• The Journal of Engineering Geology
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• v.28 no.2
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• pp.217-246
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• 2018

During the selection and characterization of target formations in the Small-scale Offshore $CO_2$ Storage Demonstration Project in the Pohang Basin, we have carefully investigated the possibility of induced earthquakes and leakage of $CO_2$ during the injection, and have designed the storage processes to minimize these effects. However, people in Pohang city have a great concern on $CO_2$-injection-intrigued seismicity, since they have greatly suffered from the 5.4 magnitude earthquake on Nov. 15, 2017. The research team of the project performed an extensive self-investigation on the safety issues, especially on the possible $CO_2$ leakage from the target formation and induced earthquakes. The target formation is 10 km apart from the epicenter of the Pohang earthquake and the depth is also quite shallow, only 750 to 800 m from the sea bottom. The project performed a pilot injection in the target formation from Jan. 12 to Mar. 12, 2017, which implies that there are no direct correlation of the Pohang earthquake on Nov. 15, 2017. In addition, the $CO_2$ injection of the storage project does not fracture rock formations, instead, the supercritical $CO_2$ fluid replaces formation water in the pore space gradually. The self-investigation results show that there is almost no chance for the injection to induce significant earthquakes unless injection lasts for a very long time to build a very high pore pressure, which can be easily monitored. The amount of injected $CO_2$ in the project was around 100 metric-tonne that is irrelevant to the Pohang earthquake. The investigation result on long-term safety also shows that the induced earthquakes or the reactivation of existing faults can be prevented successfully when the injection pressure is controlled not to demage cap-rock formation nor exceed Coulomb stresses of existing faults. The project has been performing extensive studies on critical stress for fracturing neighboring formations, reactivation stress of existing faults, well-completion processes to minimize possible leakage, transport/leakage monitoring of injected $CO_2$, and operation procedures for ensuring the storage safety. These extensive studies showed that there will be little chance in $CO_2$ leakage that affects human life. In conclusion, the Small-scale Offshore $CO_2$ Storage Demonstration Project in the Pohang Basin would not cause any induced earthquakes nor signifiant $CO_2$ leakage that people can sense. The research team will give every effort to secure the safety of the storage site.

• Horticultural Science & Technology
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• v.34 no.6
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• pp.886-897
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• 2016

The physical properties of rooting media for the establishment of plugs in a greenhouse are modified according to variations in the greenhouse environment throughout the season. In this study, we established a standard for rooting media for the production of plug seedlings for each growing season (summer, winter and spring fall). Eight types of peatmoss (PM) and 4 types of perlite (PL) commonly used in Korea were collected and blended with the ratio of 7 parts PM to 3 parts PL (v/v) to make 32 different rooting media blends. We determined the total porosity (TP), container capacity (CC), air-filled porosity (AFP), pH, and electrical conductivity (EC) of the 32 media blends, and 6 media blends were selected for seasonal use. We also conducted additional analyses for plant easily available water (EAW), buffering water (BW), cation exchange capacity (CEC), and nutrient contents in the 6 media blends. The TP, CC, and AFP of the 32 media blends ranged from 64.7 to 96.0%, 42.9 to 90.1%, and 1.3 to 27.8%, respectively, indicating that the physical properties were strongly influenced by the type of PM and PL. The pH and EC of the PMs ranged from 2.96 to 3.81 and 0.08 to $0.47dS{\cdot}m^{-1}$, respectively. However, after blending the PM with the PL the pH was raised and the EC was lowered The media blends selected for the summer growing season were Blonde Golden peatmoss (BG) + No. 1 perlite size < 1 mm (PE1) and Latagro 0-10 mm (L1) + No. 2 perlite size 1-2 mm (PE2). These two media blends had 89.8-90.9% of TP, 80.8-81.3% of CC, and 9.0-9.7% of AFP. The media blends selected for the winter growing season were Sfagnumi Turvas (ST) + PE2 and Latagro 20-40 mm (L3) + PE2. These media blends had 79.9-86.7% of TP, 60.4-74.9% of CC, and 11.8-19.6% of AFP. The TP, CC, and AFP of two media blends, BG + No.3 perlite 2-5 mm (PE3) and Orange peatmoss (O) + PE3, selected for the spring and fall growing seasons, respectively, were 85.2-87.3%, 77.9%, and 7.4-9.4%, respectively. The percentage of EAW of the media blends selected for the spring, summer, and winter growing seasons ranged from 24.2-24.9%, 22.0-28.6%, and 18.0-21.8%, respectively, but the percentages of BW were not significantly different among the selected root media blends. The pH, EC, and CEC of the 6 selected media blends ranged from 3.11-3.97, $0.06-0.26dS{\cdot}m^{-1}$, and $97-119meq{\cdot}100g^{-1}$, respectively.

• 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.