mining method was applied to analyze journals of the mathematics education posterior to the year of 2016. Text mining method is useful in the sense that it utilizes statistical approach to understand the linkages and influencing relationship between concepts and deriving the meaning that data shows by visualizing the process. Therefore, this research analyzed the key words largely mentioned in the recent mathematics education journals. Also the correlation between the subjects of mathematics education was deduced by using topic modeling. By using the trend analysis tool it is possible to understand the vital point which researchers consider it as important in recent mathematics education area and at the same time we tried to use it as a fundamental data to decide the upcoming research topic that is worth noticing.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.179-203
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• 2021

This study aims to grasp the key issues and the direction for digital divide research in South Korea. Based on the 488 KCI journal articles published between 2003 and 2020, the authors analyzed the changes in the number of articles per year and the subject areas of journals. Furthermore, the topic modelling and keyword network anlaysis were applied to identify the subjects of research. The main findings can be summarized as follows: first, there was a stable trend for a while after the number of articles had increased by the year of 2007, and then there has been a sharp increase since 2019. Second, digital divide research has been conducted from diverse fields including social science, multidiscipline, and engineering. Third, the six subject areas were identified which are 'digital divide among regions', 'digital divide among people with disabilities', 'technical environment of digital divide', 'divide from information use and its consequence', 'legal and institutional environments of digital divide', and 'digital divide of the elderly'. Finally, it was shown that the areas of 'divide from information use and its consequence' and 'technical environment of digital divide' have attracted attention recently.