• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.97-105
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• 2023

Concentration of nitrogen, one of the major elements, and ratio of two nitrogen forms (NH₄⁺ and NO₃^-) in the nutrient solution affect the quality and food safety of fresh vegetable produce. This study was conducted to find an appropriate strength and NH₄⁺:NO₃^- ratio of a nutrient solution for growth and development of a Romaine lettuce (Lactuca sativa L. var. longiflora) 'Caesar Green', a representative leafy vegetable, grown in a home hydroponic system. In the first experiment, plants were grown using three types of nutrient solution: A commercial nutrient solution (Peters) and two strengths (GNU1 and GNU2) of a multipurpose nutrient solution (GNU solution) developed in a Gyeongsang National University lab. Plants grown with the GNU1 and GNU2 had greater shoot length, leaf length and width, and biomass yield than Peters. On the other hand, the root hairs of plants grown with Peters were short and dark in color. Tissue NH₄⁺ content in the Peters was higher than that of the GNU1 and GNU2. The higher contents of NH₄⁺ in this solution may have caused ammonium toxicity. In the second experiment, eight treatment solutions, combining GNU1 and GNU2 solutions with four ratios of NO₃^- :NH₄⁺ named as 1, 2, 3 and 4 were used. Both experiments showed more growth in the GNU2 group, which had a relatively low ionic strength of the nutrient solution. The growth of Romaine lettuce showed the greatest fresh weight along with low tissue NO₃^- content in the GNU2-2. This was more advantageous in terms of food safety in that it suppressed the accumulation of surplus NO₃^- in tissues due to the low ionic trength of the GNU2 subgroup. In addition, this is preferable in that it can reduce the absolute amount of the input of inorganic nutrients to the nutrient solution.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.436-445
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• 2023

Methane (CH₄) emissions from rice paddies are mainly observed using the closed chamber method or the eddy covariance method. In this study, a new observation technique combining a portable gas analyzer (Model LI-7810, LI-COR, Inc., USA) and an automatic opening/closing chamber (Model Smart Chamber, LI-COR, Inc., USA) was introduced based on the strengths and weaknesses of the existing measurement methods. A cylindrical collar was manufactured according to the maximum growth height of rice and used as an auxiliary measurement tool. All types of measured data can be monitored in real time, and CH₄ flux is also calculated simultaneously during the measurement. After the measurement is completed, all the related data can be checked using the software called 'SoilFluxPro'. The biggest advantage of the new observation technique is that time-series changes in greenhouse gas concentrations can be immediately confirmed in the field. It can also be applied to small areas with various treatment conditions, and it is simpler to use and requires less effort for installation and maintenance than the eddy covariance system. However, there are also disadvantages in that the observation system is still expensive, requires specialized knowledge to operate, and requires a lot of manpower to install multiple collars in various observation areas and travel around them to take measurements. It is expected that the new observation technique can make a significant contribution to understanding the CH₄ emission pathways from rice paddies and quantifying the emissions from those pathways.

• Korean Journal of Heritage: History & Science
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• v.56 no.3
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• pp.214-232
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• 2023

Stromatolite at the Daegu Catholic University, Gyeongsan was designated as a natural monument in December 2009 because it was very excellent in terms of rarity, accessibility, preservation and scale. From the time of designation, the necessity of confirming the lateral extension of the stromatolite beds with the excavation of the surrounding area, and preparing a preservation plan was raised. Accordingly, the Cultural Heritage Administration conducted an investigation of the scale, production pattern, and weathering state of stromatolites with an excavation from April to December 2022, and based on this, suggested natural heritage values and conservation and use plans. The excavation was carried out in a 1,186m² area surrounding the exposed hemispherical stromatolite (approximately 30m2). Stromatolites are continuously distributed over the entire excavation area, and hemispherical stromatolites predominate in the eastern region, and the distribution and size of hemispherical domes tend to decrease toward the west. These characteristics are interpreted as a result of long-term growth in large-scale lakes, where stratiform or small columnar domes continued to grow and connect with each other, finally forming large domes. Consequently, large and small domes were distributed on the bedding plane in clusters like coral reefs. The growth of plants and lichens, as well as small-scale faults and joints developed on the stromatolite bedding surface, is the main cause of accelerated weathering. However, preservation treatment with chemicals as with dinosaur footprints or dinosaur egg fossil sites is not suitable due to the characteristics of stromatolites, and preservation with the installation of closed protection facilities should be considered. This excavation confirmed that the distribution, size and value of stromatolites are much larger and higher than at the time of designation as a natural monument. Therefore, additional excavation of areas by experts that could not be excavated due to the discovery of buried cultural properties (stone chamber tombs) and reexamination of the expansion designation of natural monuments are required.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.183-200
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• 2024

Recently, with the MZ generation's entry into society and the social participation of the female population, conflicts are occurring between workplace groups that value WLB and existing groups that emphasize collaboration due to differences in work orientation. Public institutions and companies that utilize work-life balance support systems show differences in job Commitment depending on the nature of the work and the activation of the support system. Accordingly, it is necessary to verify the effectiveness of the WLB support system actually operated by the company and present universally valid standards. The purpose of this study is, first, to verify the effectiveness of the support system for work-life balance and to find practical consensus amid changes in policies and perceptions of the working environment. Second, the influence of work-life balance level and job immersion according to work characteristics was analyzed to verify the mutual influence in order to establish standards for WLB operation that reflects work characteristics. For the study, a 2X2 matrix model was used to analyze the impact of work-life balance and work characteristics on job commitment, and four hypotheses were established. First, analysis of the job involvement level of conflict-type group members, second, analysis of the job involvement level of leading group members, third, analysis of the job involvement level of agreeable group members, and fourth, analysis of the job involvement level of cooperative group members. To conduct this study, an online survey was conducted targeting employees working in public institutions and large corporations. The survey was conducted for a total of 9 days from October 23 to 31, 2023, and 163 people responded, and the analysis was based on a valid sample of 152 people, excluding 11 copies that were insincere responses or gave up midway. As a result of the study's hypothesis testing, first, the conflict type group was found to have the lowest level of job engagement at 1.43. Second, the proactive group showed the highest level of job engagement at 4.54. Third, the conformity group showed a slightly lower level of job involvement at 2.58. Fourth, the cooperative group showed a slightly higher level of job involvement at 3.80. The academic implications of the study are that it subdivides employees' personalities into factors based on the level of work-life balance and nature of work. The practical implications of the study are that it analyzes the effectiveness of WLB support systems operated by public institutions and large corporations by grouping them.

• Korean Journal of Mineralogy and Petrology
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• v.37 no.2
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• pp.47-57
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• 2024

Jarosite, a mineral belonging to the alunite family, is found in various low pH environments and can incorporate cations or oxyanions into its structure, either by coprecipitation or substitution. This mineral is sensitive to pH changes and can easily transform into goethite upon geochemical changes, such as an increase in pH. This transformation can release toxic ions from the jarosite, potentially causing additional environmental damage. In addition to potassium (K), sodium (Na) and ammonium (NH₄) can also substitute for cations in jarosite. The formation of jarosites containing these and other cations is significant not only for acid mine drainage but also for the smelting industry. In this study, three different types of jarosites containing various cations were synthesized and the phase transformation of each jarosite to goethite upon pH change were compared. All the jarosites were sensitive to pH changes, showing much higher rates of phase change at pH 8 than at pH 4. At the relatively low pH of 4, the phase transformation of K-jarosite, which is most stable in structure, to goethite was the slowest. For the other two jarosites, the cations have either smaller or larger radii than K ions, resulting in differences in structural stability and they showed more rapid transformations to goethite. However, at pH 8, K-jarosite exhibited a much more rapid transformation to goethite than the other jarosites, which was also evident from the rapid increase in K ions in aqueous solution. The mineral transformation behavior of K-jarosite at higher pH is significantly different from that at lower pH, indicating that the mechanism of the transformation to goethite differs between these conditions, which requires further investigation. The results of this study indicate that the mineral transformation of jarosite in acid mine drainage or smelter waste disposal may significantly influence the behavior of heavy metals. This research provides valuable insights for predicting the behavior of heavy metals in smelting industry waste disposal.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.1
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• pp.83-138
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• 2017

Korea-China FTA entered into force on the 20th of December 2015, and one year elapsed after its effectuation as the FTA with China, our country's largest trading partner. Therefore, this study looks at the trends of air transport trade between Korea and China, and examines the contents of concessions to the air transport services sector in Korea-China FTA, and analyzes the impact on the air transport sector by Korea-China FTA, and proposes our country's aviation policy direction in order to respond to such impact. In 2016 the trends of air transport trade between Korea and China are as follows : The export amount of air transport trade to China was 40.03 billion dollars, down by 9.3% from the last year, and occupied 32.2% of the total export amount to China. The import amount of air transport trade from China was 24.26 billion dollars, down by 9.1% from the last year, and occupied 27.7% of the total import amount from China. The contents of concessions to the air transport services sector in Korea-China FTA are as follows : China made concessions to the aircraft repair and maintenance services and the computer reservation system services with limitations on market access and national treatment in the air transport services sector of the China Schedule of Specific Commitments of Korea-China FTA Chapter 8 Annex. Korea made concessions to the computer reservation system services, selling and marketing of air transport services, and aircraft repair and maintenance without limitations on market access and national treatment in the air transport services sector of the Korea Schedule of Specific Commitments of Korea-China FTA Chapter 8 Annex. The impact on the air transport sector by Korea-China FTA are as follows : As for the impact on the air passenger market, in 2016 the arrival passengers of the international flight from China were 9.96 million, up by 20.6% from the last year, and the departure passengers to China were 9.90 million, up by 34.8% from the last year. As for the impact on the air cargo market, in 2016 the exported goods volumes of air cargo to China were 105,220.2 tons, up by 6.6% from the last year, and imported goods volumes from China were 133,750.9 tons, up by 12.3% from the last year. Among the major items of exported air cargo to China, the exported goods volumes of benefited items in the Tariff Schedule of China of Korea-China FTA were increased, and among the major items of imported air cargo from China, the imported goods volumes of benefited items in the Tariff Schedule of Korea of Korea-China FTA were increased. As for the impact on the logistics market, in 2016 the handling performance of exported air cargo to China by domestic forwarders were 119,618 tons, down by 2.1% from the last year, and the handling performance of imported air cargo from China were 79,430 tons, down by 4.4% from the last year. In 2016 the e-commerce export amount to China were 109.16 million dollars, up by 27.7% from the last year, and the e-commerce import amount from China were 89.43 million dollars, up by 72% from the last year. The author proposes the aviation policy direction of Korea according to Korea-China FTA as follows : First, the open skies between Korea and China shall be pushed ahead. In June 2006 Korea and China concluded the open skies agreement within the scope of the third freedom and fourth freedom of the air for passenger and cargo in Sandong Province and Hainan Province of China, and agreed the full open skies of flights between the two countries from the summer season in 2010. However, China protested against the interpretation of the draft of the memorandum of understanding to the air services agreement, therefore the further open skies did not take place. Through the separate aviation talks with China from Korea-China FTA, the gradual and selective open skies of air passenger market and air cargo market shall be pushed ahead. Second, the competitiveness of air transport industry and airport shall be secured. As for the strengthening methods of the competitiveness of Korea's air transport industry, the support system for the strengthening of national air carriers' competitiveness shall be prepared, and the new basis for competition of national air carriers shall be made, and the strategic network based on national interest shall be built. As for the strengthening methods of the competitiveness of Korea's airports, particularly Incheon Airport, the competitiveness of the network for aviation demand creation shall be strengthened, and the airport facilities and safety infrastructure shall be expanded, and the new added value through the airport shall be created, and the world's No.1 level of services shall be maintained. Third, the competitiveness of aviation logistics enterprises shall be strengthened. As for the strengthening methods of the competitiveness of Korea's aviation logistics enterprises, as the upbringing strategy of higher added value in response to the industry trends changes, the new logistics market shall be developed, and the logistics infrastructure shall be expanded, and the logistics professionals shall be trained. Additionally, as the expanding strategy of global logistics market, the support system for overseas investment of logistics enterprises shall be built, and according to expanding the global transport network, the international cooperation shall be strengthened, and the network infrastructure shall be secured. As for the strengthening methods of aviation logistics competitiveness of Incheon Airport, the enterprises' demand of moving in the logistics complex shall be responded, and the comparative advantage in the field of new growth cargo shall be preoccupied, and the logistics hub's capability shall be strengthened, and the competitiveness of cargo processing speed in the airport shall be advanced. Forth, in the subsequent negotiation of Korea-China FTA, the further opening of air transport services sector shall be secured. In the subsequent negotiation being initiated within two years after entry into force of Korea-China FTA, it is necessary to ask for the further opening of the concessions of computer reservation system services, and aircraft repair and maintenance services in which the concessions level of air transport services sector by China is insufficient compared to the concessions level in the existing FTA concluded by China. In conclusion, in order to respond to the impact on Korea's air passenger market, air cargo market and aviation logistics market by Korea-China FTA, the following policy tasks shall be pushed ahead : Taking into consideration of national air carriers' competitiveness and nation's benefits, the gradual and selective open skies shall be pushed ahead, and the support system to strengthen the competitiveness of air transport industry and airport shall be built, and entry into aviation logistics market by logistics enterprises shall be expanded, and the preparations to ask for the further opening of air transport services sector, low in the concessions level by China shall be made.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• 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.