• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.61-67
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• 2004

We propose a new algorithm to generate additional training patterns using the brain-style information processing algorithm, that is, supervised and unsupervised learning models. This will be useful in the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on. We adopt the independent component analysis as an unsupervised model for generating exempalr patterns and multilayer perceptions as supervised models for verifying usefulness of the generated patterns. After statistical analysis of the proposed pattern generation algorithm, we verify successful operations of our algorithm through simulation of handwritten digit recognition with various numbers of training patterns.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.11
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• pp.943-948
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• 2016

In recent times, the requirement of a micro pattern on the surface of products has been increasing, and high precision in the fabrication of the pattern is required. Hence, in this study, dual micro patterns were fabricated on a cylindrical workpiece, and deburring was performed by magnetic abrasive deburring (MAD) process. A prediction model was developed, and the MAD process was optimized using the response surface method. When the predicted values were compared with the experimental results, the average prediction error was found to be approximately 7%. Experimental verification shows fabrication of high accuracy dual micro pattern and reliability of prediction model.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.480-481
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• 2012

LED (Light Emitting Diode)는 친환경적이며 고수명 등의 여러 장점을 가지고 있어서 액정디스플레이의 광원으로 널리 사용되고 있다. 그러나 LED 제품을 제조하기 위해서는 칩, 패키지, 모듈, 시스템으로 구성된 4단계의 복잡한 제조공정을 거쳐야 하므로 가격이 높은 단점이 있다. 이를 개선하기 위해서 패키지, 모듈, 시스템의 3단계의 공정을 하나로 통합한 hybrid LED package(HLP) 개념이 제시되었다. HLP는 LED chip을 PCB에 직접 실장한 뒤 초정밀 가공 및 성형 기술을 활용하여 일체형 광학패턴을 인가함으로써 공정을 단순화하면서도 광효율을 향상시킬 수 있다. 이에 본 연구에서는 다구찌 실험계획법을 사용하여 디스플레이에서 중요시되는 휘도를 높일 수 있는 일체형광학패턴 형상 최적화를 실시하였으며, 최적화된 일체형 광학패턴을 제조하기 위한 초정밀 가공 및 성형기술을 개발하였다. 최적화 결과 높이 25um, 꼭지각 90도의 음각형태의 사각피라미드 패턴이 최적형상으로 결정되었으며, 패턴이 없을 때와 비교하여 휘도가 약 32.3% 높아지는 것으로 나타났다. 이러한 일체형 광학패턴을 제품으로 구현하기 위하여 초정밀 절삭기술을 활용하여 마스터 금형을 제작하였다. 최종적으로 사출성형을 통해 일체형 광학패턴을 제작하게 되는데 이때 사출기 내부 공기흐름 및 진공도를 최적화함으로써 패턴 내부에 불필요한 기포가 발생하지 않도록 하는데 성공하였다. 이를 통해 생산성이 높은 사출성형으로 HLP 제품을 양산할 수 있는 가능성을 확인하였고, 추후에는 실제 제품을 제작하는 연구를 수행할 예정이다.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.339-339
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• 2011

기존의 광학리소그래피방법으로는 나노크기의 패턴을 형성하는데에 있어서 많은 제약이 있으며, 사실상 수십나노크기의 패턴을 형성하는데에는 전자빔리소그래피등 새로운 패턴형성 방법이 요구되고 있다. 블록 공중합체를 이용한 나노 패턴은 서로 다른 화학적 구조를 가지는 고분자들이 공유결합으로 연결되어 있는 분자구조를 이용하여, 하나의 분자 내에 서로 다른 블록들이 상분리를 일으키려는 것과 동시에 이들의 공유결합으로 인해 그 정도가 제한되는 것을 이용하여 라멜라, 실린더, 구 등의 주기적으로 배열된 형태의 구조물을 형성하는 패터닝 기술이다. 블록 공중합체를 이용한 나노크기의 패턴 형성은 열역학적으로 안정적인 구조이며, 대면적으로 구현 할 수 있어서 차세대 소자제작을 위한 제작기술로 많은 관심을 가지고 있다. 하지만 블록공중합체를 이용한 나노패턴 기술은 선행적으로 나노구조체를 결함이 없고, 원하는 형태로 제작 할 수 있는 공정의 확립이 필요하다. 따라서 본 연구에서는, 이러한 블록 공중합체을 이용한 나노패턴을 제조하는 공정에서, 폴리스틸렌과 실리콘 산화물 박막과의 표면반응을 막기 위한 Self-Assembly Monolayers (SAMs) 처리 공정이 패턴 형성에 미치는 영향을 알아보기 위하여 MPTS의 농도 및 처리시간을 변화시켰다. 나노패턴을 분석, 확인하기 위하여 Atomic Force Microscopic (AFM)과 Field Emission Scanning Electron Microscope (FESEM)을 이용하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.175-176
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• 2016

본 논문에서는 개방형 교육시스템 학습모형을 적용한 디자인 패턴 교육을 위한 개방형 교육 시스템을 통한 학습을 제안한다. 소프트웨어 디자인 패턴은 정형화된 답이 없으며 상황에 따라 유동적으로 사용되지만, 기존의 디자인 패턴 온라인 교육 시스템은 일방적으로 이루어져 있고 시중에 판매되는 책을 통해 디자인 패턴을 이해하기에는 어려운 부분이 많이 존재한다. 따라서 이러한 문제를 해결하고자 디자인 패턴 교육을 위한 개방형 교육 시스템을 제안한다. 디자인 패턴의 개념과 사례를 통해 기본적인 지식을 습득하고 디자인 패턴의 퀴즈와 실습을 해 이해도를 높인다. 또한, 일방적인 학습이 아닌 사용자들 간의 토론을 통해 한 방향에서 디자인 패턴을 보는 것이 아닌 다양한 시점에서 볼 수 있어 창의력도 함께 증진할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.398-405
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• 2006

We propose a pattern classification model using a selective perception ability of human beings. Generally, human beings recognize an object by putting a selective concentration on it in the region of interest. Much better classification and recognition could be possible by adapting this phenomenon in pattern classification. First, the pattern classification model creates some reference cluster patterns in a usual way. Then it generates an SPM(Selective Perception Map) that reflects the mutual relation of the reference cluster patterns. In the recognition phase, the model applies the SPM as a weight for calculating the distance between an input pattern and the reference patterns. Our experiments show that the proposed classifier with the SPM acquired the better results than other approaches in pattern classification.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.75-84
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• 2005

Sequential pattern mining is an important data mining problem with broad applications. While the current methods are generating sequential patterns within a single attribute, the proposed method is able to detect them among different attributes. By incorporating these additional attributes, the sequential patterns found are richer and more informative to the user This paper proposes a new method for generating multi-dimensional sequential patterns with the use of Hellinger entropy measure. Unlike the Previously used methods, the proposed method can calculate the significance of each sequential pattern. Two theorems are proposed to reduce the computational complexity of the proposed system. The proposed method is tested on some synthesized purchase transaction databases.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.592-600
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• 2005

Systematically extracting the test patterns of hot spots in an object-oriented software framework is a prerequisite for thoroughly testing the framework's functionality in a variety of contexts in which the framework is extended for reuse. This paper proposes a method for analyzing the design patterns and extracting the test patterns from the interaction test patterns of hot spots in an object-oriented framework. Based on the design pattern of the framework's hot spot, our method captures the object behavior allowed in that hot spot by means of statecharts, which are then used to generate the interaction test patterns and test cases. The generated test patterns and test cases can be applied repeatedly to applications which are built from extending the framework.

• Korean Journal of Optics and Photonics
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• v.21 no.3
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• pp.97-102
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• 2010

A pattern density function using simulation analysis for controlling LGP output distribution was proposed. The pattern density function was found as [Pexp(-y/70)+Qexp(+y/25)]R. We analyzed the LGP output distribution of a hemi-sphere pattern using the function and then found that its output distribution was clearly improved as compared with that of the equi-distance pattern. We found that the density function works well for the pyramid pattern case as well as.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.