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Strategy for Store Management Using SOM Based on RFM (RFM 기반 SOM을 이용한 매장관리 전략 도출)

  • Jeong, Yoon Jeong;Choi, Il Young;Kim, Jae Kyeong;Choi, Ju Choel
    • 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.

Development of Sentiment Analysis Model for the hot topic detection of online stock forums (온라인 주식 포럼의 핫토픽 탐지를 위한 감성분석 모형의 개발)

  • Hong, Taeho;Lee, Taewon;Li, Jingjing
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.187-204
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    • 2016
  • Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

Multi-day Trip Planning System with Collaborative Recommendation (협업적 추천 기반의 여행 계획 시스템)

  • Aprilia, Priska;Oh, Kyeong-Jin;Hong, Myung-Duk;Ga, Myeong-Hyeon;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.159-185
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    • 2016
  • Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

Policies for Improving Thermal Environment Using Vulnerability Assessment - A Case Study of Daegu, Korea - (열취약성 평가를 통한 열환경 개선 정책 제시 - 대구광역시를 사례로 -)

  • KIM, Kwon;EUM, Jeong-Hee
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.2
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    • pp.1-23
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    • 2018
  • This study aims to propose a way for evaluating thermal environment vulnerability associated with policy to improve thermal environment. For this purpose, a variety of indices concerning thermal vulnerability assessment and adaptation policies for climate change applied to 17 Korean cities were reviewed and examined. Finally, 15 indices associated with policies for improving thermal environment were selected. The selected indices for thermal vulnerability assessment were applied to Daegu Metropolitan City of South Korea as a case study. As results, 15 vulnerability maps based on the standardized indices were established, and a comprehensive map with four grades of thermal vulnerability were established for Daegu Metropolitan City. As results, the area with the highest rated area in the first-grade(most vulnerable to heat) was Dong-gu, followed by Dalseo-gu and Buk-gu, and the highest area ratio of the first-grade regions was Ansim-1-dong in Dong-gu. Based on the standardized indices, the causes of the thermal environment vulnerability of Ansim-1-dong were accounted for the number of basic livelihood security recipients, the number of cardiovascular disease deaths, heat index, and Earth's surface temperature. To improve the thermal environment vulnerability of Ansim-1-dong, active policy implementation is required in expansion and maintenance of heat wave shelters, establishment of database for the population with diseases susceptible to high temperature environments, expansion of shade areas and so on. This study shows the applicability of the vulnerability assessment method linked with the policies and is expected to contribute to the strategic and effective establishment of thermal environment policies in urban master district plans.

An Exploration For Future Emerging Technologies by Science Mapping and a Dynamic Portfolio Setting for Government R&D Strategy (과학지도 작성을 통한 미래기술 발굴 및 정부R&D의 동적 투자방향성 설정 연구)

  • Yang, He-Young;Son, Suk-Ho;Han, Min-Kyu;Han, Jong-Min;Yim, Hyun
    • Journal of Technology Innovation
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    • v.19 no.3
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    • pp.1-29
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    • 2011
  • Korean government built "2040 Science and Technology Future Vision" in order to show positive future scenarios and suggest a long-term guideline for a progress in science and technology. The S&T Future Vision was built based on an analysis of global megatrends and a prospect of domestic social change. After building S&T Future Vision, the "Government R&E Strategy"s was established as a follow-up action plan. The Government R&D Strategy consists of lists of future emerging technologies for future leadership, government R&D investment status and investment portfolio plans. Exploring future emerging technologies aggressively and making a governmental R&D strategic policy are requirements for national competitiveness, leadership in the world. Therefore search and selection for future emerging technologies is getting more and more important recently. Generally qualitative methodologies have been used such as expert-panel discussion method and portfolio analysis with expert valuation method in order to explore future technologies. These experts-based qualitative methodologies are well defined but lacking in some objectivity because size of expert-panels has limitations. We suggest a quantitative methodology, science mapping method to compensate this shortcoming in this study. There is another limitation related governmental R&D strategy which is that general R&D portfolios are static until a point of technology realization. We also propose a dynamic R&D investment portfolio which present different portfolios at a intermediate point and a point of technology realization. We expect this try with science mapping method and a dynamic R&D portfolio could strengthen strategic aspect of government R&D policy.

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Strategic Direction and Road Map of Expanding Prevention of Winter Disease in the Summer (동병하치 확산을 위한 전략적 방향과 이행방안)

  • Song, Ho-Sueb
    • Journal of Acupuncture Research
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    • v.27 no.3
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    • pp.147-157
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    • 2010
  • Objectives : The purpose of this study was to propose appropriate strategic directions and road maps for successful achievement of programs preventing winter disease in the summer. Methods : Details on programs preventing winter disease in the summer such as clear concept, theoretical basis, current status, intervention or available prescriptions and indication/contraindication/caution were prepared through the related journal review, upon which an observational study was devised and done for simulation to find out even a trivial problem and to guarantee the safety beforehand. The experimental group was divided into 5 groups by the size of pill and the way ginger is treated; 1cm pill with ginger group, 3cm pill group without ginger, 3cm pill group dipped into ginger, 3cm pill group applying ginger to acupoints and 3cm pill group with ginger Results 1. program preventing winter disease in the summer was defined as representative winter diseases such as common cold, influenza, chronic asthma, chronic bronchitis, allergic rhinitis, emphysema, chronic gastritis and rheumatoid arthritis, and preventive care in the summer, reinforcing deficient yang qi of five viscera by using exuberant yang qi from summer heat. 2. It was based upon historically established theories which is 'nourishing yang qi in the spring and summer', 'long summer, namely rainy spell in the summer overwhelms the winter, because of earth winning water according to the five phases theory' and 'To replenish yang qi is major principle to treat winter diseases, which can be most appropriately and timely applied to the patient with deficient yang qi of five viscera inherently, especially in the three dog days of the summer, because of exuberant exterior yang qi and deficient interior yang qi in the five viscera'. 3. In the adjacent China and Taiwan, acupoint applying method in the three dog days named 'San Fu Tie' have been stirring a boom throughout the nation, in which Xiaochuan Gao was used as a basic prescription and it mainly was applied at bilateral $BL_{13}$, $_{15}$ and $_{17}$ for about 4 hours. As far as domestic current status, the necessity of adopting the above method prior to Herbal formula was also recognised, because not a few koreans have apprehension for the safety of it including medicinal herbs and are reluctant to take it any more due to negative advertisement of narrow minded doctors' association. 4. Indication of acupoint applying method in the three dog days included most of winter diseases such as common cold, influenza, chronic asthma, chronic bronchitis, allergic rhinitis, emphysema, chronic gastritis. contraindication was pregnant woman and the weak such as infants and the old. More attention was paid to grasp firmly the normal reaction following the treatment for preventing side effect and teasing blister. recommendation was also given to abstain from food inducing phlegm and dampness such as meat, shrimp and crab as well as cold drinks and foods 5. In the simulation observational study based upon the above findings following review the related articles, no blister was shown on the acupoints icluding bilateral $BL_{13}$, $_{15}$ and $_{17}$ in every experimental group during 24hr observation following the acupoint applying treatment with pills made by modified and devised prescription. At 4 hr, the effectiveness of it reached a peak showing redness and mild tenderness and there is little difference between groups 3cm pills groups regardless of the way ginger was treated. abdominal distention and growling was found in all the volunteers during the treatment at CV 8. Strategic directions and road maps : Through successful fulfillment of the program preventing winter disease in the summer, Korean traditional medicine should be integrated into mainstream national health care services. Cultural access was thought to be as important as Scientific EBM approach. First of all, To evoke potential cultural homogeneity from campaigns and press advertisement was needed for promoting public awareness about preventing winter disease in the summer by enhancing immunity via acupoint applying treatment in the three dog days, and then indigenous name as Sambokcheop, protocol, Clinical Research Form for data collection of it should be developed and prepared. Once the first step was taken this summer, through a thorough data collection and scrutinized scientific evaluation, drawbacks should be compensted for and the efficacy and safety should be substantiated.

Quantitative Comparisons between CT and $^{68}Ge$ Transmission Attenuation Corrected $^{18}F-FDG$ PET Images: Measured Attenuation Correction vs. Segmented Attenuation Correction (CT와 $^{68}Ge$ 감쇠보정 $^{18}F-FDG$ PET 영상의 정량적 비교: 측정감쇠보정대 분할감쇠보정)

  • Choi, Joon-Young;Woo, Sang-Keun;Choi, Yong;Choe, Yearn-Seong;Lee, Kyung-Han;Kim, Byung-Tae
    • Nuclear Medicine and Molecular Imaging
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    • v.41 no.1
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    • pp.49-53
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    • 2007
  • Purpose: It was reported that CT-based measured attenuation correction (CT-MAC) produced radioactivity concentration values significantly higher than $^{68}Ge$-based segmented attenuation correction (Ge-SAC) in PET images. However, it was unknown whether the radioactivity concentration difference resulted from different sources (CT vs. Ge) or types (MAC vs. SAC) of attenuation correction (AC). We evaluated the influences of the source and type of AC on the radioactivity concentration differences between reconstructed PET images in normal subjects and patients. Material and Methods: Five normal subjects and 35 patients with a known or suspected cancer underwent $^{18}F-FDG$ PET/CT. In each subject, attenuation corrected PET images using OSEM algorithm (28 subsets, 2 iterations) were reconstructed by 4 methods: CT-MAC, CT-SAC, Ge-MAC, and Ge-SAC. The physiological uptake in normal subjects and pathological uptake in patients were quantitatively compared between the PET images according to the source and type of AC. Results: The SUVs of physiological uptake measured in CT-MAC PET images were significantly higher than other 3 differently corrected PET images. Maximum SUVs of the 145 foci with abnormal FDG uptake in CT-MAC images were significantly highest among 4 differently corrected PET images with a difference of 2.4% to 5.1% (p<0.001). The SUVs of pathological uptake in Ge-MAC images were significantly higher than those in CT-SAC and Ge-MAC PET images (p<0.001). Conclusion: Quantitative radioactivity values were highest in CT-MAC PET images. The adoption of MAC may make a more contribution than the adoption of CT attenuation map to such differences.

Prediction of Urban Flood Extent by LSTM Model and Logistic Regression (LSTM 모형과 로지스틱 회귀를 통한 도시 침수 범위의 예측)

  • Kim, Hyun Il;Han, Kun Yeun;Lee, Jae Yeong
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.40 no.3
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    • pp.273-283
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    • 2020
  • Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

Correlation among Ownership of Home Appliances Using Multivariate Probit Model (다변량 프로빗 모형을 이용한 가전제품 구매의 상관관계 분석)

  • Kim, Chang-Seob;Shin, Jung-Woo;Lee, Mi-Suk;Lee, Jong-Su
    • Journal of Global Scholars of Marketing Science
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    • v.19 no.2
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    • pp.17-26
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    • 2009
  • As the lifestyle of consumers changes and the need for various products increases, new products are being developed in the market. Each household owns various home appliances which are purchased through the choice of a decision maker. These appliances include not only large-sized products such as TV, refrigerator, and washing machine, but also small-sized products such as microwave oven and air cleaner. There exists latent correlation among possession of home appliances, even though they are purchased independently. The purpose of this research is to analyze the effect of demographic factors on the purchase and possession of each home appliances, and to derive some relationships among various appliances. To achieve this purpose, the present status on the possession of each home appliances are investigated through consumer survey data on the electric and energy product. And a multivariate probit(MVP) model is applied for the empirical analysis. From the estimation results, some appliances show a substitutive or complementary pattern as expected, while others which look apparently unrelated have correlation by co-incidence. This research has several advantages compared to previous literatures on home appliances. First, this research focuses on the various products which are purchased by each household, while previous researches such as Matsukawa and Ito(1998) and Yoon(2007) focus just on a particular product. Second, the methodology of this research can consider a choice process of each product and correlation among products simultaneously. Lastly, this research can analyze not only a substitutive or complementary relationship in the same category, but also the correlation among products in the different categories. As the data on the possession of home appliances in each household has a characteristic of multiple choice, not a single choice, a MVP model are used for the empirical analysis. A MVP model is derived from a random utility model, and has an advantage compared to a multinomial logit model in that correlation among error terms can be derive(Manchanda et al., 1999; Edwards and Allenby, 2003). It is assumed that the error term has a normal distribution with zero mean and variance-covariance matrix ${\Omega}$. Hence, the sign and value of correlation coefficients means the relationship between two alternatives(Manchanda et al., 1999). This research uses the data of 'TEMEP Household ICT/Energy Survey (THIES) 2008' which is conducted by Technology Management, Economics and Policy Program in Seoul National University. The empirical analysis of this research is accomplished in two steps. First, a MVP model with demographic variables is estimated to analyze the effect of the characteristics of household on the purchase of each home appliances. In this research, some variables such as education level, region, size of family, average income, type of house are considered. Second, a MVP model excluding demographic variables is estimated to analyze the correlation among each home appliances. According to the estimation results of variance-covariance matrix, each households tend to own some appliances such as washing machine-refrigerator-cleaner-microwave oven, and air conditioner-dish washer-washing machine and so on. On the other hand, several products such as analog braun tube TV-digital braun tube TV and desktop PC-portable PC show a substitutive pattern. Lastly, the correlation map of home appliances are derived using multi-dimensional scaling(MDS) method based on the result of variance-covariance matrix. This research can provide significant implications for the firm's marketing strategies such as bundling, pricing, display and so on. In addition, this research can provide significant information for the development of convergence products and related technologies. A convergence product can decrease its market uncertainty, if two products which consumers tend to purchase together are integrated into it. The results of this research are more meaningful because it is based on the possession status of each household through the survey data.

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Evaluation of Germplasm and Development of SSR Markers for Marker-assisted Backcross in Tomato (분자마커 이용 여교잡 육종을 위한 토마토 유전자원 평가 및 SSR 마커 개발)

  • Hwang, Ji-Hyun;Kim, Hyuk-Jun;Chae, Young;Choi, Hak-Soon;Kim, Myung-Kwon;Park, Young-Hoon
    • Horticultural Science & Technology
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    • v.30 no.5
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    • pp.557-567
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    • 2012
  • This study was conducted to achieve basal information for the development of tomato cultivars with disease resistances through marker-assisted backcross (MAB). Ten inbred lines with TYLCV, late blight, bacterial wilt, or powdery mildew resistance and four adapted inbred lines with superior horticultural traits were collected, which can be useful as the donor parents and recurrent parents in MAB, respectively. Inbred lines collected were evaluated by molecular markers and bioassay for confirming their disease resistances. To develop DNA markers for selecting recurrent parent genome (background selection) in MAB, a total of 108 simple sequence repeat (SSR) primer sets (nine per chromosome at average) were selected from the tomato reference genetic maps posted on SOL Genomics Network. Genetic similarity and relationships among the inbred lines were assessed using a total of 303 polymorphic SSR markers. Similarity coefficient ranged from 0.33 to 0.80; the highest similarity coefficient (0.80) was found between bacterial wilt-resistant donor lines '10BA333' and '10BA424', and the lowest (0.33) between a late blight resistant-wild species L3708 (S. pimpinelliforium L.) and '10BA424'. UPGMA analysis grouped the inbred lines into three clusters based on the similarity coefficient 0.58. Most of the donor lines of the same resistance were closely related, indicating the possibility that these lines were developed using a common resistance source. Parent combinations (donor parent ${\times}$ recurrent parent) showing appropriate levels of genetic distance and SSR marker polymorphism for MAB were selected based on the dendrogram. These combinations included 'TYR1' ${\times}$ 'RPL1' for TYLCV, '10BA333' or '10BA424' ${\times}$ 'RPL2' for bacterial wilt, and 'KNU12' ${\times}$ 'AV107-4' or 'RPL2' for powdery mildew. For late blight, the wild species resistant line 'L3708' was distantly related to all recurrent parental lines, and a suitable parent combination for MAB was 'L3708' ${\times}$ 'AV107-4', which showed a similarity coefficient of 0.41 and 45 polymorphic SSR markers.