• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.918-922
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• 2009

Naturally, block mapping FTL works well for sequential writes while page mapping FTL does well for random writes. To exploit their advantages, a practical FTL should be able to selectively apply a suitable scheme between page and block mappings for each write pattern. To meet that requirement, we propose a hybrid mapping FTL, which we call Janus-FTL, that distributes data to either block or page mapping areas. Also, we propose the fusion operation to relocate the data from block mapping area to page mapping area and the defusion operation to relocate the data from page mapping area to block mapping area. And experimental results of Janus-FTL show performance improvement of maximum 50% than other hybrid mapping FTLs.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1017-1024
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• 2004

Ubiquitous system requires intelligent environment and system that perceives context in a proactive manner. This paper describes proactive retrieval method using context patterns in ubiquitous computing. And as the user's contexts change, new information is delivered proactively based on user's context patterns. For proactive retrieval, we extract context patterns based on sequential pattern discovery and association rule in data mining. By storing only information to be needed in near future using the context patterns, we solved the problem of speed and storage capacity of mobile devices in ubiquitous computing. We explain algorithms and an example. Several experiments are performed and the experimental results show that our method has a good information retrieval.

• Convergence Security Journal
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• v.14 no.5
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• pp.17-24
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• 2014

This study proposed LDP (Local Directional Pattern) as a new local micro pattern for facial expression recognition to solve noise sensitive problem of LBP (Local Binary Pattern). The proposed method extracts 8-directional components using $m{\times}m$ mask to solve LBP's problem and choose biggest k components, each chosen component marked with 1 as a bit, otherwise 0. Finally, generates a pattern code with bit sequence as 8-directional components. The result shows better performance of rotation and noise adaptation. Also, a new local facial feature can be developed to present both PFF (permanent Facial Feature) and TFF (Transient Facial Feature) based on the proposed method.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.49-63
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• 2009

The performance of spatio-temporal moving pattern mining depends on how to analyze and process the huge set of spatio-temporal data due to the nature of it. The several method was presented in order to solve the problems in which existing spatio-temporal moving pattern mining methods[1-10] have, such as increasing execution time and required memory size during the pattern mining, but they did not solve properly yet. Thus, we proposed the STMP/MST method[11] as a preceding research in order to extract effectively sequential and/or periodical frequent occurrence moving patterns from the huge set of spatio-temporal moving data. The proposed method reduces patterns mining execution time, using the moving sequence tree based on hash tree. And also, to minimize the required memory space, it generalizes detailed historical data including spatio-temporal attributes into the real world scopes of space and time by using spatio-temporal concept hierarchy. In this paper, in order to verify the effectiveness of the STMP/MST method, we compared and analyzed performance with existing spatio-temporal moving pattern mining methods based on the quantity of mining data and minimum support factor.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.328-330
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• 2002

본 논문에서는 패턴이나 음성데이터와 같이 순차적 데이터론 인식하는데 널리 사용되어온 모델로서, 일련의 순차적인 성질을 내포하고있는 데이터를 다루는 문제에 적합하다고 할 수 있는 HMM을 이용하여 정보추출 문제를 다룬다. 기본적으로는 통상적인 HMM 사용법을 따르나 모델의 구조를 정함에 있어서 HMM을 사용할 때는 주로 목적에 맞는 HMM의 구조를 수동으로 구성하고 모델 내부의 확률 파라미터 값을 학습시켰던 데 반해, 본 논문에서는 데이터의 전처리 정보를 이용하여 초기에 추상적으로 설정한 모델이 학습을 통해서 점차 구체화되어 가는 자기 구성 은닉마르코프 모델(5-HMM)을 제시하여 사용한다. 제시된 방법은 CFP(Call for Paper)등의 텍스트 데이터에 더만 실험에서 기존 방식을 사용한 HMM보다 향상된 결과를 보여준다.

• Spatial Information Research
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• v.2 no.2
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• pp.175-188
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• 1994

A competitive learning network was proposed as unsupervised training method of remote sensing data, Its performance and computational re¬quirements were compared with conventional clustering techniques such as Se¬quential and K - Means. An airborne remote sensing data set was used to study the performance of these classifiers. The proposed algorithm required a little more computational time than the conventional techniques. However, the perform¬ance of competitive learning network algorithm was found to be slightly more than those of Sequential and K - Means clustering techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2125-2128
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• 2003

알려진 공격 방법에 대해서는 다양한 방법으로 공격을 탐지하여 적절한 대응을 할 수 있는 반면 알려지지 않은 방법에 의한 공격은 침입탐지 시스템에서 공격 자체를 인식하지 못하므로 적절한 대응을 할 수 없게 된다. 따라서 비정상행위에 대한 탐지를 위해 데이터마이닝 기술을 이용하여 새로운 유형의 공격을 추출하고자 하였다. 특히 대용량의 데이터에 공통적으로 나타나는 순차적인 패턴을 찾는 순차분석 기법 중 PrefixSpan알고리즘을 적용하여 비정상 행위 공격을 탐지할 수 있는 방법을 제시하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.6
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• pp.650-662
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• 1989

In this paper, a new algorithm for feature extraction and classification of recognizing Hanguel patterns is proposed. Inputed patterns classify into six basic formal patterns and divided into subregion of Hanguel phoneme and extract the crook feature from position information of the each subregion. Hanguel patterns are defined and are made of the indexed-sequence file using these crook features points. Hanguel patterns are recognized by retrievignt ehses two files such as feature indexed-sequence file and standard dictionary file. Thi paper show that the algorithm is very simple and easily construct the software system. Experimental result presents the output of feature extraction and grouping of input patterns. Proposed algorithm extract the crooked feature using distance transformation method within the rectangle of enclosure the characters. That uses the informationof relative position feature. It represents the 97% of recognition ratio.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.327-332
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• 2007

버퍼캐시에서 선반입은 교체정책과 함께 중요한 성능 향상 기법 중의 하나이다. 하지만 참조 패턴의 특성에 따라서는 선반입을 수행하면 오히려 전체 수행시간을 증가시키는 경우도 보고된 바 있다. 본 논문에서는 참조 패턴을 탐지하고 탐지된 패턴에 적절히 대응하여, 선반입의 이익은 유지하되 성능에 악영향을 미치지 않는 선반입 기법으로 순환 참조 선반입을 제안한다. 성능 평가를 위해서 리눅스에서 현재 사용되고 있는 미리 읽기 선반입과 순환 참조 선반입의 수행 시간을 비교했다. 다양한 참조 패턴을 가지는 트레이스들에 대한 시뮬레이션 성능 평가 결과, 순차 참조를 많이 포함하는 트레이스에 대해서는 순환참조 선반입이 리눅스의 미리 읽기 선반입과 유사한 정도의 $3\sim5%$ 성능향상을 보였다. 뿐만 아니라, 미리 읽기 선반입 정책을 적용했을 때 오히려 40% 가량의 성능 악화를 초래하는 특정 트레이스에 대해서도 순환 참조 선반입을 적용할 경우 0.07%의 아주 미미한 성능 저하만을 유발하였다. 본 연구에서 제안하는 순환 참조 선반입 기법은 이득이 있을 때만 적극적인 선반입을 수행하여 시스템 성능을 향상시키며, 손해가 발생할 때는 선반입을 중지하여 시스템 성능 악화를 방지함을 실험을 통해 알 수 있다.