• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Journal of Korean Society of Forest Science
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• v.36 no.1
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• pp.38-46
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• 1977

The purpose of this study are to analyze economic and psychological factors associated with the forest protection of the mountain people, and to explain the forms of the forest management by ownership classes, especially with forest in the production structure of the mountain villages, particulary from Aprial 1st to 20th in 1975. And the basis of the data for this study is to have been obtained by the sample of 462 households, in Jeonbuk province, which were selected by the method of Yandom sampling. In order to determine what relations there are between the forest ownership classes are independent and each of the selected economic and psychological factors, the chi-squre test was used. The findings may be summarized as follows; 1. The area per household forest land of the mountain villages farm families with forest was 1.4ha and are middle classes with the cultivated area, and manage their forest in favor of the forest fuel and the byproducts, which we call "Earn Ownership Management Form". As it is acomplished by the agricultural surplus labor, we can't expect the positive forest investments. 2. The expectation of the proceeds of forest investments seems to be high but 30% of them doubtful. And the mountain villages farm families with above 3ha forest area expect their forest investments to be positive and in future they have hope in the economic management from. 3. The mountainous mountain fram families reply to a small sums of capital and the control of after the fact on account of the negative factors of forest investment. But rural mountain villages farm famillies assist on spending too much money for the control and nexious insects damage. 4. The reason about illegal cut away was mainly their fuels problem and then most of moumtain farm villages was used to forest fuel in their fuel. But 57% of mountainous mountain villages farm families not having forest area, and 66% of them get their fual on the self-supply, and 66.9% of them get from public and nationat forest and other's forest. That is one of the big problems of the forest protection. 5. Above 66% of mountain people think that forest law is severe and 50% of mountainous mountain villages farm families think if usual. Especially ones not having forest area but taking advantage of forest among them think so. 6. Rural mountain villages farm families have comparatively positive attitude for protecting forest, but mountainous mountain villages farm families negative. Classes with above 3ha forest area have more outlook of forest protection. And the more such classes are, the better they can protect forest. 7. There are problem about operation and education of the forest law on the mountainous mountain villages farm families.

• Journal of Korean Society of Forest Science
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• v.85 no.2
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• pp.158-171
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• 1996

In Korea, 80,616 ha in 139 places have been designated as recreation forests by the end of 1994. Among these places, 64,547 ha in 76 places were designated in the national forest, 9,937 ha in 25 places were designated in the public forest, and 6,132 ha in 38 places were designated in private forest. Designs of 10 places and plans of 6 places in 14 places of recreation forest in Chonbuk province were compared and analyzed. The results are as follows: 1. Sixty four types of convenient facilities, 15 types of athletic facilities, 13 types of sanitation facilities, 22 types of education facilities were designed in 10 places of the recreation forest. 2. The material cost was the highest in construction costs in Waryong recreation forest. However, the labor cost was the highest one in construction costs in 5 places of recreation forest ; Dokyu-san, Hoimoon-san, Saesim, Seongsu, and Sokgeum-san And the total construction cost was much higher in private recreation forest than in both of national and public recreation forest. 3. Hoimoon-san was the only recreation forest which makes profit among 14 recreation forests in Chonbuk province. The uses of this recreation forest are increasing, and seasonal uses significantly fluctuate. 4. There are several problems for the public in enjoying these recreation forest places. The access to these places by public transportations is inconvenient. The time required for construction of many places are too long after they are designated as recreation forest, and the management of the established recreation forest is poor. 5. To improve the method of facility design in recreation forest, easy access to nature should be promoted such as recreation forest places and understanding of public concerning nature are needed. Recreation forest places themselves need to offer good facilities, and convenient access. And to make frequent use of recreation forest places, a system of information and excellent service to the public are strongly recommended.

• Korean Journal of Plant Resources
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• v.33 no.2
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• pp.73-79
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• 2020

This study was conducted to evaluate the appropriateness cultivation of R. glutinosa by plug seedling. Cultivation by direct seeding (DS) and plug seedling (PS) of R. glutinosa was conducted in each of the Pyeongchang and Eumseong areas. As a result, locally, for the Eumseong area, aerial parts in leaf length, leaf width and number of leaves per plant were better than for the Pyeongchang area. In the cultivation method, leaf width and number of leaves per plant were better with PS cultivation than with DS cultivation. Locally, rhizome yield of underground parts in the Eumseong area was better than that in the Pyeongchang area, but the rhizome yield with DS cultivation in the Eumseong area and PS cultivation in the Pyeongchang area were statistically the same. The fresh rhizome yield for DS in Eumseong, PS in Eumseong, DS in Pyeongchang and PS in Pyeongchang was 1,253 kg/10a, 1,376 kg/10a, 923 kg/10a and 1,256 kg/10a, respectively. Finally, the catalpol content for DS in Eumseong, PS in Eumseong, DS in Pyeongchang and PS in Pyeongchang was 3.67%, 2.03%, 2.96% and 1.60%, respectively. From these results, it can be seen that the R. glutinosa PS cultivation method can increase the rhizome yield.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.171-198
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• 2009

The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.2
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• pp.360-377
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• 2016

Since after the 1986 economic reform policy(Doi Moi), the central highland in Vietnam has transformed into one of the largest coffee producing areas. The transformation had been supported by mass migration of ethnic Kinhs from the coastal lowland. It did not take long for the Kinh migrants to be the ethnic majority in the region. Meanwhile the growth of coffee industry entailed in socio-economic disparity, specially between Kinh migrants and native ethnic minorities. The disparity has becomed obvious not only between coffee farming Kinhs and non-coffee farming ethnic minorities but also between coffee farming Kinhs and ehtnic minorities. The previous literatures highlight the lack of human and social capital and the lagging modernization in ethnic minority societies. However, they fall short in showing the explicit processes why ethnic minority coffee farmers earn less than ethnic majority counterparts. With a case study of Dak Lak province, this research attempts to show the reason why there is income gap between Kinh and ethnic minority Ede coffee farmers by comparing their ways of producing coffee and selling their products. The results show that Ede's land productivity is significantly lower than Kinh's. It is because Ede farmers use less fertilizer due to the shortage of the capital. Also they often get into debt for coffee production and should pay it back right after the harvest. It deprives them of chance to raise earning by selling the coffee beans at a higher price.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.3
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• pp.502-516
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• 2014

This study has decomposed the Gini coefficient using Korean Labor & Income Panel Study data and empirically analyzed the impact of demographic characteristics and source-specific income of householder on the household income gap using panel analysis. The scope of areas were divided into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' and the period before and after the global financial crisis was examined. The analysis findings are as follows. First, when the entire period was examined by income source using Gini decomposition with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas', the following results were revealed. The absolute and relative contribution level of property income to the gross income was the largest in the category of 'nationwide' and 'metropolitan areas,' while the contribution level of earned income was the largest in the category of 'non-metropolitan areas'. In addition, property income worsened the household income gap the most in the category of 'nationwide' and 'metropolitan areas.' Second, property income worsened the household income gap less after the financial crisis than before the crisis. It is probably because the price of real estate skyrocketed before the global financial crisis, worsening the household income gap, whereas the price drop after the crisis temporarily alleviated the gap. Third, a correlation analysis revealed that households with older householders whose education is high school graduation or below had relatively low gross income, and households with higher source-specific income, especially earned income, had relatively high gross income. Fourth, when the household income determinants were compared through panel analysis with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' the following results were obtained. While the impact of earned income, financial income, and other incomes was greater in non-metropolitan areas than in metropolitan areas, the impact of property income was greater in metropolitan areas than in non-metropolitan areas. To reduce the income gap, the government should impose higher taxes on the high-income class and provide tax benefits to the low-income class, with efforts to create a wide variety of jobs. In addition, since income inequality gets worse as the proportion of incomes generated through asset holdings becomes higher, the government should focus on stabilizing property prices while paying attention to the regional differentiation when carrying out related policies.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.1-19
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• 2010

In offline environment, unlike traditional advertising model through TV, newspaper, and radio, online advertising model draws instantaneous responses from potential consumers and it is convenient to assess. This kind of characteristics of Internet advertising model has driven the growth of advertising model among various Internet business models. There are, conventionally classified, CPM (Cost Per Mile), CPC (Cost Per Click), and CPS (Cost Per Sales) models as Internet advertising expenditure model. These can be examined in manners regarding risks that stakeholders should stand and degree of responsibility. CPM model that is based on number of advertisement exposure is mechanically exposed to users but not actually recognized by users resulting in risk of wasted expenditure by advertisers without any advertising effect. While on aspect of media, CPS model that is based on conversion action is the most risky model because of the conversion action such as product purchase is determined by capability of advertisers not that of media. In this regard, while there are issue of CPM and CPS models disadvantageously affecting only one side of Internet advertising business model value network, CPC model has been evaluated as reasonable both to advertisers and media, and occupied the largest segment of Internet advertising market. However, CPC model also can cause fraudulent behavior such as click fraud because of the competition or dishonest amount of advertising expenditure. On the user aspect, unintentionally accessed advertisements can lead to more inappropriate expenditure from advertisers. In this paper, we suggest "CPCD"(Cost Per Coupon Download) model. This goes beyond simple clicking of advertisements and advertising expenditure is exerted when users download a coupon from advertisers, which is a concept in between CPC and CPS models. To achieve the purpose, we describe the scenario of advertiser perspective, processes, participants and their benefits of CPCD model. Especially, we suggest the new value in online coupon; "possibility of storage" and "complement for delivery to the target group". We also analyze the working condition for advertiser by a comparison of CPC and CPCD models through advertising expenditure simulation. The result of simulation implies that the CPCD model suits more properly to advertisers with medium-low price products rather than that of high priced goods. This denotes that since most of advertisers in CPC model are dealing with medium-low priced products, the result is very interesting. At last, we contemplate applicability of CPCD model in ubiquitous environment.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.