We study metamorphism of metasedimetary rocks and origin and evolution of leucogranite form Samcheok area, northeastern Yeongnam massif, South Korea. Metamorphic rocks in this area are composed of metasedimentary migmatite, biotite granitic gneiss and leucogranite. Metasedimentary rocks, which refer to major element feature of siliclastic sediment, are divided into two metamorphic zones based on mineral assemblages, garnet and sillimanite zones. According to petrogenetic grid of mineral assemblages, metamorhpic P-T conditions are $740{\sim}800^{\circ}C$ at $4.8{\sim}5.8\;kbar$ in the garnet zone and $640-760^{\circ}C$ at 2.5-4.5kbar in sillimanite zone. The leucogranite (Imwon leucogranite) is peraluminous granite which has high alumina index (A/CNK=1.31-1.93) and positive discriminant factor value (DF > 0). Thus, leucogranite is S-type granite generated from metasedimentary rocks. Major and trace element diagram ($R_1-R_2$ diagram and Rb vs. Y+Nb etc.) show collisional environment such as syn-collisional or volcanic arc granite. Because Rb/sr ratio (1.8-22.9) of leucogranites is higher than Sr/Ba ratio (0.21-0.79), leucogranite would be derived from muscovite dehydrate melting in metasedimentary rocks. Leucogranites have lower concentration of LREE and Eu and similar that of HREE relative to metasedimentary rocks. To examine difference of REEs between leucogranites and metasedimentary rocks, we perform modeling using volume percentage of a leucogranite and a metasedimenatry rock from study area and REE data of minerals from rhyolite (Nash and Crecraft, 1985) and melanosome of migmatite (Bea et al., 1994). Resultants of modeling indicate that LREE and HREE are controlled by monazites and garnet, respectively, although zircon is estimated HREE dominant in some leucogranite without garnet. Because there are many inclusions of accessary phases such as monazite and zircon in biotites from metasedimentary rocks. leucogranitic magma was mainly derived from muscovite-breakdown in metasedimenary rocks. Leucogranites can be subdivided into two types in compliance with Eu anomaly of chondrite nomalized REE pattern; the one of negative Eu anomaly is type I and the other is type II. Leucogranites have lower Eu concetnrations than that of metasedimenary rocks and similar that of both type. REE modeling suggest that this difference of Eu value is due to that of components of feldspars in both leucogranite and metasedimentary rock. The tendency of major ($K_2O$ and $Na_2O$) and face elements (Eu, Rb, Sr and Ba) of leucogranites also indicate that source magma of these two types was developed by anatexis experienced strong fractionation of alkali-feldspar. Conclusionally, leucogranites in this area are products of melts which was generated by muscovite-breakdown of metasedimenary rock in environment of continetal collision during high temperature/pressure metamorphism and then was fractionated and crystallized after extraction from source rock.
Kim, Jungjoo;Kim, Kyoungyul;Ryu, Heehwan;Hwan, Jung Ju;Hong, Sungyun;Jo, Seonah;Bae, Dusan
KEPCO Journal on Electric Power and Energy
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v.6
no.3
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pp.305-313
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2020
The use of cable tunnels for electric power transmission as well as their construction in difficult conditions such as in subsea terrains and large overburden areas has increased. So, in order to efficiently operate the small diameter shield TBM (Tunnel Boring Machine), the estimation of advance rate and development of a design model is necessary. However, due to limited scope of survey and face mapping, it is very difficult to match the rock mass characteristics and TBM operational data in order to achieve their mutual relationships and to develop an advance rate model. Also, the working mechanism of previously utilized linear cutting machine is slightly different than the real excavation mechanism owing to the penetration of a number of disc cutters taking place at the same time in the rock mass in conjunction with rotation of the cutterhead. So, in order to suggest the advance rate and machine design models for small diameter TBMs, an EPB (Earth Pressure Balance) shield TBM having 3.54 m diameter cutterhead was manufactured and 19 cases of full-scale tunneling tests were performed each in 87.5 ㎥ volume of artificial rock mass. The relationships between advance rate and machine data were effectively analyzed by performing the tests in homogeneous rock mass with 70 MPa uniaxial compressive strength according to the TBM operational parameters such as thrust force and RPM of cutterhead. The utilization of the recorded penetration depth and torque values in the development of models is more accurate and realistic since they were derived through real excavation mechanism. The relationships between normal force on single disc cutter and penetration depth as well as between normal force and rolling force were suggested in this study. The prediction of advance rate and design of TBM can be performed in rock mass having 70 MPa strength using these relationships. An effort was made to improve the application of the developed model by applying the FPI (Field Penetration Index) concept which can overcome the limitation of 100% RQD (Rock Quality Designation) in artificial rock mass.
With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.
Internet commerce has been growing at a rapid pace for the last decade. Many firms try to reach wider consumer markets by adding the Internet channel to the existing traditional channels. Despite the various benefits of the Internet channel, a significant number of firms failed in managing the new type of channel. Previous studies could not cleary explain these conflicting results associated with the Internet channel. One of the major reasons is most of the previous studies conducted analyses under a specific market condition and claimed that as the impact of Internet channel introduction. Therefore, their results are strongly influenced by the specific market settings. However, firms face various market conditions in the real worlddensity and disutility of using the Internet. The purpose of this study is to investigate the impact of various market environments on a firm's optimal channel strategy by employing a flexible game theory model. We capture various market conditions with consumer density and disutility of using the Internet.
shows the channel structures analyzed in this study. Before the Internet channel is introduced, a monopoly manufacturer sells its products through an independent physical store. From this structure, the manufacturer could introduce its own Internet channel (MI). The independent physical store could also introduce its own Internet channel and coordinate it with the existing physical store (RI). An independent Internet retailer such as Amazon could enter this market (II). In this case, two types of independent retailers compete with each other. In this model, consumers are uniformly distributed on the two dimensional space. Consumer heterogeneity is captured by a consumer's geographical location (ci) and his disutility of using the Internet channel (${\delta}_{N_i}$).
shows various market conditions captured by the two consumer heterogeneities.
(a) illustrates a market with symmetric consumer distributions. The model captures explicitly the asymmetric distributions of consumer disutility in a market as well. In a market like that is represented in
(c), the average consumer disutility of using an Internet store is relatively smaller than that of using a physical store. For example, this case represents the market in which 1) the product is suitable for Internet transactions (e.g., books) or 2) the level of E-Commerce readiness is high such as in Denmark or Finland. On the other hand, the average consumer disutility when using an Internet store is relatively greater than that of using a physical store in a market like (b). Countries like Ukraine and Bulgaria, or the market for "experience goods" such as shoes, could be examples of this market condition.
summarizes the various scenarios of consumer distributions analyzed in this study. The range for disutility of using the Internet (${\delta}_{N_i}$) is held constant, while the range of consumer distribution (${\chi}_i$) varies from -25 to 25, from -50 to 50, from -100 to 100, from -150 to 150, and from -200 to 200.
summarizes the analysis results. As the average travel cost in a market decreases while the average disutility of Internet use remains the same, average retail price, total quantity sold, physical store profit, monopoly manufacturer profit, and thus, total channel profit increase. On the other hand, the quantity sold through the Internet and the profit of the Internet store decrease with a decreasing average travel cost relative to the average disutility of Internet use. We find that a channel that has an advantage over the other kind of channel serves a larger portion of the market. In a market with a high average travel cost, in which the Internet store has a relative advantage over the physical store, for example, the Internet store becomes a mass-retailer serving a larger portion of the market. This result implies that the Internet becomes a more significant distribution channel in those markets characterized by greater geographical dispersion of buyers, or as consumers become more proficient in Internet usage. The results indicate that the degree of price discrimination also varies depending on the distribution of consumer disutility in a market. The manufacturer in a market in which the average travel cost is higher than the average disutility of using the Internet has a stronger incentive for price discrimination than the manufacturer in a market where the average travel cost is relatively lower. We also find that the manufacturer has a stronger incentive to maintain a high price level when the average travel cost in a market is relatively low. Additionally, the retail competition effect due to Internet channel introduction strengthens as average travel cost in a market decreases. This result indicates that a manufacturer's channel power relative to that of the independent physical retailer becomes stronger with a decreasing average travel cost. This implication is counter-intuitive, because it is widely believed that the negative impact of Internet channel introduction on a competing physical retailer is more significant in a market like Russia, where consumers are more geographically dispersed, than in a market like Hong Kong, that has a condensed geographic distribution of consumers.