• The Transactions of the Korea Information Processing Society
• /
• v.2 no.3
• /
• pp.425-432
• /
• 1995

In this paper we propose a design and implementation technique of a universal automatic test pattern generator(UATPG) which is well suited for VLSI digital circuits. UATPG is designed to extend the capabilities of the existing APTG and to provide a convenient environment to computer-aided design(CAD) users. We employ heuristic techniques in line justification and fault propagation for functional gates during test pattern generation for a target fault. In addition, the flip-flops associated with design for testability (DFT) are exploited for pseudo PIs and pseudo POs to enhance the testabilities of VLSI circuits. As a result, UATPG shows a good enhancement in convenient usage and performance.

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

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04b
• /
• pp.256-258
• /
• 2004

단백질간의 상호작용에 대한 연구는 생물학적 프로세스를 이해하기 위해 중요한 부분이다. 이러한 단백질간의 상호작용에 대한 정보는 주로 생명과학 관련 연구논문에 존재하지만 컴퓨터로 자동으로 처리하여 상호작용에 관안 정보를 추출할 수 있기 위해서는 텍스트 마이닝 기술이 적용되어야 한다 바이오 텍스트 마이닝에서 대두되고 있는 중요한 쟁점은 대용량의 연구논문에서 필요한 정보를 어떻게 효율적으로 정확하게 추출할 것인가에 대한 내용이다. 또한, 관심이 있는 단백질의 종류나 관련성을 표시하는 문장내 패턴의 다양성을 수용하기 위하여 개발하는 시스템의 확장성을 높이는 것도 소프트웨어 공학적인 측면에서 중요한 이슈이다 이 논문의 목적은 생물학적 내용을 담고 있는 연구논문으로부터 단백질간의 상호작용을 추출하는 확장성을 가진 텍스트 마이닝 기법을 제안하는데 있다.

• The KIPS Transactions:PartB
• /
• v.19B no.4
• /
• pp.209-220
• /
• 2012

In this paper, we propose an adaptive pavement region detection method that is robust to changes of structural patterns in a natural scene. In order to segment out a pavement reliably, we propose two step approaches. We first detect the borderline of a pavement and separate out the candidate region of a pavement using VRays. The VRays are straight lines starting from a vanishing point. They split out the candidate region that includes the pavement in a radial shape. Once the candidate region is found, we next employ the adaptive multi-seed region growing(A-MSRG) method within the candidate region. The A-MSRG method segments out the pavement region very accurately by growing seed regions. The number of seed regions are to be determined adaptively depending on the encountered situation. We prove the effectiveness of our approach by comparing its performance against the performances of seed region growing(SRG) approach and multi-seed region growing(MSRG) approach in terms of the false detection rate.

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10a
• /
• pp.337-339
• /
• 1999

최근 시계열 데이터에서 유사한 패턴을 탐색하는 기법이 다양한 응용분야에서 중요한 연구 주제로 자리잡고 있다. 본 논문에서는 시계열의 트랜드를 정의하고 유사한 트랜드를 가지 시계열을 찾음으로써 유사성의 개념을 좀 더 확장, 발전시켰다. 즉, 시계열에서의 트렌드를 두 개의 이동 평균 선의 관계를 통해 정의함으로써 두 시계열 간의 거리만으로 유사도를 측정했던 기존 연구와는 달리 좀 더 패턴을 가진 수열들을 찾고 이것을 기존의 DFT방법을 이용하여 대용량의 시계열 데이터베이스에서 사용자가 정의한 임계치 이하로 차이가 나는 시계열에 대해 유사 시계열로서 최종적으로 검색하게 된다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.107-108
• /
• 2009

본 논문은 다중 프로세서에서의 다양한 실시간 스케줄링 기법을 지원하기 위해 객체 지향적으로 설계된 시뮬레이터를 제안한다. 다중 프로세서 기반 실시간 스케줄링의 특징을 반영한 설계 원칙에 따라 디자인 패턴을 적용하여 시뮬레이터의 재사용성과 확장성을 높였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10e
• /
• pp.184-186
• /
• 2002

대규모 웹 캐시 서버의 자원 이용도는 네트워크와 디스크 I／O 대기 시간에 주로 의존하고 또한 작업 부하 패턴에 있어 네트웍 사용이 폭주하는 시간과 새벽과 같은 한가한 시간간의 변동성이 심하다. 따라서, 한정된 자원범위에서 최상의 서비스를 제공키위해서는 절정기 동안 자원 이용도를 낮추고 이들 작업부하를 비절정기 때에 나누어 수행토록 함으로써 자원 활용도를 최대로 끌어 올리자는데, 연구의 목적이 있다 이를 위해 비절정기 동안 캐시압축 기법을 이용하여 디스크 입출력 작업을 미래예측 기법은 어느 점에서의 실제 작업 세트가 작았다는 것과 페이지 재사용 패턴의 정확한 예측은 물리적 메모리 크기의 캐시에서 높은 히트율을 생산할 것이라는 점을 보여주었다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.04b
• /
• pp.460-462
• /
• 2001

인터넷 사용자가 급증하고, 인터넷을 통한 비즈니스에 수익 모델에 대한 관심이 높아지면서 방문자별로 맞춤 정보를 제공하는 퍼스널라이제이션이 인터넷 개발자 및 사용자들의 관심을 모으고 있다. 이러한 퍼스널라이제이션을 위해서 전처리과정인 사용자 프로파일 생성과정을 확장된 웹 로그 처리 시스템을 통해서 구현해본다. 웹사이트 서버의 확장된 이벤트 처리, 즉 사용자의 행위정보를 로그에 포함시켜 로그정보를 웹 로그 서버에 전송하도록 설계하였다. 그리고 이 웹 로그 정보를 쉽게 분석할 수 있다. 이때 데이터베이스 저장 기술로 OLE DB Provider상에서 수행되는 ADO 기술을 사용함으로써 확장된 웹 로그 처리 시스템을 설계하였다. 확장된 웹 로그 DB를 패턴분석, 군집분석 등의 마이닝(Mining) 기법을 통하여 맞춤 서비스에 대한 사용자 프로파일을 구축할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.6
• /
• pp.58-67
• /
• 2009

A correspondence matching is one of the important tasks in computer vision, and it is not easy to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. A SURF(Speeded Up Robust Features) algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform) with closely maintaining the matching performance. However, because SURF considers only gray image and local geometric information, it is difficult to match corresponding points on the image where similar local patterns are scattered. In order to solve this problem, this paper proposes an extended SURF algorithm that uses the invariant color and global geometric information. The proposed algorithm can improves the matching performance since the color information and global geometric information is used to discriminate similar patterns. In this paper, the superiority of the proposed algorithm is proved by experiments that it is compared with conventional methods on the image where an illumination and a view point are changed and similar patterns exist.

• The KIPS Transactions:PartD
• /
• v.17D no.4
• /
• pp.253-258
• /
• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.