• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.597-600
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• 2008

스타(star) 네트워크는 노드 대칭성, 최대 고장 허용도, 계층적 분할 성질을 갖고, 하이퍼큐브보다 망 비용이 개선 된 상호 연결망이다. 본 연구에서는 상호연결망으로 널리 알려진 스타네트워크와 RFM, 버블정렬네트워크 사이의 임베딩 방법을 제안하고, 임베딩의 연장율 비용을 분석한다. 연구 결과로 버블정렬(Bubblesort) 그래프 $B_N$을 RFM 그래프 $R_N$에 연장비율 2, 버블정렬(Bubblesort) 그래프 $B_N$을 스타그래프 $S_N$에 연장율 3에 임베딩 할 수 있다.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.1
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• pp.33-40
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• 2003

Radio frequency management system(RFMS) is being operated to facilitate national spectrum management and monitoring in Korea. To improve the engineering analysis function in RFMS, criteria for the automated selection of the propagation model adequate to the radio station service environment considered are proposed. Those criteria are derived from the specified parameters obtained through the analysis of related ITU-R propagation & diffraction loss models which are to be used in RFMS. Then, using criteria acquired, computer program is made to achieve the automated selection of the most appropriate propagation algorithm, among the ones provided in RFMS, to the environment in which the engineering analysis is required. Furthermore, an illustrative example is shown with the proposals fur increasing the efficiency of the engineering analysis in RFMS.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2001-2010
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• 2021

The purpose of this study is to efficiently improve orthorectification of KOMPSAT images. As the development of domestic and abroad earth observation satellites accelerates, the number and amounts of satellite images acquired are rapidly increasing. Accordingly, various studies are being conducted to improve orthorectification for the acquired image more quickly and efficiently. This study focused on enhancing processing efficiency through algorithm improvement, except for improving hardware computing capabilities such as GPU. Accordingly, the algorithm was improved with the LUT-based RFM method, and compared and analyzed in terms of accuracy and time-efficiency that vary depending on offset settings.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.9-15
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• 2013

Recently, Intrusion detection system is an important technology in computer network system because of has seen a dramatic increase in the number of attacks. The most of intrusion detection methods do not detect intrusion on real-time because difficult to analyze an auditing data for intrusions. A network intrusion detection system is used to monitors the activities of individual users, groups, remote hosts and entire systems, and detects suspected security violations, by both insider and outsiders, as they occur. It is learns user's behavior patterns over time and detects behavior that deviates from these patterns. In this paper has rule-based component that can be used to encode information about known system vulnerabilities and intrusion scenarios. Integrating the two approaches makes Intrusion Detection System a comprehensive system for detecting intrusions as well as misuse by authorized users or Anomaly users (unauthorized users) using RFM analysis methodology and monitoring collect data from sensor Intrusion Detection System(IDS).

• Journal of Industrial Convergence
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• v.6 no.1
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• pp.35-56
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• 2008

포화상태인 인증사장은 신규기업 유치 보다 기존기업 유지를 통해 이탈율을 최소한으로 줄여야 한다. 기업의 여건, 성향, 요구사항 등을 분석하여, 고객관리시스템을 도입하여야 한다. 수많은 기업정보를 세분화하여 분류하고 이것을 이용하여 고객정보를 관리해야 한다. CRM은 고객관리를 효율적으로 할 수 있는 정보관리시스템이다. CRM 시스템을 효과적인 구축을 위해서는 설문지 및 QFD을 통해서 고객의 요구사항을 파악하고, SWOT을 통해 외부환경 및 내부역량을 파악한다. 또한, 내부적으로 CRM 체크리스트를 작성하고, 체크리스트에 기록된 내용을 분석하여 나온 결과를 중심으로 하여 부족한 부분에 대해서는 집중적으로 개선방안을 수립해야 한다. 고객의 등급을 RFM에 의해 분류하여 관리해야 한다. 고객의 등급 및 분류에 따라 마케팅 전략을 수립하여 운영 및 관리를 해야 한다. CRM 구축을 통해 CRM 인프라를 강화하고 CRM 전략을 수립해야 마케팅 및 기술 경쟁력을 확보할 수 있을 것이다.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.281-287
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• 2003

This paper proposed an improvement scheme for effective usage of radio-frequency management system(RFMS), which has been operated to facilitate national spectrum management and monitoring in Korea. Based on the wave propagation models, interference analysis algorithms, and sharing criteria recommended by ITU-R, we derived criteria for the automated selection of the channel interference analysis algorithms and sharing conditions adequate to the environment to be analysed. Then using the obtained criteria, computer and program has been made and shown to select the most appropriate propagation models, interference analysis algorithms, and sharing criteria from the ones provided in RFMS, with the illustrative example.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1445-1456
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• 2018

The purpose of this study is to compare the quality and characteristics of matching DEMs by using KOMPSAT-3 stereo pair capture in in-track and cross-track. For this purpose, two stereo pairs of KOMPSAT-3 were collected that were taken in the same area. The two stereo pairs have similar stereo geometry elements such as B/H, convergence angle. Sensor modeling for DEM production was performed with RFM affine calibration using multiple GCPs. The GCPs used in the study were extracted from the 0.25 m ortho-image and 5 meter DEM provided by NGII. In addition, matching DEMs were produced at the same resolution as the reference DEMs for a comparison analysis. As a result of the experiment, the horizontal and vertical errors at the CPs indicated an accuracy of 1 to 3 pixels. In addition, the shapes and accuracy of two DEMs produced in areas where the effects of natural or artificial surface land were low were almost similar.