• Journal of the Korean earth science society
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• v.45 no.2
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• pp.136-146
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• 2024

This paper introduces the seismic facies classification and mapping of igneous bodies found in the sedimentary sequences of the Yellow Sea shelf area of Korea. In the research area, six extrusive and three intrusive types of igneous bodies were found in the Late Cretaceous, Eocene, Early Miocene, and Quaternary sedimentary sequences of the northeastern, southwestern and southeastern sags of the Gunsan Basin. Extrusive igneous bodies include the following six facies: (1) monogenetic volcano (E.mono) showing cone-shape external geometry with height less than 200 m, which may have originated from a single monogenetic eruption; (2) complex volcano (E.comp) marked by clustered monogenetic cones with height less than 500 m; (3) stratovolcano (E.strato) referring to internally stratified lofty volcanic edifices with height greater than 1 km and diameter more than 15 km; (4) fissure volcanics (E.fissure) marked by high-amplitude and discontinuous reflectors in association with normal faults that cut the acoustic basement; (5) maar-diatreme (E.maar) referring to gentle-sloped low-profile volcanic edifices with less than 2 km-wide vent-shape zones inside; and (6) hydrothermal vents (E.vent) marked by upright pipe-shape or funnel-shape structures disturbing sedimentary sequence with diameter less than 2 km. Intrusive igneous bodies include the following three facies: (1) dike and sill (I.dike/sill) showing variable horizontal, step-wise, or saucer-shaped intrusive geometries; (2) stock (I.stock) marked by pillar- or horn-shaped bodies with a kilometer-wide intrusion diameter; and (3) batholith and laccoliths (I.batho/lac) which refer to gigantic intrusive bodies that broadly deformed the overlying sedimentary sequence.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• The Journal of the Petrological Society of Korea
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• v.21 no.2
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• pp.129-150
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• 2012

The tectonic evolution of the Central Ogcheon Belt has been newly analyzed in this paper from the detailed geological maps by lithofacies classification, the development processes of geological structures, microstructures, and the time-relationship between deformation and metamorphism in the Ogcheon, Cheongsan, Mungyeong Buunnyeong, Busan areas, Korea and the fossil and radiometric age data of the Ogcheon Supergroup(OSG). The 1st tectonic phase($D^*$) is marked by the rifting of the original Gyeonggi Massif into North Gyeonggi Massif(present Gyeonggi Massif) and South Gyeonggi Massif (Bakdallyeong and Busan gneiss complexes). The Joseon Supergroup(JSG) and the lower unit(quartzose psammitic, pelitic, calcareous and basic rocks) of OSG were deposited in the Ogcheon rift basin during Early Paleozoic time, and the Pyeongan Supergroup(PSG) and its upper unit(conglomerate and pelitic rocks and acidic rocks) appeared in Late Paleozoic time. The 2nd tectonic phase(Ogcheon-Cheongsan phase/Songnim orogeny: D1), which occurred during Late Permian-Middle Triassic age, is characterized by the closing of Ogcheon rift basin(= the coupling of the North and South Gyeonggi Massifs) in the earlier phase(Ogcheon subphase: D1a), and by the coupling of South China block(Gyeonggi Massif and Ogcheon Zone) and North China block(Yeongnam Massif and Taebaksan Zone) in the later phase(Cheongsan subphase: D1b). At the earlier stage of D1a occurred the M1 medium-pressure type metamorphism of OSG related to the growth of coarse biotites, garnets, staurolites. At its later stage, the medium-pressure type metamorphic rocks were exhumed as some nappes with SE-vergence, and the giant-scale sheath fold, regional foliation, stretching lineation were formed in the OSG. At the D1b subphase which occurs under (N)NE-(S)SW compression, the thrusts with NNE- or/and SSW-vergence were formed in the front and rear parts of couple, and the NNE-trending Cheongsan shear zone of dextral strike-slip and the NNE-trending upright folds of the JSG and PSG were also formed in its flank part, and Daedong basin was built in Korean Peninsula. After that, Daedong Group(DG) of the Late Triassic-Early Jurassic was deposited. The 3rd tectonic phase(Honam phase/Daebo orogeny: D2) occurred by the transpression tectonics of NNE-trending Honam dextral strike-slip shearing in Early~Late Jurassic time, and formed the asymmetric crenulated fold in the OSG and the NNE-trending recumbent folds in the JSG and PSG and the thrust faults with ESE-vergence in which pre-Late Triassic Supergroups override DG. The M2 contact metamorphism of andalusite-sillimanite type by the intrusion of Daebo granitoids occurred at the D2 intertectonic phase of Middle Jurassic age. The 4th tectonic phase(Cheongmari phase: D3) occurred under the N-S compression at Early Cretaceous time, and formed the pull-apart Cretaceous sedimentary basins accompanying the NNE-trending sinistral strike-slip shearing. The M3 retrograde metamorphism of OSG associated with the crystallization of chlorite porphyroblasts mainly occurred after the D2. After the D3, the sinistral displacement(Geumgang phase: D4) occurred along the Geumgang fault accompanied with the giant-scale Geumgang drag fold with its parasitic kink folds in the Ogcheon area. These folds are intruded by acidic dykes of Late Cretaceous age.