• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Korean Journal of Health Education and Promotion
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• v.27 no.2
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• pp.79-93
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• 2010

Objectives: This study aims to identify the factors associated with the stages of change in colorectal cancer screening, and develop a model explaining these stages of change by using structural equation modeling analysis. Methods: On the basis of literature review and expert advice, we constructed our hypothetical model that consisted of five theoretical constructs(process of change, pros, cons, self-efficacy, stage of change). In 2009, data was collected from 486 participants aged between 50 and 69. The data were analyzed by SPSS 15.0 and AMOS 6.0. Results: In the case of stage of adoption, the contemplation stage was the most common (37.4%), followed by the action stage (26.5%). In the final model of the stage of change for colorectal cancer screening, there was a direct influence of the process of change ($\gamma$=.562, p<.001), and cons ($\gamma$=.132, p<.01) on the stage of change with the terms of GFI .931, AGFI .904, RMR .038, NFI .930, and NNFI .936. Conclusion: It is necessary to produce and disseminate evidence-based cancer screening information, which can mitigate the negative attitude among the public toward cancer screening. Also, it is essential to provide a safer and more comfortable environment at cancer screening center.

• Korean Journal of Computational Design and Engineering
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• v.19 no.2
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• pp.103-110
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• 2014

Engineering changes are indispensable engineering and management activities for manufactures to develop competitive products and to maintain consistency of its product data. Analysis of engineering changes provides a core functionality to support decision makings for engineering change management. This study aims to develop a method for analysis of engineering changes based on On-Line Analytical Processing (OLAP), a proven database analysis technology that has been applied to various business areas. This approach automates data processing for engineering change analysis from product databases that follow an international standard for product data management (PDM), and enables analysts to analyze various aspects of engineering changes with its OLAP operations. The study consists of modeling a standard PDM database and a multidimensional data model for engineering change analysis, implementing the standard and multidimensional models with PDM and data cube systems and applying the implemented data cube to core functions of engineering change management, the evaluation and propagation of engineering changes.

• Human Ecology Research
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• v.55 no.3
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• pp.263-274
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• 2017

The purposes of this study were to verify the longitudinal mediating effects of work-family balance on the relationship of role recognition in the family, marital intimacy and job satisfaction of married women, and to introduce longitudinal mediating effects by using latent growth curve modeling and autoregressive cross-lagged modeling. The subjects were married women from the third year data of the Korean Longitudinal Survey of Women and Family. Structural equational models were conducted with Amos ver. 21.0. The major findings are as follows. First, the result of the longitudinal mediating effects of latent growth modeling is the rate of change of work-family balance mediated between the rate of change of role recognition in the family and the rate of change of job satisfaction, and the rate of change of work-family balance mediated between the rate of change of marital intimacy and the rate of change of job satisfaction. Second, when using the autoregressive cross-lagged modeling, the more role recognition and marital intimacy of third year were the more work-family balance of fourth year, job satisfaction of fifth year. In both models, work-family balance mediated between role recognition in the family, marital intimacy and job satisfaction. Therefore, through this study, mediating effects of work-family balance can be found that there was a longitudinal effects.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.3
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• pp.29-43
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• 2024

Legally protected species are one of the crucial considerations in the field of natural ecology when conducting environmental impact assessments (EIAs). The occurrence of legally protected species, especially 'Endangered Wildlife' designated by Ministry of Environment, significantly influences the progression of projects subject to EIA, necessitating clear investigations and presentations of their habitats. In perspective of statistics, a minimum of 30 occurrence coordinates is required for population prediction, but most of endangered wildlife has insufficient coordinates and it posing challenges for distribution prediction through modeling. Consequently, this study aims to propose modeling methodologies applicable when coordinate data are limited, focusing on Rodgersia podophylla, representing characteristics of endangered wildlife and northern plant species. For this methodology, 30 random sampling coordinates were used as input data, assuming little survey data, and modeling was performed using individual models included in BIOMOD2. After that, the modeling results were evaluated by using discrimination capacity and the reality reflection ability. An optimal modeling technique was proposed by ensemble the remaining models except for the MaxEnt model, which was found to be less reliable in the modeling results. Alongside discussions on discrimination capacity metrics(e.g. TSS and AUC) presented in modeling results, this study provides insights and suggestions for improvement, but it has limitations that it is difficult to use universally because it is not a study conducted on various species. By supporting survey site selection in EIA processes, this research is anticipated to contribute to minimizing situations where protected species are overlooked in survey results.

• The Journal of Society for e-Business Studies
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• v.4 no.1
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• pp.145-157
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• 1999

The challenge is to maximize the time-to-market benefits of concurrent engineering while maintaining control of data and distributing it automatically to the people who need it when they need it. The way PDM systems cope with this challenge is that master data is held only once in a secure vault where its integrity can be assured and all changes to it monitored, controlled and recorded. The structure of PDM is various from vendor, but they have common module. That is PDM and it is most important. The goal is to design the workflow in PDM using object-oriented modeling method. The past methods have concentrated on the flow between workflow engine and agent, but this paper will focus on task. We will model task as individual object. This paper uses OMT(Object Modeling Technique) by James Rumbaugh for base modeling tool and uses DCOM(Distributed Component Object Model) for base ORB(Object Request Broker). Research object is to design the static object model, to design state change by dynamic model and to design data transition by functional model.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.200-211
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• 1997

Most recently, offshore Engineering & Construction field is concerned about integration management technology such as CIM(Computer Integrated Manufacturing), PDM(Product Data Management) and Enterprise Information Engineering in order to cope with the rapid change of engineering and manufacturer specification as per owner's requirement during construction stage of the project. System integration and integrated data modeling with relational database in integration management technology improve the quality of product and reduce the period of the construction project by reason of owing design information jointly. This paper represents the design methodology of system integration using Business Process Reengineering by the case study. The case study is about the offshore plant material information process from front end engineering design to detail engineering for the construction and the basis of monitoring system by integrating and sharing the design information between the 2D intelligent P&ID and 3D plant modeling using relational database. As a result of the integrated data modeling and system integration, it is possible to maintain the consistency of design process in point of view of the material balancing and reduce the design assumption/duration. Near future, this system will be expanded and connected with the MRP(Material Requirement Planing) and the POR (Purchase Order Requisition) system.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.673-689
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• 2022

To understand seasonal changes it is necessary to understand the relationship between celestial bodies in a three-dimensional space, and to this end, modeling activities in which students directly construct, use, evaluate, and modify three-dimensional models are important. In this study, the process of elementary school students using globes and light bulbs to model Earth's motion in a three-dimensional space as a cause of seasonal changes was analyzed. Seventeen sixth graders participated in the modeling process. After exploring phenomena and concepts related to seasonal change, students constructed models using globes and bulbs and used them to explain seasonal changes. Video data recording students' modeling process, students' activity sheets, and transcripts of post-interview were used as research data, and data triangulation was conducted. The modeling level analysis framework was also developed based on previous studies. In particular, the framework was developed in detail in this study in consideration of the concept of Earth's motion as well as understanding model and implementing modeling. In the final analysis framework, the 3D modeling level was classified from level 1 to level 3, and student performance that may appear at each level was specified. As a result of the study, there were two main levels of modeling using globes for elementary school students to explain seasonal changes. The rotation and tilt of the axis of rotation and revolution of the earth were considered but the level at which empirical evidence was not used (level 2), the level at which empirical evidence was used to explain seasonal chages (level 3). However, even when students use empirical evidence, it did not lead to the construction of a scientific model. In this study, the cause was explored in relation to the characteristics of the tool used for modeling.

• Journal of the Korean Society of Costume
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• v.66 no.3
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• pp.147-161
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• 2016

This study analyzed the effects of job style on job satisfaction and turnover intention of fashion models, and the difference in the job style, job satisfaction and turnover intention by model activities period. Data was collected by surveying fashion models with more than 10 modeling experiences, and 230 responses were used in the data analysis. The results of were as follows: First, the job style of fashion models were classified into professional ability type, social relationship-focused type, future-oriented type and body-boasting type. Job satisfaction was classified into satisfaction with working conditions, satisfaction with model activities, and satisfaction with relationships. Turnover intention was classified into intention to change jobs, and intention to quit modeling. Second, being a professional ability type had a negative effect on satisfaction with working conditions, whereas being a future-oriented type had a positive effect on it. The professional ability type and social relationship-focused type had a positive effect on satisfaction with model activities, and the social relationship-focused type had a positive effect on satisfaction with relationships. Third, the future-oriented type and body-boasting type had a negative effect on the intention to change jobs. The social relationship-focused type, future-oriented type and body-boasting type had a negative effect on the intention to quit modeling. Fourth, there were significant differences in the professional ability type, human relationship-focused type, body-boasting type, intention to change jobs and intention to quit modeling by model activities period. Therefore, it is necessary for domestic fashion models to have the appropriate attitude to develop features and competency required for modeling projects and if improvements are made to enhance job satisfaction of fashion models, the fashion modeling industry is expected to make further developments.