• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1475-1487
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• 2018

To verify remitter's identity when the remitter transfers money to a recipient using an electronic financial service provided by the financial institution, the remitter inputs the information; such as the withdrawal account number, the withdrawal amount, the password pre-registered with the financial company, or the information from authenticating medium that is previously distributed by the financial institution. However, the 1-Way transaction between the financial institution and the remitter is exposed to a great risk of accidents such as an anomaly remittance or a voice phishing fraud. Therefore, in this study, we propose a 2-WAY trade verification model for electronic financial transaction that can be mutually agreed by allowing the recipient to share the transaction information with the remitter and the financial company. We have improved the traditional electronic financial transaction's method by replacing it to 2-WAY trade method, and it is used for various purposes; such as preventing an error within the remittance or voice phishing fraud, enhancing loan transaction and contract transaction, etc. Through these variety of applications, we are expecting to reduce the inconveniences while improving the convenience of financial transaction and vitalizing the P2P transaction of financial institution.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.385-413
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• 2019

This study examines the determinants of net export of electricity among 30 European countries sharing electricity grid during the period of 1990~2014 by separating the sample period before and after 2005 in which ETS was introduced in Europe. The empirical method used in this study is generalize least squared one considering both heterogeneous and serial correlation in the balanced panel data. According to the empirical results, after 2005 introducing the ETS, holing energy resources, concentrating only on few electricity generation resources, and nuclear electricity generation had played more important role in net export of electricity, while renewable energy had negative effect on net export of electricity and coal and gas generation have no effect on net export after introduction of ETS in Europe probably because of high environmental cost. The policy implication of the results would be that reconsidering each country's optimal generation mix strategy and its role in case freely trading electricity.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.95-108
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• 2021

Technology innovation activities are very important for companies to secure technological competitiveness and continue to grow. Korea operates a certification system at the national level to promote corporate innovation activities, and strives to enhance SMEs' global technological competitiveness. Among these, the representative system related to technological innovation is the New Excellent Technology (NET) certification. NET is certified through a strict three-stage screening process, and is operated for the purpose of commercialization of new technology, technology trading, and promotion of early market entry by companies. Acquiring NET certification means that the company has a certain level of technological competitiveness. Therefore, this study attempted to conduct an empirical analysis on which technology innovation activities of companies affect the success of R&D projects and improvement of management performance, centering on NET certification system. To verify this, technology strategy, technology planning, systematic R&D process, internal cooperation, and external cooperation activities were set as major variables. As a result of the empirical analysis, it was confirmed that all variables set in this study individually contributed to the success of the R&D project and improvement of management performance. However, when looking at a comprehensive level that considers all variables, it was analyzed that systematic R&D process management and cooperation activities with external organizations have a statistically significant effect on R&D project success, and technology strategy establishment and technology planning activities, which are the initial stages of R&D, have a statistically significant effect on management performance. This study was conducted on innovation-oriented SMEs that have established and operated corporate R&D centers and are actively conducting R&D activities, and multiple regression analysis was used as an analysis method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Korea Trade Review
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• v.42 no.1
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• pp.27-45
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• 2017

A new phenomenon in recent trade is the rising interest in the trade of product production and manufacturing methods themselves, unlike in the past, when the interest was focused on the trade of tangible goods. That is, technology is considered as the object of trade instead of a simple element of production as "technology itself is commercialized". The broad meaning of technology encompasses all the property of knowledge with economic value. Its narrow meaning refers to technology used to produce and manufacture goods. Technologies have features such as no forms, heterogeneity, accumulation of value and extinction of right. The trade of technology commands different styles and content from that of tangible goods due to their unique characteristics; and accordingly, has various risk factors. In other words, technology can be traded in various ways according to commercial objectives including licensing, technical partnership, and joint investment in addition to general trading. The specific forms of technology transfer strategies depend on the purposes and situations between corporations. In case of technical trade with any form, the parties should be cautious about the following practical aspects: First, the contract should clearly define the scope and transfer method of technology. It is a very important matter how the provider of technology will provide the user of technology with abstract technology with no substantiality. Second, a monopoly on technology recognized as intellectual property rights is granted to their inventors for some periods of time, but anyone can have access to that technology after the term of existence. Thus, it is important to check the terms of existence of a patent as well as the terms of contract. Third, the user of technology should fulfill his confidentiality obligation to prevent the technology of the provider from being leaked to a third party unjustly. Fourth, the provider of technology should make a contribution to the successful implementation of the technology by the user as well as provide the licensed technology. Finally, a model contract is recommended to minimizing the legal hiatus of complex technology transfer trade when concluding a contract.

• Journal of Korea Trade
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• v.27 no.2
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• pp.115-129
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• 2023

Purpose - This study empirically analyzes the consumer value of risk management associated with illegal, unreported, and unregulated (IUU) fishing of fishery products imported to Korea. The global regulatory paradigm for IUU fishing has shifted from production-centered to market-centered. As a result, changes in the international fisheries trade environment emphasizing "transparency" and "legality" of the production process are accelerating. Therefore, changes in the management systems of fishery products entering the country are also needed. Accordingly, this study estimated the consumer value for risk management of IUU fishing, targeting major fish species imported to Korea, and derived the feasibility of introducing related policies. Design/methodology - This study used the choice experiment as an analysis model to estimate consumers' willingness to pay for the "possibility to check for IUU fishing." The choice experiment assumes that the value of a good or service is composed of separable attributes and that the sum of the part-worth of these individual attributes becomes the total value. In this study, respondents were presented with profiles comprising three attributes (country of origin, price, and possibility of checking IUU fishing) and the levels of frozen poulp squid, the subject of the analysis. The participants were asked to select their preferred profile. The marginal willingness to pay for each attribute was derived from the results of the respondents' choices using conditional logit model estimates. Findings - There is a marked difference in utility based on the preference of the country of origin of fishery products among consumers. In addition, the utility of fishery products that have undergone IUU fishing verification was observed to be higher, with the utility marked to be higher for lower prices. Originality/value - Estimating the policy value of the risk management in IUU fishing of imported fisheries products in this study is a novel attempt that has never been conducted before. Several studies have been conducted to assess the risk of IUU fishing associated with the import of fishery products internationally. However, such studies are yet to be conducted in Korea. Instead, policies and studies have focused on issues related to complying with trading partners' legal and transparent standards for exporting fishery products. This study should be the beginning of more in-depth empirical and theoretical explorations to establish order in the domestic seafood market and respond to changes in international regulations on IUU fishing.

• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.38-55
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• 2019

The purpose of this study is to clarify the historical flow of baekdong alloy's usage according to the alloying materials mentioned in document records. For this purpose, we first overviewed the use of copper as a base material for white copper alloys and other types of copper alloys. Baekdong is an alloy of copper and other metals and is currently defined as an alloy of copper and nickel. However, depending on the research subjects and time of the scholars, baekdong may be defined as a metal with over a certain percentage of tin added to copper, or as an alloy of tin, zinc, and lead with copper. There is disagreement regarding the interpretation of this term. Baekdong, which started to appear in the literature of the Three Kingdoms Period, has been steadily seen through the Goryeo and Chosun Dynasties to the modern period. It has been used in various ways, according to each age and culture, from the symbol of the office to trading goods, daily life goods, and money. In the literature, baekdong's alloying material is not only copper and nickel, which are currently defined as alloys, but it is the same in that copper is used as the base metal of the alloy, although it varies slightly from generation to generation. In addition to copper, tin, zeolite, and emerald, zinc and lead also appeared. It was found that baekdong, which means alloy, and baekdong, which means white metal, were mixed. Nickel, which is the alloy material of baekdong as it is currently defined, is a metal with a relatively high discovery time and is widely used as a material for modern industrial fields. Nickel was introduced into Korea at the end of the Joseon Dynasty, but its use is not known in detail. In this study, we examined the acceptance and use of nickel-based baekdong in articles of modern newspapers and in statistical data. Based on the experience of craftsmen, we estimated the period when nickel-based alloys were used in crafts. Material is a direct factor in the development and deterioration of technology, and the development of technology is the basis for the changing of civilizations and cultures. In this context, this study was to investigate baekdong with the material of alloys as a starting point.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.