• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.382-397
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• 2016

This study focuses on the lessons and implications of Germany's measures for the social integration after the reunification, and especially the Germany's measure on labor reproduction for the economic stability of East Germany. This analysis indicates, whether South Korea is going to achieve gradual and peaceful reunification or absorb North Korea due to its sudden collapse, that the reunification, in any case, requires the social integration through economic stability of both North and South. In order to ensure national reconciliation, recovery of homogeneity, and establishment of identity for the economically stable social integration, the new integration of educational system is necessary. Especially, the objective of this study is to find the role and direction of vocational education for the stability of the North Korea's labor market and economic life in order to socially integrate South Koreans with its Northern counterparts. First of all, this study examined a priori example of the experiences during West Germany's social integration process, i.e. the vocational education promotion process for the social stability and economic life. It figured out the problem of vocational education for the integrity as well as analyzed the vocational education differences and integration promotion system between East & West Germany. Even though East and West Germany showed their disparities in each vocational education, they corroborated each other by finding one similar system such as bifurcation, which lead to the integration of the labor market and new vocational education policy for the economic stability. Despite the West Germany's support for the socio-economic integration, nevertheless, the East Germany's capacity turned out to be insufficient, which resulted in the failure of the policy. Based on above discussion, this study intended to suggest the efficient solutions of vocational education for the internal reunification of South and North Korea by promoting the independence and self-support of North Koreans and leading the stability of labor market and economic for the future reunification.

• (The)Study of the Eastern Classic
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• no.37
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• pp.473-507
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• 2009

The purpose of this study is to understand Zhuxi's Taoxuezhengzhi(道學政治) by reorganizing both his political opinion in each different political situation and his consistent political consciousness appeared in his whole political career. He concluded that the politics was the real problematic in Southern Sung, which made its structure distorted. This distorted structure of politics had widely rooted in whole sphere of society. In order to cure this political problematic, Zhuxi had focused on huangdi(皇帝) and chaoting(朝廷). That is why people is the basis of State and the result of politics, while huangdi and chaoting is the basis of politics and the beginnig of politics. According to Zhuxi, forming their political power group of their own will by using huangdi's power, the political elites close to only to huangdi made the function of chaoting unstable, with the result that the political decay produced. In chaoting, it resulted in the weakness of huangdi's power, the collapse of official discipline(紀綱), and the absence of public opinion(公論) and public aggreement(公議). Beyond chaoting, it resulted in the absence of political trust and the degeneration of public morals(風俗). In the Southern Sung were not altered the political orientation and culture based on the political decay, but only political orientation and characteristics of political elite only altered. This proves Zhuxi's approach that all problems in Southern Sung could resolve by the political approach. Zhuxi had suggested political issues in office. The alternatives for those political issues had basis of the theme, the one that saving people(恤民) is the purpose of politics. However his political ideas and the execution of them had been occsionally collapsed by the complex political structue, the mechanisms of political power, and the sameness and privatization of political geography in Southern Sung. Qingyuandanghuo(慶元黨禍) was the final stage of his political frustration, with the result that it led to the failure of Zhuxi's taoxuezhengzhi and interrupted the tradition of taoxue(道學) for the time being.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.