• Journal of radiological science and technology
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• v.32 no.3
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• pp.313-323
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• 2009

Diffusion-weighted imaging (DWI) has been demonstrated to be a practical method for the diagnosis of various brain diseases such as acute infarction, brain tumor, and white matter disease. In this study, we used two techniques to examine the average signal intensity (SI) and apparent diffusion coefficient (ADC) of the brains of patients who ranged in age from 10 to 60 years. Our results indicated that the average SI was the highest in amygdala (as derived from DWI), whereas that in the cerebrospinal fluid was the lowest. The average ADC was the highest in the cerebrospinal fluid, whereas the lowest measurement was derived from the pons. The average SI and ADC were higher in $T_2^*$-DW-EPI than in FLAIR-DW-EPI. The higher the b-value, the smaller the average difference in both imaging techniques; the lower the b-value, the greater the average difference. Also, comparative analysis of the brains of patients who had experienced cerebral infarction showed no distinct lesion in the general MR image over time. However, there was a high SI in apparent weighted images. Analysis of other brain diseases (e.g., bleeding, acute, subacute, chronic infarction) indicated SI variance in accordance with characteristics of the two techniques. The higher the SI, the lower the ADC. Taken together, the value of SI and ADC in accordance with frequently occurring areas and various brain disease varies based on the b-value and imaging technique. Because they provide additional useful information in the diagnosis and treatment of patients with various brain diseases through signal recognition, the proper imaging technique and b-value are important for the detection and interpretation of subacute stroke and other brain diseases.

• Korean journal of food and cookery science
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• v.28 no.4
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• pp.413-421
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• 2012

This study was conducted with 418 adults 20 years or older, all of whom lived in Daegu and Gyeongbuk. According to a survey, the number of females was greater than that of males (40.7%) by 59.3%, and that for ages 30 years was the highest. The preference for Korean traditional drinks was relatively high at 51.8%, and the frequency of drinking Korean traditional drinks was 39.0%. The adults answered that they had these beverages on special days such as holidays, ritual days, and birthdays. Among the reasons for drinking a traditional beverage 'good taste' scored the highest with 27.0% of respondents, followed by 'Korean traditional food' with 24.4%. The recognition of Korean traditional drinks was high in the order of Sikhe, Soojunggwa, Cha, and Hwachae. The preference for Sikhe was the highest. The group who agreed that it was important to develop a Korean functional traditional drink was 11.5% higher than that of the negative group, as 13.4% 'agreed a lot' and 41.1% 'agreed'. Consumer awareness toward traditional drink functionality was generally positive, with 3.5 points or higher on average, and awareness of the nutritional supplementation, diabetes control, the recuperative effects of the drinks were also high. Among Korean traditional drinks Sikhe was the highest with regard to intention to purchase. As a result, the popularization of traditional Korean drinks was based on three factors: quality oriented image, popularity oriented image, and product attribute-oriented image. These factors significantly influenced the preference for and purchase of Korean functional traditional drinks.

• 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.