• Strategy21
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• s.30
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• pp.99-142
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• 2012

Withe increased North Korea's security threats, the South Korean navy has been faced with deteriorating security environment. While North Korea has increased asymmetric forces in the maritime and underwater with the development of nuclear weapons, and China and Japan have made a large investment in the buildup of naval forces, the power of the Pacific fleet of the US, a key ally is expected to be weakened. The biggest threat comes from China's intervention in case of full-scale war with North Korea, but low-density conflict issues are also serious problems. North Korea has violated the Armistice Agreement 2,660 times since the end of Korean War, among which the number of marine provocations reaches 1,430 times, and the tension over the NLL issue has been intensifying. With tension mounting between Korea and Japan over the Dokdo issue and conflict escalating with China over Ieo do Islet, the US Navy has confronted situation where it cannot fully concentrate on the security of the Korean peninsula, which leads to need for strengthening of South Korea's naval forces. Let's look at naval forces of neighboring countries. North Korea is threatening South Korean navy with its increased asymmetric forces, including submarines. China has achieved the remarkable development of naval forces since the promotion of 3-step plan to strengthen naval power from 1989, and it now retains highly modernized naval forces. Japan makes an investment in the construction of stat of the art warship every year. Since Japan's warship boasts of its advanced performance, Japan's Maritime Self Defense Force is evaluated the second most powerful behind the US Navy on the assumption that submarine power is not included in the naval forces. In this situation, naval power construction of South Korean navy should be done in phases, focusing on the followings; First, military strength to repel the energy warship quickly without any damage in case of battle with North Korea needs to be secured. Second, it is necessary to develop abilities to discourage the use of nuclear weapons of North Korea and attack its nuclear facilities in case of emergency. Third, construction of military power to suppress armed provocations from China and Japan is required. Based on the above naval power construction methods, the direction of power construction is suggested as follows. The sea fleet needs to build up its war potential to defeat the naval forces of North Korea quickly and participate in anti-submarine operations in response to North Korea's provocations. The task fleet should be composed of 3 task flotilla and retain the power to support the sea fleet and suppress the occurrence of maritime disputes with neighboring countries. In addition, it is necessary to expand submarine power, a high value power asset in preparation for establishment of submarine headquarters in 2015, develop anti-submarine helicopter and load SLAM-ER missile onto P-3C patrol aircraft. In case of maine corps, division class military force should be able to conduct landing operations. It takes more than 10 years to construct a new warship. Accordingly, it is necessary to establish plans for naval power construction carefully in consideration of reality and future. For the naval forces to safeguard maritime sovereignty and contribute to national security, the acquisition of a huge budget and buildup of military power is required. In this regard, enhancement of naval power can be achieved only through national, political and military understanding and agreement. It is necessary to let the nation know that modern naval forces with improved weapon system can serve as comprehensive armed forces to secure the command of the sea, perform defense of territory and territorial sky and attack the enemy's strategic facilities and budget inputted in the naval forces is the essential source for early end of the war and minimization of damage to the people. If the naval power construction is not realized, we can be faced with a national disgrace of usurpation of national sovereignty of 100 years ago. Accordingly, the strengthening of naval forces must be realized.

• Korea journal of population studies
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• v.23 no.2
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• pp.79-112
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• 2000

This study was focused on the contrasting pattern of part-time employment between Japan and Taiwan where the environments are similar in terms of expanding service industries and increasing flexibility of labor. In Japan, the expansion of part-time employment and its feminization have occurred, whereas they have not at all in Taiwan. The purpose of this study was to examine the reasons behind this phenomena, and to explore what relations they might have with the supply of women\`s labor in each country. Data analysis showed the following results. First, when the phenomena of part-time employment in Japan and Taiwan are summarized as \`active\` and \`inactive\` models, the difference could be explained by a structure-oriented approach rather than an individual-oriented approach. In other words, the difference between the two countries is mainly because of the structural characteristics of the labor market. a combination of capitalism and patriarchy, and an effect of state welfare and family policies rather than a \`voluntaristic choice\` due tn household work and child rearing. In light of this. the labor market segmentation and flexibility of labor theory in particular provided a useful frame for explanation. Second, with regard to the supply of women\`s labor, the difference between Japan and Taiwan could be found in the structure of the labor market and in family response strategies. The large corporation-oriented and strictly divided labor market structure in Japan activated part-time employment and its feminization, whereas, the small family-oriented businesses and less divided labor market in Taiwan supported the continuity of full-time employment of married women. There was also a room for informal employment in Taiwan which made part-time employment unnecessary. This study showed that even within similar environments of expanding service industry and pursuing flexibility of labor different measures and adaptations were possible. The case of Taiwan in particular, showed the significance of an informal labor market which was a part of industrialization process and a strategy of producing various products through a subcontracting network.

• Korean Journal of Ecology and Environment
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• v.53 no.3
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• pp.241-254
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• 2020

Three introduced fish species, Japanese white crucian carp (Carassius cuvieri Temminck and Schlegel, 1846), bass (Micropterus salmoides Lacepède, 1802) and bluegill (Lepomis macrochirus Rafinesque, 1819), are dominant fishes in Korean freshwater ecosystem. In this study, we analyzed habitat environment conditions of these three species and their impacts to fish communities in streams across South Korea. Fish community data were obtained from the database of the Stream/River Ecosystem Survey and Health Assessment program maintained by the Ministry of Environment and the National Institute of Environmental Research, Korea. Our results showed that species richness and Shannon diversity of fish were higher at the presence sites of introduced fish than at the absence sites. However, when the abundance of these introduced fish species was increased, the species richness and abundance of fish were decreased. An association analysis showed that the introduced fish species had a low similarity in their appearance with some indigenous fishes such as Siniperca scherzeri and Channa argus and some endemic fishes of Korea such as Zacco koreanus, Sarcocheilichthys variegatus wakiyae, and Acheilognathus yamatsutae. In addition, the introduced fish species had a low appearance similarity with a large number of fishes in their association networks. Finally, our results presented that these introduced fish species influenced the negative impacts to the stream fish communities, and they were potential risk factors for fish community in Korean freshwater ecosystem. Therefore, it is necessary that continuous monitoring and establishment of management strategy for introduced fish species to preserve fish resource and biodiversity in the Korean streams.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.

• Korean Security Journal
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• no.14
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• pp.517-534
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• 2007

Since the beginning of human society, there have always been struggles and competitions for survival and prosperity, terrorism is not a recent phenomenon, however in modern times it has progressed to reflect the advances in civilization and power structures. At the time of the 9.11 terrorist attacks in the U.S. A., a new world order was in the process of being established after the breakdown of the Cold War era. The attacks drove both the Western and the Islamic worlds into heightened fear of terrorism and war, which threatened the quality of life of the whole mankind. Through two war campaigns against the Islamic world, it seems the U.S. has been pushing its own militaristic security road map of the Greater Middle East democratic initiative, justifying it as a means to retaliate and eradicate the terrorist threats towards themselves. However, with its five-year lopsided victories that cost the nation almost four thousand military casualties, and the war expenses that could match the Vietnam war, the U.S. does not yet seem to be totally emancipated from the fears of terrorism. Terrorism, in itself, is a means of resisting forced rules a form of alternative competition by the weak against the strong, and a way of expressing a dismissive response against dictatorial ideas or orders which allow for no normal changes. Intrinsically, the nature of terrorism is a reaction opposing power logics. Confronted with the absolute military power of the U.S., the Islamic strategies of terrorism have begun to rapidly evolve into a new stage. The new strategies take advantage of their civilization and circumstances, they train and inspire their front-line fighters on the Internet, and issue their orders through the clandestine network of the Al Qaeda operatives. These spontaneously generated strategies have been gained speed among the second, and third Islamic generations, many of whom are now spread throughout western societies. This represents a failure of the power-driven, one-sided overseas security initiatives by the U.S., and is creating a culture of fear and distrust in western societies. It is feared that the U.S. war campaigns have made the clash of religions far worse than before, and may ever lead to global ethnic separations and large-scale population movements. Eventually, it may result in the terrorist groups, enlarged and secretly supported by the huge sums of oil money, driving all mankind into a series of irreparable catastrophes.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.1 s.1
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• pp.139-155
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• 2001

We investigate the vertical velocity models beneath the newly installed broadband seismic network of KMA (Korea Meteorological Administration) by using receiver function inversion technique. The seismic phases are primarily P-to-S conversions and reverberations generated at the two highest impedance interfaces like the Moho (crust-mantle boundary) and the sediment-basement contact. We obtained the teleseismic P-wave receiver functions, which were derived from teleseismic records of Seoul (SEO), Inchon (INCN), Tejeon (TEJ) , Sosan (SOS/SES), Kangnung (KAN), Ulchin (ULC/ULJ), Taegu (TAG), Pusan (PUS), and Ullung-do (ULL) stations. For Kwangju (KWA/KWJ) and Chunchon (CHU) stations, the Moho conversion Ps arrivals and waveforms of radial receiver functions are azimuthally inconsistent and unclear. From the receiver function inversion result, we found that crustal thickness is 29 km at INCN, SEO, and SOS (SES) stations, 28 km at KAN station in the Kyonggi Massif, 32 km at TEJ station in Okchon Folded Belt, 34 km at TAG, 33 km at PUS station in the Kyongsang Basin, 32 km at KWJ station (readjusted station by prior KWA station) included in the Youngdong-Kwangju Depression Zone, 28 km at ULC station in the eastern margin of the Ryongnam Massif, and 17 km at ULL station in the Ullung Island of the East Sea, respectively. The Moho configuration of INCN, SOS, KWJ, and KAN stations show a laminated smooth transition zone with a 3-5 km thick. The upper crusts(${\sim}5km$) of KAN, ULC, and PUS stations show complex structures with a high velocity. The unusually thick crusts are found at the TAG and PUS stations in the Kyongsang Basin compared to the thin (29-32 km) crust of the western part (INCN, SEO, SOS, TEJ, and KWA stations) The crustal thickness beneath Ullung Island (ULL station) shows the suboceanic crust with about 17 km thickness and complex with a high velocity layer of the upper crust, and the amplitudes of Incoming Ps waves from the western direction are relatively large compared to those from othor directions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.71-84
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• 2004

There is a growing demand for agrometeorological information that end-users can use and not just interesting information. lo achieve this, each region/community needs to develop and provide localized climate and weather information for growers. Additionally, provide tools to help local users interpret climate forecasts issued by the National Weather Service in the country. Real time information should be provided for farmers, including some basic data. An ideal agrometeorological information system includes several components: an efficient data measuring and collection system; a modern telecommunication system; a standard data management processing and analysis system; and an advanced technological information dissemination system. While it is conventional wisdom that, Internet is and will play a major role in the delivery and dissemination of agrometeorological information, there are large gaps between the "information rich" and the "information poor" countries. Rural communities represent the "last mile of connectivity". For some time to come, TV broadcast, radio, phone, newspaper and fax will be used in many countries for communication. The differences in achieving this among countries arise from the human and financial resources available to implement this information and the methods of information dissemination. These differences must be considered in designing any information dissemination system. Experience shows that easy across to information more tailored to user needs would substantially increase use of climate information. Opportunities remain unexplored for applications of geographical information systems and remote sensing in agro meteorology.e sensing in agro meteorology.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.