• Journal of Software Engineering Society
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• v.26 no.1
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• pp.17-29
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• 2013

Software System Evolution is more planned and systematic maintenance process as well as a type of maintenance. The research of software evolution provides basis of decisions for maintenance value through cost-benefit analysis of legacy system and architecture-based software evolution enables engineers to reduce maintenance cost by automation of repetitive evolution tasks. This paper proposes a framework for software system evolution using architectural transformation pattern.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.135-153
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• 2018

E-Card Service for Child Nutrition Program is one of the main public policy services nowadays. In case of inconvenience during the use of the e-cards, it is recommended to cooperate with related organizations in order to promptly handle and provide guidance, and thoroughly manage child feeding service such as hygiene, nutrition and kindness etc. To do so, it is very important to provide food service that meets local actual conditions and children's needs in a cost effective manner for the underage who are worried about the poorly-fed by understanding the pattern of child feeding e-card service. Hence. this paper aims to investigate how child feeding e-card service efficiently provides meals according to the local situation and children's needs through big data analysis and to propose a method of identifying welfare conditions according to the purpose of service with actual application examples. The results suggest that, first of all, this study is able to judge appropriateness of public institution's policy in a timely and repetitive manner through non-standard data analysis such as Naver News and transaction data. Secondly, this paper proposes a multi-layered analysis framework, which performs online open data analysis to detect policy issues, visualizes retrieval and preprocessing of real data, and performs abnormal pattern recognition. These will be worthy of reference to other similar projects.

• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.53-60
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• 2002

Typical Apriori-like methods for mining sequential patterns have some problems such as generating of many candidate patterns and repetitive searching of a large database. And PrefixSpan constructs the prefix projected databases which are stepwise partitioned in the mining process. It can reduce the searching space to estimate the support of candidate patterns, but the construction cost of projected databases is still high. For efficient sequential pattern mining, we need to reduce the cost to generate candidate patterns and searching space for the generated ones. To solve these problems, we proposed SuffixSpan(Suffix checked Sequential Pattern mining), a new method for sequential pattern mining, and show a formal approach to our method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1A
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• pp.46-54
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• 2005

This paper presents an effcient mobile multicast method using the user pattern. We exploit the repetitive movement pattern of mobile node to reduce the total number of experience of graft and join procedure. We defined the locality scope by a movement pattern. While the network is included in the locality scope, the network should maintain a multicast tree even when the mobile node moves to the other network. In this way, the mobile host can receive a multicast service without a delay when it moves to the network in the locality scope later. We compare our scheme with existing schemes under the total signaling cost and the service delay time by using a discrete analytical model for cost analysis. Analytical results demonstrated that the total signaling cost and service delay time was significantly reduced through our proposed scheme.

• Biomedical Science Letters
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• v.13 no.4
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• pp.293-304
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• 2007

The emergence of extended spectrum $\beta$-lactamase (ESBL) producing bacteria is worldwide concern. Until recently, the most frequently identified strains in the Republic of Korea were E. coli and Klebsiella spp. The incidence of resistance to extended spectrum $\beta$-lactam antibiotics is increasing in Wonju city, Korea. Total 57 strains of ESBL producing E. coli and Klebsiella species were isolated from Wonju Christian Hospital during a 9 month-period from April to December, 2003. To determine the prevalence and genotypes of the ESBL producing clinical isolates, antibiotic susceptibility and ESBL activity test by VITEK system and double disk synergy (DDS) test, and PCR based genotyping were performed. Fourteen (82%) isolates of 17 ESBL producing E. coli were found to have $bla_{TEM}$ gene and 5 (29%) isolates were found to have $bla_{CTX-M}$ gene by polymerase chain reaction (PCR). Thirty (75%) isolates of 40 ESBL producing Klebsiella species with $bla_{TEM}$ gene, 38 (95%) isolates with $bla_{SHV}$ gene, and 7 (20%) isolates with $bla_{CTX-M}$ type gene were also identified. Enterobacterial repetitive intergenic consensus (ERIC) PCR and similarity index by dendrogram for genetical similarity to band pattern of each clinical isolates were examined. ESBL producing E. coli were grouped into 6 clusters up to 84% of similarity index and Klebsiella species were grouped into 12 clusters up to 76% of similarity index. In conclusion, ESBL producing clinical isolates were characterized with the results from antimicrobial resistance pattern and genetical similarity using ERiC PCR.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.5
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• pp.293-299
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• 2007

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a (gas or oil) pipeline and acquire signals (also called sensor data) from their surrounding rings of sensors. By analyzing the signals captured in intelligent PIGs, we can detect pipeline defects, such as holes and curvatures and other potential causes of gas explosions. There are two major data access patterns apparent when an analyzer accesses the pipeline signal data. The first is a sequential pattern where an analyst reads the sensor data one time only in a sequential fashion. The second is the repetitive pattern where an analyzer repeatedly reads the signal data within a fixed range; this is the dominant pattern in analyzing the signal data. The existing PIG software reads signal data directly from the server at every user#s request, requiring network transfer and disk access cost. It works well only for the sequential pattern, but not for the more dominant repetitive pattern. This problem becomes very serious in a client/server environment where several analysts analyze the signal data concurrently. To tackle this problem, we devise a fast in-memory cache manager, called T-Cache, by considering pipeline sensor data as multiple time-series data and by efficiently caching the time-series data at T-Cache. To the best of the authors# knowledge, this is the first research on caching pipeline signals on the client-side. We propose a new concept of the signal cache line as a caching unit, which is a set of time-series signal data for a fixed distance. We also provide the various data structures including smart cursors and algorithms used in T-Cache. Experimental results show that T-Cache performs much better for the repetitive pattern in terms of disk I/Os and the elapsed time. Even with the sequential pattern, T-Cache shows almost the same performance as a system that does not use any caching, indicating the caching overhead in T-Cache is negligible.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1516-1522
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• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have 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.

• The Research Journal of the Costume Culture
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• v.17 no.6
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• pp.972-980
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• 2009

This study was aimed to develop ume flower image into a competitive fashion culture product image by reinterpreting the image in modern terms, manufacturing patterns and applying them to various items. In terms of method, ume flower petal was used as a motive and developed into a pattern, using Adobe Illustrator 10, a computer design program. Based on the symbolic image and realist form of ume flower, three new basic motives of new figurative image were set using form omission, simplification, overlapping, repetition and graphic elements. Each motive developed transformed patterns through the change, transformation, combination of colors. The repetitive unit of each motive set expressed geometrical patterns and combination of flower patterns using pattern repetition and $45^{\circ}$ repetition technique in combination with the check arrangement using quadrangle, and set the direction of design that would fit for each item of fashion culture products. Also, consistency and practicality were sought in the goods planning composition of each item by applying motive pattern results to the fashion culture goods, such as neckties, scarves, T-shirts that can be consumed in everyday life. It seems that more creative culture goods including ume flowers will be developed by seeing our own cultural elements as well as flower patterns like ume flower with modern trends.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.255-263
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• 2010

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run ${\bar{x}}$-R, ${\bar{x}}$-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

• Journal of Korea Multimedia Society
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• v.5 no.5
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• pp.590-598
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• 2002

For efficient sequential pattern mining, we need to reduce the cost to generate candidate patterns and searching space for the generated ones. Although Apriori-like methods like GSP[8] are simple, they have some problems such as generating of many candidate patterns and repetitive searching of a large database. PrefixSpan[2], which was proposed as an alternative of GSP, constructs the prefix projected databases which are stepwise partitioned in the mining process. It can reduce the searching space to estimate the support of candidate patterns, but the construction cost of projected databases is still high. To solve these problems, we proposed SuffixSpan(Suffix checked Sequential Pattern mining) as a new sequential pattern mining method. It generates a small size of candidate pattern sets using partition property and suffix property at a low cost and also uses 1-prefix projected databases as the searching space in order to reduce the cost of estimating the support of candidate patterns.