• 한국정보과학회논문지:데이타베이스
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• 제34권3호
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• pp.217-231
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• 2007

이동 컴퓨팅(Mobile Computing)환경에서 빈번한 접속 단절은 클라이언트의 캐시 일관성(Consistency)문제로 직결된다. 이러한 캐시 일관성 문제를 해결하기 위해 무효화 보고(Invalidation Report)에 관한 연구가 진행되고 있다. 그러나 기존의 무효화 보고 기법은 서버 측의 데이타의 수가 많아지거나, 갱신(Update)되는 데이타의 양이 증가하게 되면 무효화 보고 기법의 크기의 증가 및 캐시 효율성의 저하를 가지고 온다. 또한 캐시 전체의 무효화를 확인하는 보고 기법은 선택적 청취(Selective tuning)를 지원하지 못한다. 본 논문에서는 이러한 기존 방식의 문제점을 해결하며 효율성을 개선한 무효화 보고 기법으로써 TTCI(Timestamp Tree-based Cache Invalidation scheme)를 제안한다. 갱신된 데이타의 타임스탬프를 이용하여 타임스탬프 트리를 구성하고 데이타의 아이디를 갱신된 순서대로 나열하여 무효화 보고를 만든다. 이를 활용하게 되면 각 클라이언트는 자신의 단절(Disconnect)된 시점에 맞는 정보를 확인하여 캐시의 이용률을 증가 시킬 수 있다. 또한 트리 구조를 이용하여 선택적 청취를 가능하게 함으로써 클라이언트의 자원 소비를 줄일 수 있다. 이러한 본 구조의 효율성을 검증하기 위해 기존의 기법인 DRCI(Dual-Report Cache Invalidation)와 성능비교를 실시한다.

• 한국컴퓨터정보학회논문지
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• 제29권6호
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• pp.113-120
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• 2024

정보통신 기술이 발전함에 따라 우리는 일상에서 다양한 형태의 데이터를 손쉽게 생성하고 있다. 이처럼 방대한 데이터를 효율적으로 관리하려면, 체계적인 카테고리별 분류가 필수적이다. 효율적인 검색과 탐색을 위해서 데이터는 트리 형태의 계층적 구조인 범주 트리로 조직화되는데, 이는 뉴스 웹사이트나 위키피디아에서 자주 볼 수 있는 구조이다. 이에 따라 방대한 양의 문서를 범주 트리의 단말 노드로 분류하는 다양한 기법들이 제안되었다. 그러나 범주 트리를 대상으로 하는 문서 분류기법들은 범주 트리의 높이가 증가할수록 단말 노드의 수가 기하급수적으로 늘어나고 루트 노드부터 단말 노드까지의 길이가 길어져서 오분류 가능성이 증가하며, 결국 분류 정확도의 저하로 이어진다. 그러므로 본 연구에서는 사용자의 요구 분류 정확도를 만족시키면서 세분화된 분류를 구현할 수 있는 새로운 노드 확장 기반 분류 알고리즘을 제안한다. 제안 기법은 탐욕적 접근법을 활용하여 높은 분류정확도를 갖는 노드를 우선적으로 확장함으로써, 범주 트리의 분류 정확도를 극대화한다. 실데이터를 이용한 실험 결과는 제안 기법이 단순 방법보다 향상된 성능을 제공함을 입증한다.

• Journal of Information Technology Applications and Management
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• 제15권4호
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• pp.21-36
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• 2008

As the great disturbance from abusing credit cards in Korea becomes stabilized, credit card companies need to interpret credit-card delinquents classification models from the viewpoint of profit. However, hit ratio which has been used as a measure of goodness of classification models just tells us how much correctly they classified rather than how much profits can be obtained as a result of using classification models. In this research, we tried to develop a new utility-based measure from the viewpoint of profit and then used this new measure to analyze two classification models(Neural Networks and Decision Tree models). We found that the hit ratio of neural model is higher than that of decision tree model, but the utility value of decision tree model is higher than that of neural model. This experiment shows the importance of utility based measure for credit-card delinquents classification models. We expect this new measure will contribute to increasing profits of credit card companies.

• 환경생물
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• 제21권3호
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• pp.251-256
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• 2003

내설악의 5개 전나무 고목림에서 고사목의 형태, 수종, 축적 등에 관한 다양한 기본자료를 수집하였다. 비록 전나무 고목림에 한정된 조사였으나 국외에서 일반적으로 알려진 고사목에 관한 각종 자료수집이 가능하였다. 고사목의 수종은 전나무 외에도 기타 활엽수종이 약간 나타났으며, 고사목의 형태는 일반적으로 알려진 모든 형태가 두루 나타났다. 고사목의 축적은 평균 60.42 $\textrm{m}^2 \; ha^{-1}$로 나타났으며, CWD와 고사목의 분포는 서로 관련이 없어 보인다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 하계학술대회 논문집
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• pp.319-322
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• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 I
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• pp.374-377
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• 2003

In this paper, a performance evaluation model of the Fat-Tree network with the multiple-buffered crossbar switches is proposed and examined. Buffered switch technique is well known to solve the data collision problem in the switch network The proposed evaluation model is developed by investigating the transfer patterns of data packets in a switch with output-buffers. Steady state probability concept is used to simplify the analyzing processes. Two important parameters of the network performance, throughput and delay, are then evaluated. To validate the proposed analysis model, the simulation is carried out on the various sizes of Fat-tree networks that use the multiple a$\times$b buffered crossbar switches. It is observed that both analysis and simulation results are match closely.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.157-159
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• 2007

The conventional hard disk has been the dominant database storage system for over 25 years. Recently, hybrid systems which incorporate the advantages of flash memory into the conventional hard disks are considered to be the next dominant storage systems to support databases for desktops and server computers. Their features are satisfying the requirements like enhanced data I/O, energy consumption and reduced boot time, and they are sufficient to hybrid storage systems as major database storages. However, we need to improve traditional index node management schemes based on B-Tree due to the relatively slow characteristics of hard disk operations, as compared to flash memory. In order to achieve this goal, we propose a new index node management scheme called FNC-Tree. FNC-Tree-based index node management enhanced search and update performance by caching data objects in unused free area of flash leaf nodes to reduce slow hard disk I/Os in index access processes.

• 환경영향평가
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• 제19권5호
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• pp.511-525
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• 2010

Understanding of the hydraulics of flow over vegetation is very important to support the management of fluvial processes. The objective of this study is to assess the effects of hydraulic influence by tree planting in a compound channel with vegetated floodplain. This study analyzes the influence of tree planting on hydraulic features in Young-river in Munkyung city using HEC-RAS and RMA-2 model. The study results showed that there is a rise in water surface elevation and decrease in velocity near vegetated area. It is also ascertained that only negligible effects was seen within the feasible range of freeboard for the existing levees. However, as hydraulic features can vary depending on the aspect of flood inundation during each flood period, it is necessary to accumulate data through continuous data collecting.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.247-249
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• 2007

Crown parameters are important roles in tree species identification, because the canopy is the aggregate of all the crowns. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify and classify single tree species. In this paper, shape parameters derived from pixel-based crown area measurements and texture features derived from GLCM parameters in QuickBird image were tested and compared for individual tree species identification. As expected, initial studies have shown that the crown parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees within the compartment(less than forest stand) for single extraction from spaceborne high resolution image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.