• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Bio-Environment Control
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• v.1 no.2
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• pp.154-161
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• 1992

This experiment was carried oui to determine the relativeness between growth, yield characters and bio-informations as influenced by the spray and rest time intervals of nutrient solution. Tomato(Lycopersicon esculentum Mill.) were grown in aeroponic system on a misting schedule of continuously 60 sec, 30 sec and 10 sec at 10 min intervals with full strength Yamazaki's solution recommended for tomato production. The results obtained were as follows : 1. Leaf area was highest in the plot of 30 sec spray and 10 min rest while the forest one was the plot of 60 sec spray and 10 min rest. Growth characteristics in terms of dry weight of each organ, number of flower, number of flower setted and fruit dry weight were greater in the plot of 30 sec spray and 10 min rest than the other treatments. 2. The number of flower increased with decreasing dry weight but number of flower sorted was not significantly different among treatment except for the plot of 60 sec spray and 10 min rest. 3. Leaf dry weight and fruit dry weight were highly correlated so that 30 sec spray and 10 min rest plot which is the highest fruit dry weight showed the largest leaf area. Continuously sprayed plot reduced markedly the fruit dry weight compared with leaf area. Optimum spray and rest time of nutrient solution in the range of this experiment was determined as 30 sec spray and 10 min rest. 4. Solar radiation within glasshouse during daytime reduced severely compared with outdoor one and air temperature within greenhouse was higher than the leaf temperature of tomato plant. The changes of environmental factors, solar radiation, temperature were accompanied with the sensitive change of bio-informations of tomato leaf Especially differences of spray intervals of nutrient solution affected greatly to the changes of bio-informations : leaf water potential, stomatal resistance and leaf temperature etc. 5. The changing patterns of leaf growth as influenced by the spray and rest intervals of nutrient solution were closely related to the leaf water potential, stomatal resistance and leaf temperature. Feasibility was demonstrated that measurement of bio-information of tomato leaf as influenced by the change of environmental factors could be expected to the amount of growth and fruit yield.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.136-149
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• 2020

It is generally believed that Douglas Fir timber imported from North America is used in repair work for Korean wooden heritage sites due to an insufficient supply of extra-large sized Korean pine timber. Based on this understanding in the cultural heritage repair field, Cultural Heritage Repair Business Entities ("CHRBE") prefer North American Douglas Fir timber which is more easily acquired on the market than large Korean pine timber. However, if CHRBE use large quantities of foreign-origin wood in the heritage repair field, this presents the threat of negative domestic impacts on cultural heritage such as breaching the preservation principal and ultimately weakening material authenticity. Therefore, this study aims to investigate the current supply status of large Korean pine timber through examination of existing research, interviews with experts engaged in CHRBE, and timber mills. With this information, the authors seek to identify whether the market supply of large Korean pine timber is indeed insufficient or not. In addition to this, this paper identifies the reasons why large Korean pine timber is not widely used if such timber supply is actually sufficient. In order to propose suggestions regarding the issues above, the authors study the distribution channel for large Korean pine timber and the price spectrum of this timber through examination of price information from the public agencies under the Korea Forest Service, research papers from the Cultural Heritage Administration, and estimation documents from timber mills. This paper also identifies two main opinions about why Korean timber has not been commonly used in the Korean heritage repair field. The first opinion is that the supply of large Korean pine timber really is insufficient in Korea. However, the second opinion is that it is hardly used due to inappropriateness of the government's procurement and estimation system, despite the fact that the supply of the timbers on the market is actually sufficient. Through the aforementioned research, this paper comes to the conclusion that the second opinion has strong grounds in many aspects. In terms of suggestions, alternative routes are proposed to stimulate the use of large Korean pine timber via supply by the 'Korea Foundation for Traditional Architecture and Technology' and surveys of the price spectrum of the timber, etc.

• Journal of the Korean GEO-environmental Society
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• v.16 no.12
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• pp.45-52
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• 2015

With the rising interest in the environment, more attention on ecological restoration for damaged slope surface to restore its original state has been drawn. Generally, the most useful method is vegetation based spray work. This method uses green soil including sewage sludge, sawdust, paper sludge, and weathered granite soil. However, because there are neither accurate information nor test values about green soil, green soil is often lost by environmental factors such as rainfalls and strong winds. To solve the problem of green soil, it is necessary to prepare design standards about green soil, and conduct studies to deal with green soil loss in consideration of various variables including basic material property, soil quality of slope surface, and weather. This study was conducted in the mixture of green soil and eco-friendly soil stabilizer. With green soil, basic material property test and compaction test were conducted for the analysis on the basic characteristics of green soil. In the mixture with soil stabilizer at a certain ratio, we conducted shear strength test depending on the ratio in order to analyze the maximum shear strength, cohesion and the change in internal friction angles. Furthermore, in the mixture ratio of green soil and soil stabilizer, which is the same as the ratio in the shear strength test, an inclination of slope surface was made in laboratory for the analysis on erosion and germination rate. Finally, this study evaluated the most effective and economic mixing ratio of soil stabilizer to cope with neighboring environmental factors. According to the test, the shear strength of green soil increased up to 51% rely onto the mixing ratio of and a curing period, and its cohesion and internal friction angle also gradually increases. It is judged that the mixture of soil stabilizer was effective in improving shear strength and thereby increased the stability of green soil.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.