• Korean Security Journal
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• no.52
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• pp.73-91
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• 2017

This study is based on the political and economic standpoint of each country, Use advanced equipment to prevent new terrorism from causing widespread damage, In order to establish a countermeasures against terrorism in accordance with the reality of Korea, which is effective in responding to terrorist attacks, Korea conducted a SWOT analysis of the terrorist environment and terrorist environment through specialists. First, internal strengths of Korea 's terrorist environment include stable security situation, weakness of religious and ethnic conflicts, strong regulation and control of firearms, and counter terrorism capabilities and know - how accumulated during major international events. Second, the internal weaknesses of the terrorist environment in Korea include the insecurity of the people, the instability caused by the military confrontation with North Korea, the absence of anti-terrorism law system, the difficulty of terrorism control and management by the development of the Internet and IT technology. Third, the external opportunities for Korea 's terrorist environment are as follows: ease of supplementation and learning through cases of foreign terrorism failure, ease of increase of terrorist budget and support with higher terrorism issues, strengthening of counterterrorism through military cooperation with allied nationsRespectively. Fourth, the external threats to the terrorist environment in Korea are the increase of social dissatisfaction due to the continuous influx of defectors and foreign workers, the goal of terrorism from international terrorist organizations through alliance with the United States,Increased frequency of incidents, and increased IS coverage of terrorism around the world. In addition, the SWOT in - depth interviews on the terrorist environment of the expert group were conducted to diagnose and analyze the problems, terrorism awareness and legal system in the Korean terror environment. The results of the study are summarized as follows.First, the basic law on terrorism should be enacted.Second, the establishment of an integrated anti-terrorism organization.Third, securing and nurturing specialized personnel in response to terrorism.

• The Korean Journal of Archival Studies
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• no.35
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• pp.3-40
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• 2013

Establishment and expansion of a FOI regime is a fundamental basis for modern democracy. Informed decisions and supports by the people are critical to establishment of democratic institutions and policies. The best tool to make informed decisions and to ensure accountability is the FOI. For effective FOI, good records management is necessary requirement. This paper observes and analyses the development of the FOI in the U.S., the Open Government policy, and the government records management reforms under Obama Administration to search viable solutions for Korean FOI and public records management reforms. Major revisions and advancement of the FOIA in the United States are examined, especially the revision of the FOIA as the OPEN Government Act of 2007. The FOIA revision enhanced greatly the freedom of information in the U.S. including the establishment of an independent FOI ombudsman by the Congress. The paper also discusses the Presidential memoranda on the Open Government and the FOI by President Obama, the following directives, Presidential memorandum on government records management and the Government Records Management Directive. Major contents of the directives, plans, and achievement are summarized and analysed. Finally, this paper compares the government records management reforms under former President Roh Mu Hyun with the Obama's reform drive. The comparison found that major difference in the "top-down" government records reforms are the difference in democratic institutions such as weak congressional politics, strong bureaucratic obstacles, and relatively weak social and professional supports for the reforms in Korea, while these reforms were similar in terms that they were driven by insightful political leaders. Independent FOI ombudsman and national records administration are necessary for such democratic reforms.

• Journal of the Daesoon Academy of Sciences
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• v.40
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• pp.243-272
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• 2022

Jeongeup, Jeollabuk-do, is an area that requires attention from those who study the history of Korean thought. In addition, Jeongeup is an area wherein many works were recorded for the first time in literary history. This is the case with Jeongeupsa as a style of Baekje songs and the lyrics of the noble families of the Joseon Dynasty, Sangchungok. Jeongeup is likewise the location where Choi Chi-won (857~?) was selected to serve as a local taesu (viceroy) and where a unique tradition of music and style were passed down. In this paper, the relationship between Choi Chi-won's role in the process of establishing a silent Confucian academy in Jeongeup and the emergence of scholar culture was examined. When Choi Chi-won left after his term in office, a birth shrine called Taesansa Temple was built to repay the selection of the villagers, and it became the source that led to the opening of the Confucian academy Museongseowon in the future. Jeongeup will be shown to be the location where Choi Chi-won realized his aspirations and honed his capabilities. In particular, Choi Chi-won's played a crucial role in the mid-Joseon Dynasty by supporting the construction and securing the name of Museongseowon. That is why Choi Chi-won was able to be revived as a symbolic figure in the region. In addition, it can be seen that the shape of Choi Chi-won was more sedentary- in the form of a Confucian scholar- and Confucian scholars emphasized the transfer of portraits at Museongseowon. Through the poetry written by Choi Chi-won, readers can learn about the worries and perceptions of scholars during those times. Although his value in the field of poetry is diverse, he can especially be recognized as a Confucian intellectual. In a large number of his works, he expresses his anxiety, agony, and critical inner consciousness all of which came from his encounter with the realities of his time. In fact, Choi Chi-won showed his qualities as a prominent literary figure of his time who had extraordinary aspirations and an admirable work ethic. However, he failed to overcome his regional and mental alienation as a poet in neighboring countries. Therefore, he internalized a sort of fierceness in terms of his perception of the world. However, it seems that it was rather a factor that made his work exhibit a strong lyrical style. In addition, Choi Chi-won's collection of writings includes a number of works that strongly criticized various forms of pathological phenomena caused by terminal phenomena of the time. He also highlighted the wrong in society by realistically depicting the lives poor and needy people and their eventual sacrifice via distorted relationships. This can be read encapsulating the agony of intellectuals of that time. The dictionary definition of a 'Confucian scholar' is "a Confucian term referring to a person or class that embodies Confucian ideology," and in its contemporary meaning it suggests " ⋯ an example of a personality, but not an identity, and the conscience of one's time period as a source of human morality inwardly and social order outwardly." In this respect, it could even be said that Choi Chi-won could be considered the originator of scholar culture.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.