• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of Global Scholars of Marketing Science
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• v.17 no.4
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• pp.141-173
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• 2007

The study examines how the environmental factors of store influence service brand personality and repurchase intention in the service environment. The service industry has been experiencing the intensified competition with the industry's continuous growth and the influence from rapid technological advancement. Under the circumstances, it has become ever more important for the brand competitiveness to be distinctively recognized against competition. A brand needs to be distinguished and differentiated from competing companies because they are all engaged in the similar environment of the service industry. The differentiation of brand achievement has become increasingly important to highlight certain brand functions to include emotional, self-expressive, and symbolic functions since the importance of such functions has been further emphasized in promoting consumption activities. That is the recent role of brand personality that has been emphasized in the service industry. In other words, customers now freely and actively express their personalities or egos in consumption activities, taking an important role in construction of a brand asset. Hence, the study suggests that it is necessary to disperse the recognition and acknowledgement that the maintenance of the existing customers contributes more to boost repurchase intention when it is compared to the efforts to create new customers, particularly in the service industry. Meanwhile, the store itself can offer a unique environment that may influence the consumer's purchase decision. Consumers interact with store environments in the process of,virtually, all household purchase they make (Sarel 1981). Thus, store environments may encourage customers to purchase. The roles that store environments play are to provide informational cues to customers about the store and goods and communicate messages to stimulate consumers' emotions. The store environments differentiate the store from competing stores and build a unique service brand personality. However, the existing studies related to brand in the service industry mostly concentrated on the relationship between the quality of service and customer satisfaction, and they are mostly generalized while the connective studies focused on brand personality. Such approaches show limitations and are insufficient to investigate on the relationship between store environment and brand personality in the service industry. Accordingly, the study intends to identify the level of contribution to the establishment of brand personality made by the store's physical environments that influence on the specific brand characteristics depending on the type of service. The study also intends to identify what kind of relationships with brand personality exists with brand personality while being influenced by store environments. In addition, the study intends to make meaningful suggestions to better direct marketing efforts by identifying whether a brand personality makes a positive influence to induce an intention for repurchase. For this study, the service industry is classified into four categories based on to the characteristics of service: experimental-emotional service, emotional -credible service, credible-functional service, and functional-experimental service. The type of business with the most frequent customer contact is determined for each service type and the enterprise with the highest brand value in each service sector based on the report made by the Korea Management Association. They are designated as the representative of each category. The selected representatives are a fast-food store (experimental-emotional service), a cinema house (emotional-credible service), a bank (credible-functional service), and discount store (functional-experimental service). The survey was conducted for the four selected brands to represent each service category among consumers who are experienced users of the designated stores in Seoul Metropolitan City and Gyeonggi province via written questionnaires in order to verify the suggested assumptions in the study. In particular, the survey adopted 15 scales, which represent each characteristic factor, among the 42 unique characteristics developed by Jennifer Aaker(1997) to assess the brand personality of each service brand. SPSS for Windows Release 12.0 and LISREL were used in the analysis of data verification. The methodology of the structural equation model was used for the study and the pivotal findings are as follows. 1) The environmental factors ware classified as design factors, ambient factors, and social factors. Therefore, the validity of measurement scale of Baker et al. (1994) was proved. 2) The service brand personalities were subdivided as sincerity, excitement, competence, sophistication, and ruggedness, which makes the use of the brand personality scales by Jennifer Aaker(1997) appropriate in the service industry as well. 3) One-way ANOVA analysis on the scales of store environment and service brand personality showed that there exist statistically significant differences in each service category. For example, the social factors were highest in discount stores, while the ambient factors and design factors were highest in fast-food stores. The discount stores were highest in the sincerity and excitement, while the highest point for banks was in the competence and ruggedness, and the highest point for fast-food stores was in the sophistication, The consumers will make a different respond to the physical environment of stores and service brand personality that are inherent to the corresponding service interface. Hence, the customers will make a different decision-making when dealing with different service categories. In this aspect, the relationships of variables in the proposed hypothesis appear to work in a different way depending on the exposed service category. 4) The store environment factors influenced on service brand personalities differently by category of service. The factors of store's physical environment are transferred to a brand and were verified to strengthen service brand personalities. In particular, the level of influence on the service brand personality by physical environment differs depending on service category or dimension, which indicates that there is a need to apply a different style of management to a different service category or dimension. It signifies that there needs to be a brand strategy established in order to positively influence the relationship with consumers by utilizing an appropriate brand personality factor depending on different characteristics by service category or dimension. 5) The service brand personalities influenced on the repurchase intention. Especially, the largest influence was made in the sophistication dimension of service brand personality scale; the unique and characteristically appropriate arrangement of physical environment will make customers stay in the service environment for a long time and will lead to give a positive influence on the repurchase intention. 6) The store environment factors influenced on the repurchase intention. Particularly, the largest influence was made on the social factors of store environment. The most intriguing finding is that the service factor among all other environment factors gives the biggest influence to the repurchase intention in most of all service types except fast-food stores. Such result indicates that the customers pay attention to how much the employees try to provide a quality service when they make an evaluation on the service brand. At the same time, it also indicates that the personal factor is directly transmitted to the construction of brand personality. The employees' attitude and behavior are the determinants to establish a service brand personality in the process of enhancing service interface. Hence, there should be a reinforced search for a method to efficiently manage the service staff who has a direct contact with customers in order to make an affirmative improvement of the customers' brand evaluation at the service interface. The findings suggest several managerial implications. 1) Results from the empirical study indicated that store environment factors have a strong positive impact on a service brand personality. To increase customers' repurchase intention of a service brand, the management is required to effectively manage store environment factors and create a friendly brand personality based on the corresponding service environment. 2) Mangers and researchers must understand and recognize that the store environment elements are important marketing tools, and that brand personality influences on consumers' repurchase intention. Based on such result of the study, a service brand could be utilized as an efficient measure to achieve a differentiation by enforcing the elements that are most influential among all other store environments for each service category. Therefore, brand personality established involving various store environments will further reinforce the relationship with customers through the elevated brand identification of which utilization to induce repurchase decision can be used as an entry barrier. 3) The study identified the store environment as a component of service brand personality for the store's effective communication with consumers. For this, all communication channels should be maintained with consistency and an integrated marketing communication should be executed to efficiently approach to a larger number of customers. Mangers and researchers must find strategies for aligning decisions about store environment elements with the retailers' marketing and store personality objectives. All ambient, design, and social factors need to be orchestrated so that consumers can take an appropriate store personality. In this study, the induced results from the previous studies were extended to the service industry so as to identify the customers' decision making process that leads to repurchase intention and a result similar to those of the previous studies. The findings suggested several theoretical and managerial implications. However, the situation that only one service brand served as the subject of analysis for each service category, and the situation that correlations among store environment elements were not identified, as well as the problem of representation in selection of samples should be considered and supplemented in the future when further studies are conducted. In addition, various antecedents and consequences of brand personality must be looked at in the aspect of the service environment for further research.