• Journal of Technology Innovation
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• v.28 no.4
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• pp.1-26
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• 2020

Due to the spread of Corona 19 around the world, Infectious Disease Medicine and New Medical Devices such as Diagnostic Agent are being rapidly developed and launched, and for the fast supply and demand of these, each country has eased import regulations or has implemented policies for fast approval(NIDS, 2020). On the other hand, New Developed Medical Devices that are not related to New Infectious Diseases, they are still entering the market through strict licensing and licensing regulations, such as delay and cancellation in the test inspection process, etc. Therefore, This Study specialized in the government-managed laws encountered when New Medical Devices enter the market, derive Factors influencing the Strength of Regulations, analyzes the Strength of Regulations, and proposes a Regulatory Response Framework. The Research Method was conducted by Literature Research, was applied by Failure Mode and Effects Analysis(FMEA) Method, Expert Interview(1st): Idea Collection, Expert Interview(2nd): Validation, and Priority through the Application Process of FMEA Method. A Method of Quantifying the Intensity of Regulation was proposed by multiplying the Impact of the Influencing Factors for each stage of regulation and the Burden Impact for each type of Regulatory Affairs to find the Importance of the Regulatory Factors and multiplying the Severity of the Regulatory Impact. The Implications are that major overseas countries and the Korean government are actively responding with Special Regulatory Policies and Mitigation Policies for fast licensing of New Developed Medical Devices in accordance with Corona 19. It is expected that the direction for improvement of regulations and measures to respond to regulations will be implemented so that a more proactive and preemptive response to the regulatory process of the licensing policy for New Devices can be achieved.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.328-338
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• 2018

Central Asia (Uzbekistan, Kazakhstan, Kyrgyzstan) is a hot and arid continental climate, with most areas (68%) consisting of barren vegetation, desert, and meadows. The main agricultural areas for crop production include irrigated farmland, non-irrigated farmland, grassland, prairie and mountain. We are experiencing climate change with recent climate variability increasing. Agriculture is one of major economic sectors and provides a means of livings for the rural population of Central Asia, especially the poor. In the past two decades, Central Asia has experienced a high population growth rate, with Kazakhstan at 16.8%, Uzbekistan at 34.5% and Kyrgyzstan at 28.4%. As a major industry, Kazakhstan has the largest share of exports of agricultural products followed by petroleum, mineral resources, steel, and chemicals. Uzbekistan is the fifth largest cotton exporter as well as the sixth largest cotton producer in the world. Kyrgyzstan exports ores, stones, cultured pearls, and minerals. These three countries are rich in mineral resources, agricultural products, and energy resources. However, not only do they have difficulties in economic development due to the weakness of logistics and industrial infrastructure, but they also have imperceptible cooperation and investment among countries due to insufficient research and development. Through this study, we will investigate national outlook, economic indicators, major agricultural products, import and export status, and agricultural technology cooperation status, and study how Korean agricultural industry advances into these countries through SWOT analysis. Through this, we hope to contribute to the basic data of Central Asian studies and cooperation and investment in agriculture in each country. In addition, in order to increase cooperative exchange and investment in these countries, we will prepare a Central Asia logistics hub for the rapidly changing interKorean railroad era.

• Korea Trade Review
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• v.48 no.4
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• pp.303-320
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• 2023

Recently, import and export companies have been using T/T remittance and Surrender B/L more frequently than L/C when selecting the process and method of trade payment settlement. The new trade and logistics environment is thriving in the era of the Fourth Industrial Revolution (4IR). Document-based trade transactions are undergoing a digitalization as bills of lading or smart contracts are being developed. The purpose of this study is to verify whether exporters choose export payment types based on negotiating factors. In addition, we would like to discuss the application of the characteristics of the new trade and logistics environment. Data for analysis was collected through surveys. The collection method consisted of direct visits to the company, e-mail, fax, and online surveys. The survey distribution period is from February 1, 2023, to April 30, 2023. The questionnaire was distributed in 2,000 copies, and 447 copies were collected. The final 336 copies were used for analysis, excluding 111 copies that were deemed inappropriate for the purpose of this study. The results of the study are shown below. First, among the negotiating factors, the product differentiation of exporters did not significantly affect the selection of export payment types. Second, among the negotiating factors, the greater the purchasing advantage recognized by exporters, the higher the possibility of using the post-transfer method. In addition to analyzing the results, this study suggests that exporters should consider adopting new payment methods, such as blockchain technology-based bills of lading and trade finance platforms, to adapt to the characteristics of the evolving trade and logistics environment. Therefore, exporters should continue to show interest in initiatives aimed at digitizing trade documents as a response to the challenges posed by bills of lading. In future studies, it is necessary to address the lack of social awareness in Korea by conducting advanced research abroad.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.