• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 춘계학술대회 논문집
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• pp.1381-1385
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• 2004

This paper presents a modelling methodology using fuzzy logic and train performance simulation for determining an economical running pattern for a high speed train which minimizes energy consumption under an given trip margin. The economical running pattern is defined with an economical maximum speed in traction phase, a speed at the end of coasting. As a case study, the simulation is carried out for an economical run of korea high speed train prototype, and the results of fuzzy model described.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.141-146
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• 2005

Ensemble method has been known as one of the most powerful classification tools that can improve prediction accuracy. Ensemble method also has been understood as ‘perturb and combine’ strategy. Many studies have tried to develop ensemble methods by improving perturbation. In this paper, we propose two new ensemble methods that improve combining, based on the idea of pattern matching. In the experiment with simulation data and with real dataset, the proposed ensemble methods peformed better than bagging. The proposed ensemble methods give the most accurate prediction when the pruned tree was used as the base learner.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2011년도 추계학술대회
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• pp.557-561
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• 2011

The study proposes three types of production lead time according to the production or demand pattern. First of all, it discusses the difference of three lead times. While pitch time and cycle time are used in push system with process stock and mass conveyor production, the tact time is used in pull system like as JIT based lean production system.

• 복식문화연구
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• 제12권1호
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• pp.59-72
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• 2004

The objectives of this study were to investigate the perceptions of Korean female university students for clothing pattern tendency and structural element of clothing pattern image dimension and to find how individual personality type influence the preferred clothing pattern characteristics. For this study, a questionnaire was designed and sent to 600 female university students of Daejeon, Seoul and metropolitan area. The tool used in this study was MBTI(The Myers-Briggs Type Indicator) Form G Korean version and for the analysis of data SPSS 10.0 package were used. 10 representative patterns for this study were floral, dot, stripe, check, animal, abstract & artistic, geometric, vegetable & leaf, paisely, patchwork pattern. The data were analyzed by factor analysis, arithmetic mean, One-Way ANOVA, x²-test. The major findings were as follows: Clothing pattern image dimension perceived by Korean female university students for 10 representative patterns were basic form, deluxe, specialty, and cultural dimension. Among them, basic form and deluxe dimension were major dimensions. In basic form dimension, dot pattern score was high indicating female students perceive it as light, comfortable, clean, cool and simple pattern image. In deluxe dimension, floral pattern scored high and in specialty dimension, abstract and artistic pattern scored high among other pattern image. In cultural dimension, geometric pattern and check pattern scored high. Based on other detailed analysis results, It is concluded that the personality type greatly influence clothing pattern evaluation. For example, in case of color combination of patchwork pattern, there was a difference in color preference depend on a personality type such as sensing(S) or intuition(N). Therefore, sensing personality type preferred adjacent color combination than contrast color combination. Detailed marketing strategy is necessary in planning textile design of merchandise plan.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• 유통과학연구
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• 제13권6호
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• pp.17-23
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• 2015

Purpose - Transnational corporations (TNCs) have influenced drastic changes (financial services, manufacturing, labor, technology transfer) in Central Eastern Europe (CEE). This paper examines the indirect changes in the CEE pattern of industrial development and market distribution. Research design, data, and methodology - Over 25 years, neighboring (or rival) countries competed to attract TNCs as a double-edged strategy for privatization and debt reduction. Through their experience attracting foreign direct investment (FDI), many countries started to reflect aspects of national capitalism. Countries also began to realize in 2010 that TNCs sought to enter markets with more favorable conditions for export-oriented manufacturing. Results - The analysis reveals that TNC investment strategies were aimed at eliminating local competition to acquire industrial "brown fields" to convert into "green fields." CEE countries have since strengthened their national systems and the support of large-scale state-owned enterprises and small and medium-sized start-up enterprises. Conclusions - CEE has changed based on industrial development and a regional structure of TNC market distribution and associated government policies. The pattern toward flexible markets gives countries the ability to further their economies.

• Korea-Australia Rheology Journal
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• 제19권2호
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• pp.67-73
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• 2007

A finite volume method (FVM) base on the SIMPLE algorithm as the pressure correction strategy and the traditional staggered mesh is used to investigate steady, fully developed flow of Oldroyd-3-constant fluids through a duct with square cross-section. Both effects of the two viscoelastic material parameters, We and ${\mu}$, on pattern and strength of the secondary flow are investigated. An amusing sixteen vortices pattern of the secondary flow, which has never been reported, is shown in the present work. The reason for the changes of the pattern and strength of the secondary flow is discussed carefully. We found that it is variation of second normal stress difference that causes the changes of the pattern and strength of the secondary flow.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 정기총회 및 창립40주년기념 학술대회 학회본부
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• pp.347-350
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• 1987

This paper is proposed the optimal switching pattern of sleeted harmonic elimination (SHE PWM). It defined harmonic elimination band (HBE) to find the solution of Constant Voltage (CV), and sought all solutions which we are included HEB. Then, it calculated generalised klirr factor (GKF) by this solution and decided optimal switching pattern, used as initial conditions of newton raphson (NR) method to decide switching pattern at variable voltage (W). This strategy is applied to 1HP three phase induction motor. From the result, the validity of theoretical proposition can be verified.

• 산업기술연구
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• 제25권A호
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• pp.163-173
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• 2005

Recent sequential pattern mining algorithms mine all of the frequent sequences satisfying a minimum support threshold in a large database. However, when a frequent sequence becomes very long, such mining will generate an explosive number of frequent sequence, which is prohibitively expensive in time. In this paper, we proposed a novel sequential pattern algorithm using only closed frequent sequences which are small subset of very large frequent sequences. Our algorithm extends the sequence by depth-first search strategy with effective pruning. Using bitmap representation of underlying databases, we can obtain a closed frequent sequence considerably faster than the currently reported methods.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.