• 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.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.1
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• pp.28-37
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• 2011

In this study, the series-poem, Bihaedangsasippalyoung(48 poems for beautiful scene of Bihaedang), written by scholars of Jiphyonjeon for Bihaedang garden of Anpyung-Daegun(Prince Anpyung, 1416-1453), was analyzed focusing on scenery lexeme to interpret the meaning of scenery and gardening culture of Sadaebu(noblemen) during the first term of Chosun Dynasty. The study result is as followings. First, the subtitle of Sasippalyoung(48 poems) written by Anpyung-Daegun while he grew Bihaedang garden on the foot of Inwang Mountain showed repetitive nomativity comparing joining of yin and yang, such as life and form of animal and plan, time and space, meaning and symbolism, etc. Among scenery lexemes, 38 are represented plant and flowers, and 8 are represented gardening ornaments and animals. Second, the names of gardens were expressed as Wonrim, Jongje, Imchon(Trees and Ponds), or Hwawon(Flower garden), or also presented as Gongjeong(Empty garden), Manwon(Full garden), Jungjeong(Middle garden), Huwon(Backyard), Wonrak(Inner court), or Byulwon(Seperated garden) depending on density and location. In addition, there were pavilions and ponds, stepping stones and stairs, a pergola, a flat bench, flowerpots, an artificial hill, oddly shaped stones, wells, aviary, flower beds, or hedges. A gardener was called Sahwa(flower keeper), planting and gardening of garden trees were called Jaebae(cultivation), a pond island was called Boogoo(floating hill), and miniature landscapes were called Chukjee(reduced land). Third, willows were planted on the outdoor yard, and plum trees were planted in front of the library, which led to bamboo woods road. Peony, camellia, tree peony and crepe myrtle were planted on the inner court with mossy rocks, small artificial hills, glass rocks, flower pots. There were rectangular ponds, while breeding deer, dove, rooster, and cranes. Fourth, landscape elements were enjoyed as metaphysical symbolic landscape by anthropomorphism, such as (1) gentlemen and loyalty, (2) wealth and prosperity, (3) Taoist hermit and poetical life, (4) reclusion and seclusion, (5) filial piety, virtue, introspection, etc. In other words, the garden presented a variety of gardening culture appreciating meaningful landscape, such as investigation of things, reclusion and seclusion, and building orientation of a fairyland yearning eternal youth and Mureungdowon(Taoist Arcadia) by making a garden blending beautiful flowers and trees, with precious birds and animals. Fifth, there were many landscape appreciation schemes, such as Angkyung(looking-up), Bukyung(looking-down), Jeokyung(looking-under), Chakyung(bringing outer space into inside), Yookyung(flower viewing), Yojeong(walking around the garden enjoying flowers), Hwasaekhyangbyuk(flower gardening), and Garden appreciation enjoying landscape through time and seasons with different inspirations.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.