• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.95-108
• /
• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Korean Journal of Heritage: History & Science
• /
• v.53 no.3
• /
• pp.80-109
• /
• 2020

The Sacheonwangsa Temple in Silla is an esoteric temple that was founded provisionally in 670, and was completed in 679. This study attempted to elucidate the planning method of the Sacheonwangsa Temple based on the results of research on excavations and investigations into its construction processes and construction measures thereof. The research results are as follows. (1) In the site construction, assuming the size of one Bang (坊) on the south of Nangsan Mountain, after dividing the north-south width into three equal parts, there is a possibility that two of these parts were set to the flat portion. (2) In the 'Jochang (祖創, 670)', it is estimated that an area of 300 cheoks by 300 cheoks was postulated on the flat surface, and, as an initial conception, the mandala's plane design of the outer square 2 hasta (3 cheoks) and inner square 1 hasta (1.5 cheoks) was originally devised for the setting of 'Mudra (神印)', and an area 100 times greater has been set as the basis in the scale and layout planning of the central block. (3) During 'Gaechang (攺刱, ~679)', it is judged that because of the narrowness of the distance between the Pagoda and Geumdang Hall, which occurs when the center of the Geumdang Hall coincides with the center of 'the first stage of the foundation (先築基壇)', the scale and layout planning were adjusted from the initial conception. (4) The arrangement of the building was determined by dividing the fixed size of the central block (280 cheoks by 320 cheoks). Specifically, the east-west direction is set on the quartile's line of the east-west width of the central block, and in contrast, the north-south direction is based on the structural characteristics of the central block. It is presumed that the position of the transept was determined through the division and adjustment of the column spacing of the east-west corridor, then the Geumdang Hall and Altar were based on this. (5) The scale of the Geumdang Hall and Pagoda is determined by the petition of the division by the unit fraction starting from the quartile's line of the central block's east-west width. This planning is understood to be based on the self-similarity, which is rooted in the mandala's plane design as the model.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1505-1514
• /
• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• (The) Research of the performance art and culture
• /
• no.19
• /
• pp.88-123
• /
• 2009

Gyeongsanjain Dano Festival is a local festival of Yeongnam that was initially designated as Intangible Cultural Heritage No. 44 "Hanjanggun Nori" in 1971 and finally became Gyeongsanjain Dano Festival in 2007. At first, few parts of it were designated for preservation and it was succeeded as a whole without connections between each part. Problems also rise from the fact that it has not been closely studied. Gyeongsanjain Dano Festival is mostly classified into five parts: Keungut, Yeowonmu, Hojanggut, Hanmyoje, and Jainpalgwangdae. This study has closely discussed each part as follows: 1. Keungut 1) Process and Contents of Keungut - Characteristics of Gut in Daegu and Gyeongsan 2) Presence of Performers that can Execute Traditional Gut of Local Area 3) Problems with the Name of Keungut - Ex: 'Hanjanggungut' or 'Keungut' 4) Problems of Dano Festival without Parts to Summon and Send off Spirits before and After Keungut and Restoration Measures 2. Yeowonmu 1) Essence of Yeowonmu and Yeowonhwa 2) Problems with the Mass Game of Hundreds of High School Girls 3) Origin and Succession of Yeowonmu 3. Hojanggut 1) Changes in Characters of Hojanggut 2) Composition and Characteristics of Hojanggut 3) Problems with the Name and Characteristics of Hojanggut 4. Jainpalgwangdae Nori 1) Similarity with Newly Created Shows 2) Problems with the Name of Palgwangdae 3) Difference with the Composition of Other Mask Dances 4) Dances and Movements Distinguished from Other Mask Dances in Yeongnam The following are the solutions suggested for Gyeongsan Jain Dano Festival. First, for the restoration of Gyeongsanjain Dano Festival, Dano festivals and Byeolsinje of nearby areas with clear traditions could be benchmarked. Second, the major content of Gyeongsanjain Dano Festival is 'Hanjanggungut,' and it has to be the leading content of the festival. Third, the structural principle of Korean festival must be adopted and the process and principle must correspond to those of other traditional shows. Fourth, as Gyeongsanjain Dano Festival is the comprehensive form of art, folk, and festival, each part must be closely related in a well-planned scenario. Fifth, Intangible talents and successful training must be widely acknowledged for successful transmission and responsible performances. Sixth, traditional festivals must be restored along with the discovery and development of various festival repertories and spectacles such as tour programs and experiential tours that contribute to local economy.