• Journal of architectural history
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• v.32 no.1
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• pp.73-88
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• 2023

Recently, local studies looking at the 'local(region) as a whole' are gradually increasing. The study of local architectural history is important in that it provides specific information that encompasses the local and the entire and clues to three-dimensionalize the time and space in the local. To infer the 'presentness' of each era, reliable data in various fields are needed. Recently, as many databases (DB) and archives, from the 'National Archives of Korea' to other local archives, have been established and publicized, research resources in the local are growing rapidly in quantity and quality. Nevertheless, it is difficult to comprehensively check the data necessary to study the local architectural history(local architectural history research resources). Against this background, this study confirmed the trend of changes in the archive construction environment and the status and problems of local architectural history research resources in places that currently disclose local history research resources among generalized web-archives. Next, the relationship between the actual research on local architectural history was confirmed through the analysis of existing studies and the data used for Jeju. As a result, local studies, local archives, and local architectural history research agree with recent changes in local research trends, and the degree of archival construction has reached the same level as the available research resources except core data in local architectural history research. However, there is a problem that the density of information that can be used is low because the local architectural history research resources that can be obtained are fragmented because there are no archives and construction entities specialized in local architecture. As each archive has entered the stabilization and upgrading stage, the construction of new archives needs to be reconsidered, but it is time to find a detailed way to link related information quickly and accurately, such as private records, to reduce the gap in information needed in terms of research on local architecture and architecture history.

• Journal of Environmental Health Sciences
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• v.49 no.2
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• pp.118-128
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• 2023

Background: The use of heated tobacco products (HTP) causes many adverse health effects. Although the use of HTP by adolescents is prevalent worldwide, research related to it is very limited. There is a considerable lack of research related to the current HTP use rather than ever-HTP use. In particular, research related to predictive factors for current HTP use in adolescents is scarce. Objectives: The purpose of this study was to analyze related characteristics according to the current use of HTP among South Korean adolescents. Methods: This was a cross-sectional study that used data from the Seventeenth Korean Youth Risk Behavior Web-based Survey (KYRBWS). A total of 54,848 students in 2021 were included in this study. Chi-square-test, multiple logistic regression analysis, and chi-square test for trend were used for analyzing related characteristics according to use of HTP. Results: Overall, 715 (1.3%) students responded as having used HTP during the last 30 days among the 54,848 students. It was found that residence type, subjective body type recognition, subjective health recognition, alcohol use, habitual drug experience, close friend current smoking, and conventional cigarette smoking were significantly associated characteristics with the current use of heated tobacco products. Comparing 'very thin recognition' with 'very fat recognition', the adjusted odds ratio (OR) was 1.93 (95% confidence interval [CI]: 1.29~2.87) for current use of HTP. Additionally, comparing 'very unhealthy recognition' with 'very healthy recognition', the aOR was 3.82 (95% CI: 2.40~6.07) for current use of HTP. Conclusions: Based on these results, residence type, subjective body type recognition, subjective health recognition, alcohol use, habitual drug experience, close friend current smoking, and conventional cigarette smoking were associated with significantly increased odds of current HTP use. Therefore, the results of this study can provide useful evidence about adolescent behaviors in predicting current HTP use.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.455-455
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• 2021

The Life Cycle Assessment (LCA) as an environmental impact assessment tool has received increasing attention over the years. Unlike the water footprint (WF) and carbon footprint (CF) assessments whose focus is only on a single environmental aspect, the LCA systematically analyzes the different impacts along the entire life cycle, making possible the identification of potential environmental tradeoffs. In Korea, LCA has drawn much attention from both industry and academia since the mid-90s. However, the level of Korea-related LCA studies with respect to different sectors in the past 20 years has not been analyzed. This study, therefore, sought to assess the status of environmental Life Cycle Assessment (LCA) studies in Korea, with a view to understanding the current level of sustainability reporting and identify potential research gaps. Online searches of English written articles published between 2000 and 2019 were conducted on Google, Google scholar, Scopus, and Web of Science databases using the Keywords "life cycle assessment", "lca", and "Korea." At the end of the search, about 88 LCA related studies were identified for Korea within the study period. Majority of these studies focused on the construction (49%) and energy (31%) sectors with fewer environmental studies on the transportation (9%), manufacturing (8%), agriculture (2%), and information and communication (1%) industries. Based on publication trend, results show that LCA studies in Korea have been on the rise in the past 20 years, even though the number of publications has not followed a constant pace. In comparison with the economic sectors of the country, reports show an inadequacy in the coverage of major industries of growing economic relevance like the tourism, health, and agriculture, suggesting a need to further increase and improve LCA related studies in these sectors.

• Journal of Distribution Science
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• v.21 no.10
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• pp.39-49
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• 2023

Purpose: The purpose of this study is to explore areas that have been studied extensively in previous studies related to franchising as a platform for global distribution. Furthermore, franchising is a strategic distribution method that gives entrepreneurs the opportunity to replicate an established business model. In addition, franchisees benefit from the use of established branding and receive support from the franchisor. Research design, data, and methodology: This study used the Preferred Reporting Items Systematics Review and Meta-Analyses (PRISMA) method to analyse data from 2003 to 2023 in the Web of Science and Scopus databases. Results: A total of 79 articles were identified and analysed to see trends and related themes such as product distribution, business distribution, business strategy, emerging market, and franchising relationship. Also, publication trends by year related to franchises are also presented. Conclusions: Overall, the research trend related to franchising as a global distribution is well seen, and every year, many researchers begin to explore the topic of franchising as a method of distribution that can be explored from various aspects either quantitatively or qualitatively. Lastly, limitations and recommendations are made to provide guidance for future studies related to the topic broadly and deeply in enriching the findings.

• Korea Trade Review
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• v.45 no.5
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• pp.37-53
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• 2020

This study aims to provide the implications of research development by comparing domestic and international studies conducted on the subject of FTA. To this end, among the papers written during the period from 2000 to July 23, 2020, papers whose title is searched by FTA (Free Trade Agreement) were selected as research data. In the case of domestic research, 1,944 searches from the Korean Citation Index (KCI) and 970 from the Web of Science and SCOPUS were selected for international research, and the research trend was analyzed through keywords and abstracts. Frequency analysis and word embedding (Word2vec) were used to analyze the data and visualized using t-SNE and Scattertext. The results of the analysis are as follows. First, in the top 30 keywords of domestic and international research, 16 out of 30 were found to be the same. In domestic research, many studies have been conducted to analyze the outcomes or expected effects of countries that have concluded or discussed FTAs with Korea, on the other hand there are diverse range of study subjects in international research. Second, in the word embedding analysis, t-SNE was used to visually represent the research connection of the top 60 keywords. Finally, Scattertext was used to visually indicate which keywords were frequently used in studies from 2000 to 2010, and from 2011 to 2020. This study is the first to draw implications for academic development through abstract and keyword analysis by applying various text mining approaches to the FTA related research papers. Further in-depth research is needed, including collecting a variety of FTA related text data, comparing and analyzing FTA studies in different countries.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.155-162
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• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• Journal of Platform Technology
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• v.12 no.1
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• pp.85-90
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• 2024

The trend of seeking knowledge or information has been increasingly shifting towards the digital implementation on the web rather than relying on analog printed media such as books or publications. This shift is driven by the perception that using digital resources, particularly digital dictionaries, is more effective and time-saving compared to traditional paper dictionaries. Consequently, the construction of a semantic network for vocabulary has emerged as a significant issue for linguists, computational linguists, and natural language processing specialists. To address this, linguists have conducted numerous studies to find methods for structuring and classifying the meanings and concepts of vocabulary. In these studies, specialized terminology for constructing vocabulary semantic networks is as crucial as common language. However, in the process of finding and accumulating specialized terminology, there is still a manual step where individuals directly verify and extract specialized terms from paper documents or vast digital datasets. In this paper, we propose an automated program to extract the specialized terms that users desire from digital materials, aiming to compensate for errors in human-operated tasks and streamline the process.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.