• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.117-133
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• 2021

The art of the Confucian literati' was mainly centered on literati' painting. Literary paintings combined with poem-writing-painting are based on the literary characters and studies. This is usually based on the technique of Shensi(神似) caused by a hobby accomplishment, enjoying the chinese painting and cherishing with chinese painting. The Confucian literati' cultivated their own character and sublimated humanism to art based on studying. They sought the life of supremacy and supreme gentleness, and enjoyed life on the boundary of pleasure through art. The aim of the Confucian literati' arts lies in the pursuit of expressing the artist's inner world, spirit, and the combination of the Confucian and the Taoism, Because of literati's spirit based on learning, the Confucian literati' arts still exist. The aesthetic of Sunbi Spirit is mainly in the customs of Sunbi, the loyalty of Sunbi, the Silhak(實學) of Sunbi, and loving of the people of Sunbi. We can find honor and loyalty in the Sunbi spirit of the Confucian literati' of the Joseon Dynasty. In addition, it is also possible to observe the loyal troops, the hard work for the country, and the Pung-ryu with nature. In other words, the Sunbi honor, loyalty, loyal troops, pursuit of study and the Pung-ryu show the spirit of the Confucian literati' of Joseon Dynasty. The aesthetic of the Sunbi spirit is in Pung-ryu, loyalty, Silhak, loving of the people etc. The aesthetic of experience of art is mainly based on the aesthetic experience by emotional intelligence and the aesthetic experience according to the individual's inclination. The aesthetic sense actually shows Pung-ryu, loyalty, Silhak and love etc. We can see it in many of our literary paintings. Therefore, the Confucian literati' painting in Chosun Dynasty were the intentionality of the mind and the intentionality of the spare. Furthermore, it has directivity of expressing the artist's inner world, directivity of substance, so it is possible to see that the characteristics of the Sunbi spirit are diverse.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.2
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• pp.111-123
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• 2022

Carbonate green rust (CGR) and sulfate green rust (SGR) commonly occur in nature. In this study, CGR and SGR were synthesized through co-precipitation, and their formation mechanisms and physicochemical properties were investigated. X-ray diffraction (XRD) and Rietveld refinement showed both CGR and SGR with layered double hydroxide structure were successfully synthesized without any secondary phases under each synthetic condition. Refined structural parameters (unit cell) for two green rusts were a (=b) = 3.17 Å and c = 22.52 Å for CGR and a (=b) = 5.50 Å and c = 10.97 Å for SGR with the crystallite size 57.8 nm in diameter from (003) reflection and 40.1 nm from (001) reflections, respectively. Scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDS) results showed that both CGR and SGR had typical hexagonal plate-like crystal morphologies but their chemical composition is different in the content of C and S. In addition, Fourier transform infrared (FT-IR) spectroscopy analysis revealed that carbonate (CO₃^2-) and sulfate (SO₄^2-) molecules were occupied as interlayer anions of CGR and SGR, respectively. These SEM/EDS and FT-IR results were in good agreement with XRD results. Changes in the solution chemistry (i.e., pH, Eh and residual iron concentrations (Fe(II):Fe(III)) of the mixed solution) were observed as a function of the injection time of hydroxyl ion (OH^-) into the iron solution. Three different stages were observed in the formation of both CGR and SGR; precursor, intermediator, and green rust in the formation of both CGR and SGR. This study provides co-precipitation methods for CGR and SGR in a way of the stable synthesis. In addition, our findings for the formation mechanisms of the two green rusts and their physicochemical properties will provide crucial information with researches and industrials in utilizing green rust.

• 한국노년학
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• v.29 no.4
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• pp.1539-1557
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• 2009

The purpose of this study was to analyze the effects of program's participation, self-esteem and depression on quality of life for the elderly participating social welfare using facilities. Therefore, this paper will help improve a life's quality of all the olds by fixing the problems we got in this society and suggesting the better program for the elderly participating social welfare using facilities. The results of the study were as follows. There was no specific difference in statistic that shows the quality of the old's life who have their economic life. In quality of life in neighbors, the male were more friendly to their neighbors and the lower level of education, the higher quality of life. Healthy old people had greater quality of life than unhealthy old people did. If the old were excellent in scholarship, if unmarried couple shared the same house, if both of husband and wife live together when they are married, and if their allowance are full in pocket, the quality of their life was enhanced. Self-esteem influenced the quality of life in economic, but depression influenced negatively. In quality of life in neighbors, feeling depression and joining in program of a work for prosperity in future didn't effect doing well with the neighbors. On the other hand, participating in the social community program effected positively. In quality of life in neighbors, feeling depression and program of rehabilitation effected on the quality of life negatively. Educating refinement program effected positively being a good match, but promoting health program effected negatively.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.258-266
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• 2023

This study analyzed methodologies for estimating carbon stocks of perennial woody crops and the research cases in overseas countries. As a result, we found that Australia, Bulgaria, Canada, and Japan are using the stock-difference method, while Austria, Denmark, and Germany are estimating the change in the carbon stock based on the gain-loss method. In some overseas countries, the researches were conducted on estimating the carbon stock change using image data as tier 3 phase beyond the research developing country-specific factors as tier 2 phase. In South Korea, convergence studies as the third stage were conducted in forestry field, but advanced research in the agricultural field is at the beginning stage. Based on these results, we suggest directions for the following four future researches: 1) securing national-specific factors related to emissions and removals in the agricultural field through the development of allometric equation and carbon conversion factors for perennial woody crops to improve the completeness of emission and removals statistics, 2) implementing policy studies on the cultivation area calculation refinement with fruit tree-biomass-based maturity, 3) developing a more advanced estimation technique for perennial woody crops in the agricultural sector using allometric equation and remote sensing techniques based on the agricultural and forestry satellite scheduled to be launched in 2025, and to establish a matrix and monitoring system for perennial woody crop cultivation areas in the agricultural sector, Lastly, 4) estimating soil carbon stocks change, which is currently estimated by treating all agricultural areas as one, by sub-land classification to implement a dynamic carbon cycle model. This study suggests a detailed guideline and advanced methods of carbon stock change calculation for perennial woody crops, which supports 2050 Carbon Neutral Strategy of Ministry of Agriculture, Food, and Rural Affairs and activate related research in agricultural sector.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Chest Surgery
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• v.35 no.9
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• pp.643-652
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• 2002

As the prevalence of coronay artery disease is increasing, the surgical treatment has been universalized and operative outcome has been improved. We analyzed the short and mid-term results of 292 CABGs performed in Kangdong Sacred Heart Hospital. Material and Method: From June 1994 to December 2001, 292 patients underwent coronary artery bypass grafting. There were 173 men and 119 women and their ages ranged from 39 to 84 years with a mean of $61.8{\pm}9.1$ years. We analyzed the preoperative risk factors, operative procedures and operative outcome. In addition, we analyzed the recurrence of symptoms, long-term mortality and complications via out-patient follow-up for discharged patients. Result: Preoperative clinical diagnoses were unstable angina in 137(46.9%), stable angina in 34(11.6%), acute myocardial infarction in 40(13.7%), non-Q myocardial infarction in 25(8.6%), postinfarction angina in 22(7.5%), cardiogenic shock in 30(10.3%) and PTCA failure in 4(1.4%) patients. Preoperative angiographic diagnoses were three-vessel disease in 157(53.8%), two-vessel disease in 35 (12.0%), one-vessel disease in 11(3.8%) and left main disease in 89(30.5%) patients. We used saphenous veins in 630, internal thoracic arteries in 257, radial arteries in 50, and right gastoepiploic arteries in 2 distal anastomoses. The mean number of distal anastomoses per patient was $3.2{\pm}1.0$ There were 18 concomitant procedures ; valve replacement in 8(2.7%), left main coronary artery angioplasty in 6(2.1%), patch closure of postinfarction ventricular septal defect(PMI-VSD) in 2(0.7%), replacement of ascending aorta in 1(0.3%) and coronary endarterectomy in 1(0.3%) patient. The mean ACC time was $96.6{\pm}35.3 $ minutes and the mean CPB time was $179.2{\pm}94.6$ minutes. Total early mortality was 8.6%, but it was 3.1% in elective operations. The most common cause of early mortality was low cardiac output syndrome in 6(2.1%) patients. The stastistically significant risk factors for early mortality were hypertension, old age($\geq$ 70 years), poor LV function(EF＜40%), congestive heart failure, preoperative intraaortic balloon pump, emergency operation and chronic renal failure. The most common complication was arrhythmia in 52(17.8%) patients. The mean follow-up period was $39.0{\pm}27.0$ months. Most patients were free of symptoms during follow-up. Fourteen patients(5.8 %) had recurrent symptoms and 7 patients(2.9%) died during follow-up period. Follow-up coronary angiography was performed in 13 patients with recurrent symptoms and they were managed by surgical and medical treatment according to the coronary angiographic result. Conclusion: The operative and late results of CABG in our hospital, was acceptable. However, There should be more refinement in operative technique and postoperative management to improve the results.

• Economic and Environmental Geology
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• v.25 no.3
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• pp.337-349
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• 1992

The Tertiary basins in Korea have widely been studied by numerous researchers producing individual results in sedimentology, paleontology, stratigraphy, volcanic petrology and structural geology, but interdisciplinary studies, inter-basin analysis and basin-forming process have not been carried out yet. Major work of this study is to elucidate evidences obtained from different parts of a basin as well as different Tertiary basins (Pohang, Changgi, Eoil, Haseo and Ulsan basins) in order to build up the correlation between the basins, and an overall picture of the basin architecture and evolution in Korea. According to the paleontologic evidences the geologic age of the Pohang marine basin is dated to be late Lower Miocence to Middle Miocene, whereas other non-marine basins are older as being either Early Miocene or Oligocene(Lee, 1975, 1978: Bong, 1984: Chun, 1982: Choi et al., 1984: Yun et al., 1990: Yoon, 1982). However, detailed ages of the Tertiary sediments, and their correlations in a basin and between basins are still controversial, since the basins are separated from each other, sedimentary sequence is disturbed and intruded by voncanic rocks, and non-marine sediments are not fossiliferous to be correlated. Therefore, in this work radiometric, magnetostratigraphic, and biostratigraphic data was integrated for the refinement of chronostratigraphy and synopsis of stratigraphy of Tertiary basins of Korea. A total of 21 samples including 10 basaltic, 2 porphyritic, and 9 andesitic rocks from 4 basins were collected for the K-Ar dating of whole rock method. The obtained age can be grouped as follows: $14.8{\pm}0.4{\sim}15.2{\pm}0.4Ma$, $19.9{\pm}0.5{\sim}22.1{\pm}0.7Ma$, $18.0{\pm}1.1{\sim}20.4+0.5Ma$, and $14.6{\pm}0.7{\sim}21.1{\pm}0.5Ma$. Stratigraphically they mostly fall into the range of Lower Miocene to Mid Miocene. The oldest volcanic rock recorded is a basalt (911213-6) with the age of $22.05{\pm}0.67Ma$ near Sangjeong-ri in the Changgi (or Janggi) basin and presumed to be formed in the Early Miocene, when Changgi Conglomerate began to deposit. The youngest one (911214-9) is a basalt of $14.64{\pm}0.66Ma$ in the Haseo basin. This means the intrusive and extrusive rocks are not a product of sudden voncanic activity of short duration as previously accepted but of successive processes lasting relatively long period of 8 or 9 Ma. The radiometric age of the volcanic rocks is not randomly distributed but varies systematically with basins and localities. It becomes generlly younger to the south, namely from the Changgi basin to the Haseo basin. The rocks in the Changgi basin are dated to be from $19.92{\pm}0.47$ to $22.05{\pm}0.67Ma$. With exception of only one locality in the Geumgwangdong they all formed before 20 Ma B.P. The Eoil basalt by Tateiwa in the Eoil basin are dated to be from $20.44{\pm}0.47$ to $18.35{\pm}0.62Ma$ and they are younger than those in the Changgi basin by 2~4 Ma. Specifically, basaltic rocks in the sedimentary and voncanic sequences of the Eoil basin can be well compared to the sequence of associated sedimentary rocks. Generally they become younger to the stratigraphically upper part. Among the basin, the Haseo basin is characterized by the youngest volcanic rocks. The basalt (911214-7) which crops out in Jeongja-ri, Gangdong-myon, Ulsan-gun is $16.22{\pm}0.75Ma$ and the other one (911214-9) in coastal area, Jujon-dong, Ulsan is $14.64{\pm}0.66Ma$ old. The radiometric data are positively collaborated with the results of paleomagnetic study, pull-apart basin model and East Sea spreading theory. Especially, the successively changing age of Eoil basalts are in accordance with successively changing degree of rotation. In detail, following results are discussed. Firstly, the porphyritic rocks previously known as Cretaceous basement (911213-2, 911214-1) show the age of $43.73{\pm}1.05$ 및 $49.58{\pm}1.13Ma$(Eocene) confirms the results of Jin et al. (1988). This means sequential volcanic activity from Cretaceous up to Lower Tertiary. Secondly, intrusive andesitic rocks in the Pohang basin, which are dated to be $21.8{\pm}2.8Ma$ (Jin et al., 1988) are found out to be 15 Ma old in coincindence with the age of host strata of 16.5 Ma. Thirdly, The Quaternary basalt (911213-5 and 911213-6) of Tateiwa(1924) is not homogeneous regarding formation age and petrological characteristics. The basalt in the Changgi basin show the age of $19.92{\pm}0.47$ and $22.05{\pm}0.67$ (Miocene). The basalt (911213-8) in Sangjond-ri, which intruded Nultaeri Trachytic Tuff is dated to be $20.55{\pm}0.50Ma$, which means Changgi Group is older than this age. The Yeonil Basalt, which Tateiwa described as Quaternary one shows different age ranging from Lower Miocene to Upper Miocene(cf. Jin et al., 1988: sample no. 93-33: $10.20{\pm}0.30Ma$). Therefore, the Yeonil Quarterary basalt should be revised and divided into different geologic epochs. Fourthly, Yeonil basalt of Tateiwa (1926) in the Eoil basin is correlated to the Yeonil basalt in the Changgi basin. Yoon (1989) intergrated both basalts as Eoil basaltic andesitic volcanic rocks or Eoil basalt (Yoon et al., 1991), and placed uppermost unit of the Changgi Group. As mentioned above the so-called Quarternary basalt in the Eoil basin are not extruded or intruaed simultaneously, but differentiatedly (14 Ma~25 Ma) so that they can not be classified as one unit. Fifthly, the Yongdong-ri formation of the Pomgogri Group is intruded by the Eoil basalt (911214-3) of 18.35~0.62 Ma age. Therefore, the deposition of the Pomgogri Group is completed before this age. Referring petrological characteristics, occurences, paleomagnetic data, and relationship to other Eoil basalts, it is most provable that this basalt is younger than two others. That means the Pomgogri Group is underlain by the Changgi Group. Sixthly, mineral composition of the basalts and andesitic rocks from the 4 basins show different ground mass and phenocryst. In volcanic rocks in the Pohang basin, phenocrysts are pyroxene and a small amount of biotite. Those of the Changgi basin is predominant by Labradorite, in the Eoil by bytownite-anorthite and a small amount pyroxene.