• Cartoon and Animation Studies
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• s.39
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• pp.59-83
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• 2015

Magical realism, blooming and improving in Latin America, opened the new vision about reality and rationalism, coming out from the out-styled frame of past. While having common points with unrealistic literature, which uses fantastical components, magical realism is different from Surrealism and fantasy literature that is focusing on reality and realizing reality intensely. In the early stage of this research, magical realism was restricted by the characteristics of literature of Latin America, but the research of magical realism is expanding in planning Post-Modernism nowadays. Lately, the influence of magical realism is identified in literatures, arts, films, and animations over the world; according to the research, however, research about magical realism in animations was not done in Korea before. A Korean independent animation "A coffee vending machine and its sword" was evaluated positively in many international film festivals is valuable as the research of magical realism. Throughout this study, this animation "A coffee vending machine and its sword" was analyzed by its narrative and images. The analysis of narrative consists two parts. One is about the form of narrative and the other is about contents through the story. Analysis of Image is also divided into two parts: background image and character image. In this animation, the protagonist is narrating about the fantastic accidents in his life and his own feelings towards it. The narration leads audience to understand his situation and feelings in meta-fiction. On the surface, audience watches the love story of a normal girl and coffee vending machine in this artwork, but deep inside the animation, it is visible that the directors tried to make audiences think about the life of 880,000-won Generation in Korea. The background image was represented as real places in Seoul including the landmark of Seoul, making mimesis of reality in Korea. The character image has two conflicting aspects with reincarnated warrior, Jinyoungyoung and a coffee vending machine. It is a hybrid-character transmogrifying between two characters. Likewise, "A coffee vending machine and its sword" has the characteristics of Korean magical realism through form, content and image. Through analyzing the Korean independent animation "A coffee vending machine and its sword", this research tried to find a way of using factors of fantasy, of representing reality as a dramatic device and of using magical realism of Korean animation for bond of sympathy with audience.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.942-948
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• 2010

To predict the potential reduction of $CH_4$ by recovering several types of wastes as of reusable energy sources like RDF, the $CH_4$ emission for each type of waste from Landfill Site 3 of SUDOKWON Landfill was estimated for the period of 2017 to 2024. Without any recovering effort on types of wastes being disposed of at the Landfill, there are producing a total of $526{\times}10^6\;Nm^3$ of $CH_4$; municipal waste of $337{\times}10^6\;Nm^3$, construction waste of $178{\times}10^6\;Nm^3$, and facility waste of $11{\times}10^6\;Nm^3$. It composed of 41.5% to that observed from 2002 to 2009. With properly retrieved by MT(Mechanical Treatment), it released a total of $158{\times}10^6\;Nm^3$ $CH_4$; $127{\times}10^6\;Nm^3$, $28{\times}10^6\;Nm^3$, and $4{\times}10^6\;Nm^3$, respectively. Additionally, when biologically degradable residues can be fully treated by MBT (Mechanical & Biological Treatment) system, the total amount of $CH_4$ emitted from the site will be lowered down as low as $115{\times}10^6\;Nm^3$, which is comparably lower showing only 21.8% to that for without any energy recovery practice. Futhermore, it is far less showing 9.1% to that obtained from 2002 to 2009. It can be decided that predictable amount of $CH_4$ emission reduced could be successfully accomplished and enhanced through ways of energy recovery efforts such as further scale adjustment of LFG treatment capacity in association with currently implemented practices in the landfill site.

• Economic and Environmental Geology
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• v.46 no.1
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• pp.11-19
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• 2013

The Daehwa Mo-W deposit is located within the Gyeonggi massif. Quartz and calcite vein mineralization occurred in the Precambrian gneiss and Jurassic granites. Three main types (Type I: liquid-rich $H_2O$ type, Type II: vapor-rich $H_2O$ type, Type III: $CO_2-H_2O$ type) of fluid inclusions were observed and are classified herein based on their phase relations at room temperature. Within ore shoots, type III fluid inclusions have been classified into four subtypes (type IIIa, IIIb, IIIc and IIId) based on their volume percent of aqueous and carbonaceous ($CO_2$) phase at room temperatures combined with their total homogenization behavior and homogenization behavior of $CO_2$ phase. Homogenization temperatures of primary type I fluid inclusions in the quartz range from $374^{\circ}C$ to $161^{\circ}C$ with salinities between 13.6 and 0.5 equiv. wt.% NaCl. Homogenization temperatures of primary type III fluid inclusions in quartz of main generation, are in the range of $303^{\circ}C$ to $251^{\circ}C$. Clathrate melting temperatures of the type III fluid inclusions were 7.3 to $9.5^{\circ}C$, corresponding to salinities of 5.2 to 1.0 equiv. wt. % NaCl. Melting and homogenization temperatures of $CO_2$ phase of type III fluid inclusions were -57.4 to $-56.6^{\circ}C$ and 29.0 to $30.8^{\circ}C$, respectively. Fluid inclusion data indicate a complex geochemical evolution of hydrothermal fluids. The Daehwa early hydrothermal system is characterized by $H_2O-CO_2$-NaCl fluid at about $400^{\circ}C$. The main mineralization occurred by $CO_2$ immiscibility at temperatures of about 300 to $250^{\circ}C$. At the late base-metal mineralization aqueous fluid formed by mixing with cooler and less saline meteoric groundwater.

• Analytical Science and Technology
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• v.29 no.4
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• pp.170-178
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• 2016

Accuracy and precision of ID methods for different spike isotopes of ⁷⁶Se, ⁷⁷Se, and ⁷⁸Se were compared for the analysis of Selenium using quadrupole ICP/MS equipped with Octopole reaction cell. From the analysis of Se inorganic standard solution, all of three spikes showed less than 1 % error and 1 % RSD for both short-term (a day) and long-term (several months) periods. They showed similar results with each other and ⁷⁸Se showed was a bit better than ⁷⁶Se and ⁷⁷Se. However, different spikes showed different results when NIST SRM 1568a and SRM 2967 were analyzed because of the several interferences on the m/z measured and calculated. Interferences due to the generation of SeH from ORC was considered as well as As and Br in matrix. The results showed similar accuracy and precisions against SRM 1568a, which has a simple background matrix, for all three spikes and the recovery rate was about 80% with steadiness. The %RSD was a bit higher than inorganic standard (1.8 %, 8.6 %, and 6.3 % for ⁷⁸Se, ⁷⁶Se and ⁷⁷Se, respectively) but low enough to conclude that this experiment is reliable. However, mussel tissue has a complex matrix showed inaccurate results in case of ⁷⁸Se isotope spike (over 100 % RSD). ⁷⁶Se and ⁷⁷Se showd relatively good results of around 98.6 % and 104.2 % recovery rate. The errors were less than 5 % but the precision was a bit higher value of 15 % RSD. This clearly shows that Br interferences are so large that a simple mathematical calibration is not enough for a complex-matrixed sample. In conclusion, all three spikes show similar results when matrix is simple. However, ⁷⁸Se should be avoided when large amount of Br exists in matrix. Either ⁷⁶Se or ⁷⁷Se would provide accurate results.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.43-50
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• 2016

Urbanization in Korea has increased significantly and subsequently, various facilities have been concentrated in urban areas at high speed in accordance with a growing urban population. Accordingly, damages have occurred due to a variety of disasters. In particular, fire damage among the social disasters caused the most severe damage in urban areas along with traffic accidents. 44,432 cases of fire occurred in 2015 in Korea. Due to these accidents, 253 were killed and property damage of 4,50 billion won was generated. However, despite the efforts to reduce a variety of damage, fire danger still remains high. In this regard, this study collected fire data, generated from 2007 to 2014 through the Jinju Fire Department and the National Fire Data System(NFDS) and calculated fire risk by analyzing the clustering of fire cases and facilities in Jinju-si based on the current DB of facilities, offered by the Ministry of Government Administration and Home Affairs. As a result, the risk ratings of fire occurrence were classified as four stages under the standards of the US Society of Fire Protection Engineers(SEPE). Business facilities, entertainment facilities, and automobile facilities were classified as the highest A grade, detached houses, Apartment houses, education facilities, sales facilities, accommodation, set of facilities, medical facilities, industrial facilities, and life service facilities were classified as U grade, and other facilities were classified as EU grade. Finally, hazardous production facilities were classified as BEU grade, the lowest grade. In addition, in the case of setting the standard with loss of life, the highest risk facility was the hazardous production facilities, while in the case of setting the standard with property damage, a set of facilities and industrial facilities showed the highest risk. In this regard, this study is expected to be effectively utilized to establish the fire reduction measures against facilities, distributed in urban space by calculating risk grades regarding the generation frequency, casualties, and property damage, through the classification of fire, occurred in the city, according to the facilities.

• Journal of Nutrition and Health
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• v.50 no.5
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• pp.415-425
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• 2017

Purpose: Many studies have suggested that neuronal cells protect against oxidative stress-induced apoptotic cell death by polyphenolic compounds. We investigated the neuroprotective effects and the mechanism of action of Momordica charantia ethanol extract (MCE) against $H_2O_2-induced$ cell death of human neuroblastoma SK-N-MC cells. Methods: The antioxidant activity of MCE was measured by the quantity of total phenolic acid compounds (TPC), quantity of total flavonoid compounds (TFC), and 2,2-Diphenyl-1-pycrylhydrazyl (DPPH) radical scavenging activity. Cytotoxicity and cell viability were determined by CCK-8 assay. The formation of reactive oxygen species (ROS) was measured using 2,7-dichlorofluorescein diacetate (DCF-DA) assay. Antioxidant enzyme (SOD-1,2 and GPx-1) expression was determined by real-time PCR. Mitogen-activated protein kinases (MAPK) pathway and apoptosis signal expression was measured by Western blotting. Results: The TPC and TFC quantities of MCE were 28.51 mg gallic acid equivalents/extract g and 3.95 mg catechin equivalents/extract g, respectively. The $IC_{50}$ value for DPPH radical scavenging activity was $506.95{\mu}g/ml$ for MCE. Pre-treatment with MCE showed protective effects against $H_2O_2-induced$ cell death and inhibited ROS generation by oxidative stress. SOD-1,2 and GPx-1 mRNA expression was recovered by pre-treatment with MCE compared with the presence of $H_2O_2$. Pre-treatment with MCE inhibited phosphorylation of p38 and the JNK pathway and down-regulated cleaved caspase-3 and cleaved PARP by $H_2O_2$. Conclusion: The neuroprotective effects of MCE in terms of recovery of antioxidant enzyme gene expression, down-regulation of MAPK pathways, and inhibition apoptosis is associated with reduced oxidative stress in SK-N-MC cells.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.121-145
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• 2010

University timetabling depending on the educational environments of universities is an NP-hard problem that the amount of computation required to find solutions increases exponentially with the problem size. For many years, there have been lots of studies on university timetabling from the necessity of automatic timetable generation for students' convenience and effective lesson, and for the effective allocation of subjects, lecturers, and classrooms. Timetables are classified into a course timetable and an examination timetable. This study focuses on the former. In general, a course timetable for liberal arts is scheduled by the office of academic affairs and a course timetable for major subjects is scheduled by each department of a university. We found several problems from the analysis of current course timetabling in departments. First, it is time-consuming and inefficient for each department to do the routine and repetitive timetabling work manually. Second, many classes are concentrated into several time slots in a timetable. This tendency decreases the effectiveness of students' classes. Third, several major subjects might overlap some required subjects in liberal arts at the same time slots in the timetable. In this case, it is required that students should choose only one from the overlapped subjects. Fourth, many subjects are lectured by same lecturers every year and most of lecturers prefer the same time slots for the subjects compared with last year. This means that it will be helpful if departments reuse the previous timetables. To solve such problems and support the effective course timetabling in each department, this study proposes a university timetabling support system based on two phases. In the first phase, each department generates a timetable template from the most similar timetable case, which is based on case-based reasoning. In the second phase, the department schedules a timetable with the help of interactive user interface under the timetabling criteria, which is based on rule-based approach. This study provides the illustrations of Hanshin University. We classified timetabling criteria into intrinsic and extrinsic criteria. In intrinsic criteria, there are three criteria related to lecturer, class, and classroom which are all hard constraints. In extrinsic criteria, there are four criteria related to 'the numbers of lesson hours' by the lecturer, 'prohibition of lecture allocation to specific day-hours' for committee members, 'the number of subjects in the same day-hour,' and 'the use of common classrooms.' In 'the numbers of lesson hours' by the lecturer, there are three kinds of criteria : 'minimum number of lesson hours per week,' 'maximum number of lesson hours per week,' 'maximum number of lesson hours per day.' Extrinsic criteria are also all hard constraints except for 'minimum number of lesson hours per week' considered as a soft constraint. In addition, we proposed two indices for measuring similarities between subjects of current semester and subjects of the previous timetables, and for evaluating distribution degrees of a scheduled timetable. Similarity is measured by comparison of two attributes-subject name and its lecturer-between current semester and a previous semester. The index of distribution degree, based on information entropy, indicates a distribution of subjects in the timetable. To show this study's viability, we implemented a prototype system and performed experiments with the real data of Hanshin University. Average similarity from the most similar cases of all departments was estimated as 41.72%. It means that a timetable template generated from the most similar case will be helpful. Through sensitivity analysis, the result shows that distribution degree will increase if we set 'the number of subjects in the same day-hour' to more than 90%.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.