• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.1-14
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• 2022

This study aims to identify topics in newspaper articles related to fine particle matter and to investigate the characteristics and time series trend of each topic. Related national newspaper articles during 1990 and 2021 were collected from Bigkinds. A total of 18 topics have been discovered using LDA, and 11 clusters deduced from clustering. Hot topics include related products/residence, overseas cause(China), power plant as a domestic cause, nationwide emergency reduction measures, international cooperation, political issues, current situation & countermeasure in other countries, and consumption patterns. Cold topics include the concentration standard and indoor air quality improvement. These findings would be useful in inferring the political direction and strategies. In particular, the consumer protection policy should be expanded as the related market is growing. It will also be necessary to pursue policies that will promote public safety and health, and that will enhance public consensus and international cooperation.

• The Pure and Applied Mathematics
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• v.17 no.4
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• pp.389-396
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• 2010

We investigate the existence of homoclinic orbits of the following systems of $Li{\'{e}}nard$ type: $a(x)x^'=h(y)-F(x)$, $y^'$=-a(x)g(x), where $h(y)=m{\mid}y{\mid}^{p-2}y$ with m > 0 and p > 1 and a, F, 9 are continuous functions such that a(x) > 0 for all $x{\in}{\mathbb{R}}$ and F(0)=g(0)=0 and xg(x) > 0 for $x{\neq}0$. By a series of time and coordinates transformations of the above system, we obtain sufficient conditions for the positive orbits of the above system starting at the points on the curve h(y) = F(x) with x > 0 to approach the origin through only the first quadrant. The method of this paper is new and the results of this paper cover some early results on this topic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.607-615
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• 2020

The purpose of this study was to analyze the research status and trends related to the industrial mathematics based on text mining techniques with a sample of 4910 papers collected in the SIAM Journal on Applied Mathematics from 1970 to 2019. The R program was used to collect titles, abstracts, and key words from the papers and to analyze topic modeling techniques based on LDA algorithm. As a result of the coherence score on the collected papers, 20 topics were determined optimally using the Gibbs sampling methods. The main results were as follows. First, studies on industrial mathematics were conducted in a variety of mathematics fields, including computational mathematics, geometry, mathematical modeling, topology, discrete mathematics, probability and statistics, with a focus on analysis and algebra. Second, 5 hot topics (mathematical biology, nonlinear partial differential equation, discrete mathematics, statistics, topology) and 1 cold topic (probability theory) were found based on time series regression analysis. Third, among the fields that were not reflected in the 2015 revised mathematics curriculum, numeral system, matrix, vector in space, and complex numbers were extracted as the contents to be covered in the high school mathematical curriculum. Finally, this study suggested strategies to activate industrial mathematics in Korea, described the study limitations, and proposed directions for future research.

• International Area Studies Review
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• v.13 no.1
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• pp.81-102
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• 2009

The discussion of the relationship between macro-economic variables is very important research topic. the most economic variables discussed in connection with the liberalization of capital is the exchange rate and stock prices and these two variables have a relationship of mutual influence are identified. However, the results on the time frequency and the time span of a variable appear differently. Therefore, the purpose of this research describes a cause that the result of prior research varied and presents more reliable research methodology. In this research, when the time frequency and span varied, the VAR analysis of the exchange rate and stock prices appeared differently. So, we use the Monte Carlo simulation method in order to performing our purpose. Our research supports the existing research said the ratio that each coefficient VAR model contained 95% confidence interval of estimated coefficient in Monte Carlo simulation is higher when it is applied more the long term and frequent observation.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.95-106
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• 2023

Korean healing research has developed over the past 20 years along with the growing social interest in healing. The field of healing research is diverse and includes legislated natural-based healing. In this study, abstracts of 2,202 academic journals, master's, and doctoral dissertations published in KCI and RISS were collected and analyzed. As for the research method, LDA topic modeling used to classify research topics, and time-series publication trends were examined. As a result of the study, it identified that the topic of Korean healing research was connected with 5 types and 4 mediators. The five were "Healing Tourism," "Mind and Art Healing," "Forest Therapy," "Healing Space," and "Youth Restoration and Healing," and the four mediators were "Forest," "Nature," "Culture", and "Education". In addition, only legalized healing studies extracted from Korean healing research and the topics were analyzed. As a result, legalized healing research classified into four. The four types were "Healing Spatial Environment Plan", "Healing Therapy Experiment", "Agricultural Education Experiential Healing", and "Healing Tourism Factor". Forest Therapy, which has the largest amount of research in legalized healing, Agro Healing and Garden Healing which operate similar programs through plants, and Marine Healing using marine resources also analyzed. As a result, topics that show the unique characteristics of individual healing studies and topics that are considered universal in all healing studies derived. This study is significant in that it identified the overall trend of research on Korean healing facilities and programs by utilizing LDA topic modeling.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.535-550
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• 2016

Securing the safety and the reliability of liquid-propellant rocket engines (LREs) for space vehicles is indispensable as engines consist of many complex components and operate under extremely high energy-dense conditions. Thus, health monitoring has become a mandatory requirement, especially for the reusable LREs that are currently being developed. In this context, a dynamic simulation program based on MATLAB/Simulink was developed in the current research on the Space Shuttle Main Engine (SSME), a partly reusable engine. Then, a series of fault simulations using this program was conducted: at a steady state operating condition (104% Rated Propulsion Level), various simulated fault conditions were artificially injected into the simulation models for the five major valves, the pumps, and the turbines of the SSME. The consequent effects due to each fault were analyzed based on the time responses of the major parameters of the engine. It is believed that this research topic is an essential pre-step for the development of fault detection and diagnosis algorithms for reusable engines in the future.

• The Journal of Information Systems
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• v.17 no.4
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• pp.281-299
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• 2008

An online game is popular topic due to the increased total online game market volume nowadays. Even though many studies on an online game are released, most studies have used survey method that reveal only section of the situation like a snapshot. For this reason, previous studies have a little limitation that does not show dynamically changing issues like a product life cycle and change in customer's satisfaction. Because of this, we researched on an online game with the system dynamic model which can show dynamic simulation to analysis time series data. We chose MMORPG (Massively Multi-play Online Role Playing Game) in sort of an online game because it has many absorbing factors and enthusiastic users. We assumed that the game developer is ready for updated version game and release that periodically and focused on dormant users who used to be enthusiastic about MMORPG and designed simulation model which analyze how to influence of update and CRM strategy on users. The simulation results showed that the update has positive influences to gather new users and hold established users and CRM strategies help to prevent dormant users from transferring to rivals to offer them re-absorbing factors. Through this study, we confirmed importance of update on a online game and suggested the necessity to introduce CRM strategy in an online game market.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.4
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• pp.408-414
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• 2018

Various studies have been conducted to reduce marine accidents. However, research on marine incidents is only marginal. There are many reports of marine incidents, but the main content of existing studies has been qualitative, which makes quantitative analysis difficult. However, quantitative analysis of marine accidents is necessary to reduce marine incidents. The purpose of this paper is to analyze marine incident data quantitatively by applying big data techniques to predict marine incident trends and reduce marine accident. To accomplish this, about 10,000 marine incident reports were prepared in a unified format through pre-processing. Using this preprocessed data, we first derived major keywords for the Marine incidents at sea using text mining techniques. Secondly, time series and cluster analysis were applied to major keywords. Trends for possible marine incidents were predicted. The results confirmed that it is possible to use quantified data and statistical analysis to address this topic. Also, we have confirmed that it is possible to provide information on preventive measures by grasping objective tendencies for marine incidents that may occur in the future through big data techniques.