• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.631-640
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• 2001

Round robin scheduling discipline, which is a sort of frame-based scheduling, is quite simple and straightforward for handling multiple queues, and by putting a different weight on each queue, a network can offer differentiated services such as different bandwidth, or delay bound. The most typical algorithm among this disciplines is the weighted round robin(WRR). Also, WRR algorithm can be implemented efficiently by dynamic binary scheduling wheel(DBSW) architecture. This paper performs the analysis of the DBSW architecture and compares the results with simulation results. The analysis data and simulation data show that the DBSW structure decreases average buffer length because it is capable of maintaining the allocated weight of each VC correctly.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.435-444
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• 1999

현재까지 연구되고 있던 정보보호관련분야의 계량화방법을 좀 더 다른 방법으로 접근하여, 정보시스템 환경 하에서 보안 및 관리 운영 평가 지수에 계량화하여 1차 집단과 2차 집단간의 차이를 연구하였다. 정보화 관련항목에 대하여 빈도 분석을 적용함으로서 군별, 항목별 분류를 통한 항목 비례 가중치법을 산출하였다. 또한, 선지정 가중치법을 이용하여, 보호지수와 관리운용지수에 따른 상관관계를 조사하여 안전관리 지수를 계량화하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.2
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• pp.135-139
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• 2009

We present a new ensemble learning method that employs the set of region experts, each of which learns to handle a subset of the training data. We split the training data and generate experts for different regions in the feature space. When classifying a data, we apply a weighted voting among the experts that include the data in their region. We used ten datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as Bagging and Adaboost. We used SMO, Naive Bayes and C4.5 as base learning algorithms. As a result, we found that the performance of our method is comparable to that of Adaboost and Bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.

• Proceedings of the Korea Database Society Conference
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• 2000.11a
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• pp.101-108
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• 2000

기업에서 보유한 전문 지식 정보가 급속도로 증가함에 따라 대량의 문서에 저장된 지식 정보를 효과적으로 탐색하여 기업 경영에 활용하기 위한 지식경영시스템 도입이 확산되고 있다. 이러한 지식경영시스템에서 핵심적인 구성 요소는 전문 분야의 지식 정보를 체계적으로 분류하고 효율적으로 검색하기 위한 지식 탐사 기법이다. 본 논문에서는 데이타마이닝 기법을 이용하여 문서를 자동적으로 분류하기 위한 새로운 모델을 제안하였다. 연관 규칙 탐사 알고리즘을 이용하여 학습용 문서 집합으로부터 세부 분야를 대표하는 색인어 집합을 구성하였다. 세부 분야별 색인어 집합에 대하여 전체 문서에 대한 비중에 따라 가중치 배열을 구성하여 문서를 자동으로 분류하기 위한 기준으로 삼았다. 임의의 문서를 자동적으로 분류하는 실험을 통하여 제안된 방법의 효율성을 검정하였다.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1474-1482
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• 2004

In this paper, we propose an approach for extracting and filtering block motion vectors using an adaptive weight function. We first extract motion vectors from a sequence of images by using size-varibale block matching and then process them by adaptive robust estimation to filter out outliers (motion vectors out of concern). The proposed adaptive robust estimation defines a continuous sigmoid weight function. It then adaptively tunes the sigmoid function to its hard-limit as the residual errors between the model and input data are decreased, so that we can effectively separate non-outliers (motion vectors of concern) from outliers with the finally tuned hard-limit of the weight function. The experimental results show that the suggested approach is very effective in filtering block motion vectors.

• Korean Journal of Cognitive Science
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• v.7 no.1
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• pp.5-35
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• 1996

This paper addresses an one-pass parallel thinning algorithm which shows effectiveness in both accuracy and speed. The proposed method is based on parallel iterative boundary removal.Image connectivity are preseved and the algorithms performance is compared to other algorithms especially to parallel thinning algorithm which is the best parallel algorithm have been proposed.Evaluation result shows that the proposed algorithm compare favorably to others.The result shows exact thinning free from one pixel boundary noise and free from distortion of shape.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.261-270
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• 2006

Positional data of moving objects can be regularly sampled in order to minimize the cost of data collection in LBS. Since position data which are regularly sampled cannot include the changes of position occurred between sampling periods, sampled position data differ from the data predicted by a time parameterized linear function. Uncertain position data caused by these differences make the accuracy of the range queries for present positions diminish in the TPR tree. In this paper, we propose the uncertainty region to handle the range queries for uncertain position data. The uncertainty region is defined by the position data predicted by the time parameterized linear function and the estimated uncertainty error. We also present the weighted recent uncertainty error policy and the kalman filter policy to estimate the uncertainty error. For performance test, the query processor based by the uncertainty region is implemented in the TPR tree. The experiments show that the Proposed query processing methods are more accurate than the existing method by 15%.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.227-237
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• 2000

A Web database System is a large-scaled multimedia application system that has multimedia processing facilities and cooperates with relational/Object-Oriented DBMS. Conventional hypertext modeling methods and DB gateway have limitations for Web database because of their restricted versatile presentation abilities and inefficient concurrency control caused by bottleneck in cooperation processing. Thus, we suggest a Dynamic Navigation Model & Virtual Graph Structure. The Dynamic Navigation Model supports implicit query processing and dynamic creation of navigation spaces, and introduce node-link creation rule considering navigation styles. We propose a mapping methodology between the suggested hypertext model and the relational data model, and suggest a dynamic allocation scheduling technique for query processing server based on weighted value. We show that the proposed technique enhances the retrieval performance of Web database systems in processing complex queries concurrently.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.205-214
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• 1997

In this paper, we suggest a cache mechanism to improve the speed fo query processing in distributed directory environment. For this, request and result and result about objects in remote site are store in the cache of local site. A cache mechanism developed through six phases; 1) Cached information which stored in distributed directory system is classified as application data, system data and meta data. 2) Cache system architecture is designed according to classified information. 3) Cache schema are designed for each cache information. 4) Least-TTL algorithms which use the weighted value of geograpical information and access frquency for replacements are developed for datacaches(application cache, system cache). 5) Operational algorithms are developed for meta data cache which has meta data tree. This tree is based on the information of past queries and improves the speed ofquery processing by reducing the scope of search space. 6) Finally, performance evaluations are performed by comparing with proposed cache mechanism and other mechanisms.