• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.112-124
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• 2021

This study was conducted to examine and analyze local governments, park status, project characteristics, and the implementation in detail for private park special projects across the country as a means of responding to the sunsetting of urban parks. As a result of the analysis, first, the private park special project, was found to be mainly implemented in cities with a population of more than 100,000, so there was a limit to the application on military installations or in local small cities. Therefore, rather than applying the special system collectively, it was judged that institutional flexibility, considering the characteristics and size of local government, was needed. Second, the current special projects by the park creation donation collection method shows monotonous development centered on apartment houses, so it is necessary to diversify the development by introducing a park preservation method that purchases and donates park sites. Third, it was found that the area standard needs to be eased to less than 50,000m² to include parks with high utilization and good accessibility in urban areas of large cities, as the type and area of parks are limited. Fourth, most special projects are mountain parks, which are feared to damage the natural terrain and skyline, so separate ordinances should be established and applied, and development approaches should be made to allow nature and parks to coexist with the setting of detailed building guidelines for each type of facility. The guidelines should include, first, after the nationwide private park special projects are completed, standards for appropriate returns for similar projects should be established, institutional standards such as the recovery of excess profits should be established, and environmental reviews should be conducted. Second, it was found that local governments should institutionalize the composition of private consultations to promote the efficient management of projects through a cooperative system, and third, a roadmap for maintenance after the donation of special parks should be established.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.1
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• pp.65-72
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• 2008

The purpose of the HLW deep geological disposal is to isolate and to delay the radioactive material release to human beings and the environment for a long time so that the toxicity does not affect to the environment. The main requirements for the HLW repository design is to keep the buffer temperature below $100\;^{\circ}C$ in order to maintain its integrity. So the cooling time of spent fuels discharged from the nuclear power plant is the key consideration factors for efficiency and economic feasibility of the repository. The disposal tunnel/disposal hole spacing, the disposal area and thermal capacity required for the deep geological repository layout which satisfies the temperature requirement of the disposal system is analyzed to set the optimized spent fuels cooling time. To do this, based on the reference disposal concept, thermal stability analyses of the disposal system have been performed and the derived results have been compared by setting the spent fuels cooling time and the disposal tunnel/disposal hole spacing in various ways. From these results, desirable spent fuels cooling time in view of disposal area is derived. The results shows that the time reaching the maximum temperature within the design limit of the temperature in the disposal site is likely shortened as the cooling time of spent fuels becomes short. Also it seems that the temperature-rising and-dropping patterns in the disposal site are of smoothly varying form as the cooling time of spent fuels becomes long. In addition, it is revealed that a desirable cooling time of spent fuels is approximately 40-50 years when spent fuels are supposedly disposed in the deep geological disposal site with its structural scale under consideration in this study.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• 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.