• Asia Marketing Journal
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• v.14 no.2
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• pp.177-207
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• 2012

Today's consumers perceive consumption as a representation of themselves. It is not simply an act that fulfills a consumer's physical and practical needs. Even in terms of life quality, consumers increasingly want to achieve an emotional and sensible experience through consumption. Consumers now make decisions based on their need to express their position in relation to other people, pursue emotional satisfaction, and try to improve the quality of life. Culture-art products that meet such internal and external demands of consumers have made significant improvements in both quantity and quality, because of the social interest and policy support. The recognition of personal and social values of culture and arts has brought about interest in and need for culture-art products. Businesses have agilely embraced such change and actively implemented various marketing strategies utilizing culture and arts. For example, businesses began to sponsor artists who produce culture-art products while building facilities for cultural and art performances or exhibitions. Businesses have also provided performances and exhibitions free-of-charge or at affordable prices. As a result, the supply in the market has started to exceed its demand as is often the case in many of other markets. However, such imbalance has occurred not because of over-supply but because of a lack of demand. Given these circumstances, the government and culture and art related organizations, which had mainly concentrated on the supply side, started to recognize the importance of creating personal and social values in culture and arts. As a result, the government and various organizations are now creating various strategies that include policy measures to achieve their new found goal. Unfortunately however, such efforts are not meeting the expectations. Focusing on above-mentioned circumstances and problems, this study aims to find measures to create demand for culture-art products in the internal conditions of those who consume culture-art products. In other words, given that the demand for culture-art products has not increased despite all external conditions to encourage consumption, this study aims to find the reasons in consumers' value judgment on culture-art products. Though there were recent studies on culture-art products that applied consumer behavior on marketing theories, most of them focused on peripheral aspects such as people's motivation for or satisfaction from watching culture-art events. Hence, there is a need to understand what kind of value consumers perceive from culture-art products and how such value cognition leads to consumption in a comprehensive manner. This study acts as follow-up to a separate study entitled "Qualitative Study about Value Cognition and Benefits of Consumer on Culture-Art Products". The current study aims to extend practical implications that enhance the effectiveness of marketing strategies among the producing and policy agencies in the industry. The purpose of this study is to investigate dimensions of value cognition, benefits and attributes of culture-art products, and identify the effects of consumer value cognition on benefits and attributes. The questionnaire was developed based on the conceptual structure of qualitative research and previous researches. It was composed of value cognition, benefits, attributes of culture-art products and demographic variables. This survey was conducted on-line and off-line among a total of 662 persons ranging from their teens to their 50's who were living in Seoul, Gyeonggi-do, various metropolitan cities, and small and medium-sized cities. The data collected was analyzed by factor analysis and path analysis using SPSS WIN 18.0 and AMOS 16.0. This empirical study found that the dimensions of value cognition of culture-art products were categorized into personal goods, aesthetic goods and public property. This shows that the consumers perceive culture-art products as products that are worthy enough to pay the costs not just for personal benefits but also for their social values. Also the formation of value cognition for culture-art products requires special conditions unlike that for physical consumer goods and services, which simply require marketing stimuli. The dimensions of benefits pursued by consuming culture-art products were found to be composed of four types - pursuit of aesthetic benefits, pursuit of actual benefits, pursuit of emotional benefits, and pursuit of conspicuous character. This result implies that people consume culture-art products not just to pursue pleasure from emotional and intelligent satisfaction as well as social relations, but also to seek the needs and benefits embodied at a social level. The dimensions of attributes of culture-art products had seven different factors, - environmental, price, evaluation, people, artwork, composition, and personal relations - which is plentiful. This is because the attributes of culture-art products are very complicated compared to other consumer goods or services. Since culture-art products include not just cultural or artistic works but also all physical, human, environmental, and systemic elements of the products in a comprehensive manner, consumers perceive everything they experience in the process of consuming culture-art products as part of the products. The dimensions of value cognition was found to affect attributes of the products, mostly using pursued benefits as a mediating factors. This result is consistent with the result of qualitative research, and proves that applying the means-end chain theory in the reverse direction is reasonable. The result can be interpreted that consumers' value cognitions for culture-art products turns into actual benefits leading to consumers' decisions. Furthermore, this result reveals that when consumers choose culture-art products, they take into account the attributes of culture-art products depending on the benefits they pursue. These results confirm that despite their conceptual and abstract attributes, culture-art products have values that contribute to actual benefits for individual consumers and society. Hence, value cognition generates benefits to be pursued and this in turn affects the consumers' choices of attributes on products. Based on the conceptual structure of consumers' value cognitions on culture-art products and its dimensions, it is possible to find detailed methods to provide opportunities for education and training to form and reinforce positive value cognition on culture-art products. And through those methods, it will be possible to develop attributes of culture-art products according to the dimensions of pursued benefits, and allow conceptual products become the subject to valuable consumption in real life. These results provide theoretical understanding of consumer behavior in culture marketing and useful information to culture-art producers, companies that use culture and art, and government agencies that use culture-art as a mean to improve the public perception of quality of life. As a follow up on this study, there should be experimental studies that can develop criteria visualizing the demands of consumers who purchase culture-art products and identify their detailed attributes. Studies that compare characteristics of different areas within the culture-art product category and in-depth studies on a specific area or genre will also be needed. In order to develop marketing strategies for culture-art products, studies on the formation and reinforcement of positive value cognition on culture-art products and education for the development of consumer demand as well as on the development and differentiation of attributes of culture-art products depending on types of consumer groups should also follow.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

• Journal of Korean Home Economics Education Association
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• v.19 no.3
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• pp.149-168
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• 2007

The purpose of this study was to analyze the home economics education items in the teacher recruitment examination for secondary school. To achieve the purpose, all the home economics education items, which were carried out for seven times from the school year 2001 to the most recent year 2007, were compared and analyzed. The form of items was analyzed by frequency and rate. Behavioral domain of items was analyzed by content analysis. In this study, some recommendations were suggested for the quality of home economics education items through discussion of science education and society education items, which were abstracted from the school year 2001 to the most recent year 2007. The results of this study were as follows. First, the score ratio of home economics education items was fluid as 20-30% from the school year 2001 to 2004 but it fixed as 30-35% since the school year 2005. In subcategory of home economics education, curriculum items accounted for highest ratio(43%). In the next thing, items of teaching-learning method(35%), evaluation(19%) and philosophy(3%) related to home economics education were followed in order. Second, the form of home economics education items was coexistent form of single item and subordinate item from the school year 2001 to 2004. But it was changed into form of single item by 100% since the school year 2005. Third, regarding the content of home economics education items, most of the curriculum items were related to the content of the 7th National Curriculum. Teaching-learning method items were taken mostly from model of teaching-learning. Evaluation items were taken mostly from performance assessment. Philosophy items related to home economics education were taken only from Habermas's three systems of action on the school year 2005. Fourth, about behavioral domain of home economics education items, most of the curriculum items were level of 'simple knowledge or memory'. Therefore, it was suggested that behavioral domain of curriculum items had to be changed into 'complex knowledge or comprehension and application'. The behavioral domain of teaching-learning method items and education evaluation items was mostly 'complex knowledge or comprehension and application'. However, to bettering the items it was suggested that the behavioral domain of them has to be changed 'comprehension' into more 'application'. Fifth, regarding the coverage of home economics education items, curriculum items were limited only superficial content of the 7th National Curriculum. Therefore, it was suggested that coverage of curriculum items had to be extended to theoretical content, which was philosophical background and various principles of curriculum. It was suggested that coverage of teaching-learning method items had to be extended to the content including various teaching-learning theories and the practical reasoning home economics instruction proved effective as home economics instruction recently. Evaluation items were taken mostly from performance assessment. Therefore, it was suggested that coverage of evaluation items had to be extended to analysis of evaluation result, item validity and reliability, and evaluator's philosophical perspective.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• Korean Journal of Ecology and Environment
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• v.37 no.1 s.106
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• pp.112-121
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• 2004

A study was performed to examine the effects of UV-disinfected reclaimed water on microorganism concentration during rice culture. Four treatments were used and each one was triplicated to evaluate the changes of microorganism concentrations: stream water irrigation (STR), biofilter effluent irrigation (BE), UV-disinfected water irrigation with dose of 6 mW ${\cdot}$ s $cm{-2}$ (UV-6), and UV-disinfected water irrigation with dose of 16 mW ${\cdot}$ s $cm{-2}$ (UV-16). The indicator microorganisms of interest were total coliform (TC), fecal coliform (FC), and E. coli. The biofilter effluent from 16-unit apartment sewage treatment plant was used as reclaimed water and flowthrough type UV-disinfection system was used. Concentrations of indicator microorganisms in the treatment plots ranged from $10^2$ to $10^5$ MPN/100 mL during 24 hours after irrigation in May and June, where initial irrigation water for transplanting reparation was biofilter-effluent without UV-disinfection. It implies that initial irrigation using only non-disinfected reclaimed water for puddling in paddy field can be health-concerned because of more chance of farmer's physical contact with elevated concentration of microorganisms. The concentrations of microorganisms varied widely with rainfall, and treatments using UV-disinfected water irrigation showed significantly lower concentrations than others and their levels were within the range of paddy rice field with normal surface water irrigation. The mean concentrations of STR and BE during growing season were in the range of 4 ${\times}\;10^3$ MPN/100 mL for TC, and 2${\times}\;10^3$ MPN/100 mL for FC and E, Coli, While mean concentrations of UV-S and UV-lS were less than 1${\times}\;10^3$ MPN/100 mL for all the indicator microorganisms. Overall, UV-disinfection was thought to be feasible and practical alternative for agricultural reuse of secondary level effluent in Korea.

• International Commerce and Information Review
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• v.15 no.3
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• pp.353-384
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• 2013

When it comes to the current inter-Korean relationship, the two Koreas are in the step of core practical negotiation on exchanges and cooperation away from deadlock so far. It is expected that they will have more and frequent meetings in the future. Therefore, now is the time the South Korea needed to come up with systematic countermeasures because there is nothing more important and giving more impact on our society than the matter of North Korea. As the purpose of social science lies with the explanation and prediction of the social phenomena in the society, it is considered to be meaningful to analyze the representative military negotiations such as the defense ministry-level talks, general-level talks, and working-level talks between the two Koreas where the participants from South Korea consisting of the military representatives discussed with their counterparts of North Korea since the signing of the armistice in Korea on July 27, 1953. This study analyzes and evaluates the behaviors of North Korea's military negotiations with the South Korea in the Kim Jong-il era on the overall basis. In particular, the research tries to prove that the behaviors of military negotiations under Kim Jeong-il regime were made in the frame of the negotiation model by analyzing many negotiations presented in 'With Century', Kim Il Sung's Memoirs under his anti-Japan-guerilla era and suggesting the analysis frame of anti-Japan-guerilla style negotiation model. Based on the results of this proof, the study looks at carefully the specific characteristics of anti-Japan guerrilla-type negotiation.

• Korean Journal of Child Education & Care
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• v.18 no.3
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• pp.1-20
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• 2018

Objective: The purpose of this study is to provide practical data to effectively implement day care that respects the rights of infants in day care centers by investigating the relationship between private day care center teachers' empowerment, job satisfaction, infant's right respect care, and examining the influence of empowerment and job satisfaction of private day care center teachers on Infant's right respect care. Methods: The 228 day care teachers who work in private daycare centers located in Seoul and Gyeonggi province were the investigated subjects. For the analysis of questionnaire, frequency, percentage, reliability, t-test, ANOVA, correlation, and regression were used as SPSSWIN 20.0 program. Results: First, the difference of empowerment according to the individual variables of teachers was different to career, monthly salary, marital status, and turnover intentions. The difference of job satisfaction according to personal variables showed difference to age, career, marital status, type of work organization, and number of teachers and classes. There was a difference in infant's right respect care to age and marital status. Second, in the relationship between empowerment and job satisfaction and between empowerment and infant's right respect care, there was a positive correlation. The relationship between job satisfaction and infant's right respect care showed there is a positive correlation between infant's right respect care and all other factors except for the pay and promotion factors of job satisfaction. Third, Expert extension, autonomy, fellow relationship and job self have a positive influence on infant's right respect care. Conclusion/Implications: This study suggests that specific efforts should be made at the daycare center to raise the level of expertise and autonomy of private daycare teachers and to give satisfaction to their fellow relationship and their job self factors to effectively implement infant's right respect care.