• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.143-154
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• 2020

In this study, four types of bodice patterns of elderly males in their 70s and 80s were made, and appearance evaluation was conducted through 3D simulation. For objective evaluation, airgap, cross section, color distribution, etc. were analyzed to compare differences between patterns. The pattern shape of bodice for elderly males was a pattern without darts except for the L pattern. As a result of appearance evaluation for 3D simulation, the elderly males' pattern was found to have a significant difference among the patterns on the front, side, and back items, and the H pattern was analyzed as the best pattern in all items except the armhole shape on the side. As a result of evaluating the airgap, color distribution, and cross-section, the most suitable pattern for the elderly male's body type was analyzed as the H pattern. Based on the H pattern, it is thought that the development of a pattern suitable for the upper body shape of elderly male should be made.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.39-48
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• 2020

Data mining techniques have been suggested to find efficiently meaningful and useful information. Especially, in the big data environments, as data becomes accumulated in several applications, related pattern mining methods have been proposed. Recently, instead of analyzing not only static data stored already in files or databases, mining dynamic data incrementally generated in a real time is considered as more interesting research areas because these dynamic data can be only one time read. With this reason, researches of how these dynamic data are mined efficiently have been studied. Moreover, approaches of mining representative patterns such as maximal pattern mining have been proposed since a huge number of result patterns as mining results are generated. As another issue, to discover more meaningful patterns in real world, weights of items in weighted pattern mining have been used, In real situation, profits, costs, and so on of items can be utilized as weights. In this paper, we analyzed weighted maximal pattern mining approaches for data generated incrementally. Maximal representative pattern mining techniques, and incremental pattern mining methods. And then, the application scenarios for analyzing the required commodity patterns in infants are presented by applying weighting representative pattern mining. Furthermore, the performance of state-of-the-art algorithms have been evaluated. As a result, we show that incremental weighted maximal pattern mining technique has better performance than incremental weighted pattern mining and weighted maximal pattern mining.

• Journal of the Korean Society of Costume
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• v.52 no.1
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• pp.53-67
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• 2002

자연을 분석함으로써, 얻어진 기하학적 패턴은 이미 자연의 질서를 포함하고 있는 논리적이고 합리적인 기본형이기 때문에 간결하며 시각적으로 명쾌감을 준다. 이러한 기하학적 패턴은 복식 디자인에 있어서 20 세기 이후 여러 디자이너의 작품을 통해 재구성되어 현대적 이미지를 나타내는 중요한 모티브가 되고 있으며, 다양한 기법과 재료로 형성화하여 도입되고 있다. 이에 본 연구는 복식의 문양, 실루엣, 디테일에 사용되고 있는 기하학적 패턴을 연구함으로써 기하학적 패턴의 새로운 조형가치를 고찰하였다. 먼저 기하학의 용어 정의를 하였고 기하학적 패턴의 유형과 표현 기법을 분석하고 정리하여 현대 패션에 나타난 기하학적 패턴의 조형미와 그것을 바탕으로 패션 이미지를 추론해 보았다. 현대 패션에 나타난 기하학 패턴을 분석해 보면 유형으로는 첫째, 기하하적 문양으로 복식디자인에 있어서 주로 평면적인 형태로 많이 나타나지만, 크기가 다르고 동일한 기하학적 패턴을 조합시킴으로서 평면적인 형태에 공간감을 부여하기도 하며, 같은 기하학적 패턴의 표면이라도 배치구조에 의해 직선 혹은 사선으로 지각되므로 전혀 다른 이미지를 주었다. 또한 현대 패션에 나타난 기하학적 패턴이 종류는 세로 스트라이프, 가로 스트라이프, 격자 문양, 원, 사선 스트라이프, 마름모, 사각형, 삼각형 등의 순서로 많이 나타났다. 둘째, 색채는 단색의 복식에 강한 대비가 이루어지는 색상으로 표현되어 역동감과 유연한 운동감을 나타났다. 셋째, 기하학적 실루엣으로 단순한 라인의 형태를 나타내거나 입체적이고 부조적인 형태로 구성되어 전체적인 실루엣으로 사용되어 강한 조형감각을 보여주는데 원형을 이용한 실루엣이 가장 많았으며 사각형을 이용한 실루엣, 삼각형을 이용한 실루엣 순서로 나타났다. 넷째, 기하학적인 디테일로 복식의 어느 한 부분에 장식적으로 사용되거나 입체적 형태로 부출 되어 부조적인 느낌을 주는데 소매에 가장 많이 나타났으며 앞여밈, 칼라, 밑단, 주머니 순서로 장식되었다. 다섯째, 현대 패션에 표현된 기하학적 패턴의 표현기법으로는 프린팅, 퀼팅, piece기법, 패치워크, 엮기, 꼴라쥬, 아플리케 순서로 많이 나타났다. 위의 분석을 토대로 기하학 패턴을 활용한 디자인에 내재된 조형의지는 다음과 같이 정리되었다. 첫째, 기하학적 패턴이 지닌 단순성과 경직성을 완화하기 위하여 여러 가지 패브릭을 조합시켜 입체적인 표면효과로 시각적인 착시효과를 극대화하였다. 둘째, 표현기법은 입체파적 표현주의의 특성의 하나로 복시에 사용되는 소재의 왜곡으로 설명할 수 있으며, 새롭고 실험적인 소재의 도입으로 인해 의외성과 부조화를 유발시키는 통시에 유희직인 일면도 지니는 일종의 그로테스크를 나타냈다. 이상에서 정립된 조형의지를 바탕으로 현대 패션에 나타란 기하학 패턴은 절제된 단순함과 명확성으로 단순미가 유추되었고 강한 색상대비로 인한 시각적 집중효과로 주목성을 가지며 재현이 가능하므로 반복성이 유추되었다. 그리고 표준영역이 없는 창의적 표현으로 풍부한 독창성을 보여주고 있다. 또한 내재된 패션 이미지를 분석해 보면 정확함과 차가움의 의미를 지닌 이지적 이미지와 우주의 질서를 반영하는 상징적 이미지, 복잡한 자연으로부터 간결한 형태로의 경향성이 이루어낸 인공적 이미지를 느낄 수 있었으며, 미래적 이미지와 전통적 이미지의 상반된 개념의 이미지를 같이 내포하고 있음을 추론할 수 있었다. 이와 같이 현대 패션에 표현된 기하학적 패턴은 복식을 조형예술 분야로 확실히 인식시키고 발전시키는 데 중요한 촉매제 역할을 담당하고 있으며 또한 많은 디자이너들에게 창조적 욕구를 불러일으키고 영감을 주는데 중요한 모티브를 제공하고 있다.

• The Mathematical Education
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• v.62 no.2
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• pp.175-194
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• 2023

Despite the importance of pattern learning for elementary school students, few studies have investigated in detail the understanding of patterns of lower-grade students. This study aimed to analyze the understanding of patterns of second-grade elementary school students. Since the patterns in the second grade are taught through the unit called Finding Rules, students' understanding of patterns was compared and contrasted before and after they learned the unit. To this end, a written instrument to measure students' understanding of patterns was developed on the basis of previous studies on pattern learning for lower-grade students. A total of 189 students were analyzed. As a result of the study, the overall correct answer rates in the post-test were higher in most items than those in the pre-test, illustrating the positive effect of the specific unit. However, students found it difficult to find rules in which two components would change simultaneously either in geometric or numeric patterns, find patterns that would be similar in structure, represent geometric patterns into numeric patterns, find empty terms in increasing patterns, and reason the specific terms in patterns that can be differently interpreted. Based on these research results, this study sheds light on students' understanding of patterns and suggests implications to improve their understanding.

• Journal of Korea Game Society
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• v.18 no.2
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• pp.121-130
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• 2018

Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. It prerequisites that any interesting pattern should occupy a maximum portion of the transactions it appears. But in our real world scenarios the completeness of any pattern is more likely to become various in transactions. Hence, we should also consider the problem of finding the qualified patterns with the significant values of the weighted support by completeness in order to reduce the loss of information within any pattern in transaction. In these pattern recommendation applications, patterns with higher completeness may lead to higher recall while patterns with higher completeness may lead to higher recall while patterns with higher frequency lead to higher precision. In this paper, we propose a measure of weighted support and completeness and an algorithm WSCFPM(weigted support and completeness frequent pattern mining). Our algorithm handles the invalidation of the monotone or anti-monotone property which does not hold on completeness. Extensive performance analysis show that our algorithm is very efficient and scalable for word pattern mining.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.165-171
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• 2013

Coding patterns that appeared frequently in the source code is a typical piece of code. The functionality that difficult to modularize, such as logging or synchronization processing, and the useful sentences in programming is extracted in software as codding pattern. Large-scale software could not be analyzed fully because the number of coding pattern that can be manually investigated is limited. In this paper, the characteristics of coding patterns perform the evaluation. The goal is to extract for codding-pattern to analyzed by developer. We was selected 6 indicators and performed analysis of 4 open-source. Matrix relations between the values and characteristics of the actual pattern analysis, pattern instances, the width of the distribution of instances, the pattern repeating structure of the elements included in the rates should be analyzed for patterns and indicators that help in choosing was confirmed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.29-36
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• 2009

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data mining problem with broad applications. Since a sequential pattern in DNA sequences can be a motif, we studied to find sequential patterns in DNA sequences. Most previously proposed mining algorithms follow the exact matching with a sequential pattern definition. They are not able to work in noisy environments and inaccurate data in practice. Theses problems occurs frequently in DNA sequences which is a biological data. We investigated approximate matching method to deal with those cases. Our idea is based on the observation that all occurrences of a frequent pattern can be classified into groups, which we call approximated pattern. The existing PrefixSpan algorithm can successfully find sequential patterns in a long sequence. We improved the PrefixSpan algorithm to find approximate sequential patterns. The experimental results showed that the number of repeats from the proposed method was 5 times more than that of PrefixSpan when the pattern length is 4.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.71-82
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• 2002

The software pattern is knowledge representation derived from the verified solutions or the experience of the experts. On account of the design varieties of software development, however, it is not the facilitated task to discover the best proper software pattern. This situation requires that software patterns be categorized in terms of their innate concepts. This paper proposes software pattern ontology(SPO) for the systematic categorization of software patterns by means of conceptual properties of patterns after the comparative analysis of association between software pattern and ontology. The SPO presented in this paper can establish the basis for the software pattern management system at the conceptual level. This paper also shows an idea for the application by unifying conceptual properties of software pattern and ontology. 

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.11a
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• pp.161-164
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• 2003

본 연구는 집중력, 기억력 및 학습 능력의 뇌기능 증진을 위한 의식적인 손 운동에 관련된 연구이다. 우선 효과적인 재활을 위한 손가락 운동 패턴을 연구하였다. 단순한 손가락 운동(Simple Finger Movement ; SFM) 패턴과 의식적인 손가락 운동(Intentional Finger Movement ; IFM)패턴을 비교하였다. 다음으로 각각 두 패턴 운동을 시켜 피험자의 집중력과 학습 능력의 증진을 검증하고자 한다. SFM 패턴과 IFM 패턴의 비교와 집중력과 학습 능력의 증진의 검증은 뇌파(mid $\alpha$파)를 이용하였다. 실험은 먼저 SFM 패턴의 운동을 시키고 다음에 IFM 패턴의 운동을 시키는 실험을 하였다. 결과적으로 IFM 패턴에서 mid $\alpha$ 파의 증가가 이루어졌음을 측정함으로써, IFM 패턴이 뇌의 집중력과 학습 능력을 증진시킨다는 결과를 얻었다.