• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.175-185
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• 2005

Governmental authorities have been trying to develop our city in respect to the growth of economy and it is true that their developmental policies have negative effects on ecosystem without doubt. To estimate these negative effects, this study is mainly focused on analysing the usage of the land according to the urban growth, and the temporal and spatial patterns between the elements which consist of the landscape of Daegu Metropolitan Sphere, by using the GIS method and the landscape indices. The results of the this study are as follow; the urban areas widened for $193.4km^2$ due to the shift of the urban function, and the forest areas were encroached for $455.6km^2$ into other landcover patterns. It was the shift of the agriculture areas that are given the most influence in those procedures since those developmental conditions are relatively satisfactory. Moreover the forest areas are structurally fragmented into the complicated form, and also the patterns of adjacent patches are become complex. These transitions are regarded as causes of increased external interventions to the forest areas, and these could possibly deteriorate the soundness of forest areas by reducing the core areas which are habitats of species. In conclusion, the results of this study evaluate the influence of much broader urban development on environment structure around urban and mutual relationship between them. In addition, it can provide methods and basic informations for the establishment of metropolitan urban plan after due considerations of the landscape ecological principle.

• Journal of Environmental Health Sciences
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• v.39 no.3
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• pp.239-246
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• 2013

Objectives: Antimicrobial resistance and multidrug resistance patterns have been studied with a total of 189 samples of Salmonella Enteritidis and Salmonella Typhimurium isolated from diarrhea patients in Incheon from 2008 to 2012. Methods: Antimicrobial resistance tests were determined by Disc Diffusion method. Results: The serological distribution of Salmonella spp. showed 108 strains (30.1%) of S. Enteritidis, 81 strains (22.6%) of S. Typhimirium, eight strains (8.0%) of S. Typhi, 11 strains ( 3.1% ) of S. Paratyphi, and the 151 other strains (42.1%). The separation rate of Salmonella spp. by year showed 14.5% (52 strains) in 2008, 13.6% (49 strains) in 2009, 22.8% (82 strains) in 2010, 25.3% (91 strains) in 2011, and 23.7% (85 strains) in 2012. Additionally, the separation rate of S. Enteritidis and S. Typhimirium in 2010 was the highest. The Salmonella spp. isolated from diarrhea patients showed significant differences according to age (p<0.05), gender (p<0.01) and medical institution (p<0.05). The highest resistance was found to the following antimicrobial agents: imipenem 77 strains, ampicillin 47 strains, ciprofloxacin 34 strains, nalidixic acid 29 strains for S. Enteritidis, and ampicillin 45 strains, nalidixic acid 45 strains for S. Typhimurium. Separated S. Enteritidis and S. Typhimurium resistance to the antibiotics by the year showed significant differences (p<0.05). The patterns of multidrug resistance rates were 43.1% (47 strains) for one drug, 8.3% (9 strains) for two drugs, 11.0% (12 strains) for three drugs, 15.62% (17 strains) for four drugs, and 13.7% (15 strains) for five or more drugs for S. Enteritidis. For S. Tyhpimurium, the rates were 15.0% (12 strains) for one drug, 10.0% (8 strains) for two drugs, 6.3% (five strains) for three drugs, 18.7% (15 strains) for four drugs, and 23.8% (19 strains) for five or more drugs. Conclusion: The antibiotic resistance issue is directly related to people's lives. Thus, the usage of antibiotics should be reduced in order to manage antibiotic resistance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.19-29
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• 2011

Due to the development of web technologies and the increasing use of smart devices such as smart phone, in recent various web services are widely used in many application fields. In this environment, the topic of supporting personalized and intelligent web services have been actively researched, and an analysis technique on a web-click stream generated from web usage logs is one of the essential techniques related to the topic. In this paper, for efficient analyzing a web-click stream of sequences, a sequential pattern mining technique is proposed, which satisfies the basic requirements for data stream processing and finds a refined mining result. For this purpose, a concept of interesting sequential patterns with a time-interval constraint is defined, which uses not on1y the order of items in a sequential pattern but also their generation times. In addition, A mining method to find the interesting sequential patterns efficiently over a data stream such as a web-click stream is proposed. The proposed method can be effectively used to various computing application fields such as E-commerce, bio-informatics, and USN environments, which generate data as a form of data streams.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.275-281
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• 2016

In modern society, which boomed it became easier to obtain the necessary information to the emergence of a variety of smart devices. Due to this, the frequency of using the content based on the Web is growing rapidly. In addition, companies are turning into a production and modify the content using the CMS under the web-based. It can be a very important part to provide users with the content. Currently web services are designing a UI to the device and provided. To improve the ease of use, they are enhancing services only by survey and analysis of the patterns of all users. Most are designed without considering the UX only in the technical aspects. In this paper, to break the limits that apply to all users of the Web service pattern analysis, we propose a visualization system via the animation based on the individual user's movement patterns and usage patterns. Through this convergence is expected to be able to transform the web from the central manager to the user UX and the planning aspects researchers.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.3 s.151
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• pp.378-385
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• 2006

To release fashion trends in an efficient way, many of the apparel business and fashion educational institutes in land adopt fashion shows employing fashion models. Modeling rather than flat pattern making realizes the majority of the complicated design works for the fashion shows. However, for the different measurements between the dress form and the real human model, problems often occur during the modeling and fitting processes. Researches on the standard dress form development representing professional fashion models' features are therefore in urgent need to enable the related apparel business and fashion institutes to make appropriate use of the dress form in their jobs. The study has been conducted as a preliminary study using a questionnaire method ultimately to develop the female dress form. A questionnaire in the research aimed at an investigation into the actual conditions of and satisfaction with the usage and the body measurements of existed dress forms. Approximately 30 fashion-related educational institutes and 10 apparel companies responded to the survey. Data derived from the survey was analyzed using SPSS version 10.1, the statistics tool. The results throughout the research were discussed in terms of largely three categories that are; (1) the general conditions of the usage of the dress form to prepare fashion shows: e.g. the frequency of holding the fashion show in an annual term, the proportion of professional and amateur models employed for the fashion show, the methods to construct garments, types and number of dress forms utilized and etc.; (2) factors considered to purchase the dress form e.g. its functionality, shapes, sizes, duration, price, A/S condition and etc.; and(3) satisfaction with the similarity between the dress form and the human body in the relation to the body measurements. Measurements in length wise, front and back waist lengths, neck to bust point on the dress forms were apparently differed from the ones of the actual body. In particular, differed torso length measurements cause the problem to have to alter the whole silhouette, consequently, the resultant patterns as well. In girth measurements, in order of bust and waist girths, the satisfaction was low.

• The Journal of Pediatrics of Korean Medicine
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• v.26 no.3
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• pp.64-73
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• 2012

Objectives The purpose of this study is to see the prevalence and the patterns of the use of complementary and alternative medicine (CAM) in children and adolescent patients with allergies. Methods We analyzed data on 547 children and adolescents (age from 0-18 years old) chosen from all regions throughout the country with allergic diseases, including atopic dermatitis, allergic rhinitis, asthma and allergic urticaria. We used multiple logistic regression modeling to predict CAM use based on predictor variables. Results The previous 12-months prevalence of CAM usage in overall was 70.7% (atopic dermatitis, 78.1%; allergic rhinitis, 52.9%; asthma, 70.3%; allergic urticaria, 86.3%). Central and southern regions displayed significantly lower rate of using CAM compare to the northern region, and CAM was less likely to be used for the allergic rhinitis patients than the atopic dermatitis patients. The most commonly used CAM type was natural products (62.2%). Top five of the most frequently used CAM modalities were softener water, vitamin, red ginseng, wood bathing and aloe oil. One of the main reasons for trying CAM was from the 'hope for a more effective outcomes in additional to the conventional medicine' (43.9%). The subjective effectiveness of CAM was found to be excellent in 74.0% of the patients, and 70.3% of the parents were willing to recommend CAM therapies to the others. Conclusions CAM is used widely to treat allergic diseases in children and adolescents in Korea. Korean medical doctors should actively discuss the use of CAM with the patients and provide information on the effectiveness and safety of CAM as guide in making choice for usage of CAM.

• Journal of the HCI Society of Korea
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• v.12 no.2
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• pp.31-40
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• 2017

This a qualitative research about the virtual personal assistant, voice recognition device SKT 'NUGU' which was launched on September 1, 2016. For the study, an in-depth interview was committed with the 9 research participants who had used this device for more than a month. For the result of the interview, 362 concepts were discovered and through open coding, axis coding, selective coding the concepts got categorized in 16 sub-categories and 10 top categories. After recognizing 362 concepts from the interview sources, I proposed a paradigm model from the open coding. And from the selective coding, the main category of the study has been narrowed down to understand the 'Usage Patterns by Each Type'. As a result of the typification, it was confirmed that the usage pattern can be described in two different types of the dependent and inquiry type. From the result of the research, it provided the basic data about the user experience of virtual assistant which can be utilized when suggesting virtual personal assistant in the near future.