• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.5A
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• pp.863-872
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• 2001

본 논문에서는 PNNI(Private Network to Network Interface) 기반의 무선 ATM 망에서 이웃(neighborhood)과 이동 단말기의 위치 관계를 고려하여 계층화된 위치 등록기에 의한 적응적 위치 관리 기법을 제시한다. 제안된 위치 추적 과정에서는 단말기의 위치 영역을 홈(home) 이웃과 외부(foreign) 이웃으로 구분하고, 홈 이웃에서는 최하위 레벨의 위치 등록기와 홈 위치 등록기에 단말기의 위치를 등록하고 외부 이웃에서는 각 계층의 위치 등록기에 위치를 등록한다. 착신 단말기, 발신 단말기와 착신측 홈 이웃의 위치 관계를 고려하여 최소한의 비용으로 위치 파악 및 호 설정 과정을 수행한다. 그리고, PNNI 기반의 무선 ATM 망에서 제안된 적응적 위치 관리 기법을 기존 LR(Location Registers) 기법과 위치 관리비용 측면에서 성능을 비교하여 우수한 성능을 가지는 것을 알 수 있다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.588-594
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• 2005

사례기반추론(Case-Based Reasoning)은 다양한 예측 문제에 있어서 성공적으로 활용되고 있는 데이터마이닝 기법 중 하나이다. 사례기반추론 시스템의 예측 성능은 예측에 사용되는 최근접이웃(Nearest Neighbor)을 어떻게 설정하느냐에 따라 영향을 받게 된다. 따라서 최근접 이웃을 결정짓는 k 값의 설정은 성공적인 사례기반추론 시스템을 구축하기 위한 중요 요인 중 하나가 된다. 최근접 이웃의 설정에 있어서 대부분의 선행 연구들은 고정된 k 값을 사용하는 사례기반추론 시스템은 k 값을 크게 설정할 경우 최근접 이웃 안에 주어진 오류를 일으킬 수 있으며, k 값이 작게 설정된 경우에는 유사 사례 중 일부만을 예측에 사용하기 때문에 예측 결과의 왜곡을 초래할 수 있다. 본 이웃을 결정함에 있어서 Similarity Threshold를 이용하는 s-NN 방법을 제안하였다. 본 연구의 실험을 위해 UCI(University of california, Irvine) Machine Learning Repository에서 제공하는 두 개의 신용 데이터 셋을 사용하였으며, 실험 결과 s-NN 적용한 CBR 모델이 고정된 k 값을 적용한 전통적인 CBR 모델보다 더 우수한 성능을 보여주었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2533-2540
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• 2013

In multihop cellular network environments, the mobility of nodes is a major obstacle to find a reliable routing path between a mobile node (MN) and the access node (AN). Therefore, in this paper, we propose a fast and reliable neighbor discovery protocol that enables the fast and reliable neighbor discovery by considering the node mobility in the multihop cellular network. The proposed neighbor discovery protocol inhibits the transmission of unnecessary control messages to quickly find a suitable neighbor node (NN) and performs a priority-based access control to transmit control messages without collision in the order of NN desirable to be selected. Simulation results show that the proposed neighbor discovery protocol can discover the NNs faster than the conventional scheme and select a more reliable relay node although the number of neighbor nodes increases and the node mobility increases.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.387-397
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• 2008

Recently many researches on data analysis using spatial statistics have been studied in various field and the studies on small area estimations using spatial statistics are in actively progress. In analysis of lattice data, defining the neighborhood information system is the most crucial procedure because it also determines the result of the analysis. However the used neighborhood informal ion system is generally defined by sharing the common border lines of small areas. In this paper the other neighborhood information systems are introduced and those systems are compared with Moran's I statistic and for the comparisons, Economic Active Population Survey (2001) is used.

• Journal of Software Engineering Society
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• v.24 no.2
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• pp.35-45
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• 2011

Simulated annealing has been studied a long times. And it is one of the effective techniques for test data generation. But basic SA methods showed bad performance because of neighborhood selection strategies in the case of large input domain. To overcome this limitation, we propose new neighborhood selection approach, Branch Distance. We performs case studies based on the proposed approach to evaluate it's performance and to compare it whit basic SA and Random test generation. The results of the case studies appear that proposed approach show better performance than the other approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1105-1112
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• 2020

One-dimensional 3-neighborhood Cellular Automata (CA)-based pseudo-random number generators are widely applied in generating test patterns to evaluate system performance and generating key sequence generators in cryptographic systems. In this paper, in order to design a CA-based key sequence generator that can generate more complex and confusing sequences, we study a one-dimensional symmetric 5-neighborhood CA that expands to five neighbors affecting the state transition of each cell. In particular, we propose an n-cell one-dimensional symmetric 5-neighborhood CA synthesis algorithm using the algebraic method that uses the Krylov matrix and the one-dimensional 90/150 CA synthesis algorithm proposed by Cho et al. [6].

• The Korean Publising Journal, Monthly
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• s.8
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• pp.14-15
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• 1987

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.809-818
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• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.164-172
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• 2004

For a local search algorithm to find a bettor quality solution it is required to generate and evaluate a sufficiently large number of candidate solutions as neighbors at each iteration, demanding quite an amount of CPU time. This paper presents a method of selectively generating only good-looking candidate neighbors, so that the number of neighbors can be kept low to improve the efficiency of search. In our method, a newly generated candidate solution is probabilistically selected to become a neighbor based on the quality estimation determined heuristically by a very simple evaluation of the generated candidate. Experimental results on the problem of load balancing for production scheduling have shown that our candidate selection method outperforms other random or greedy selection methods in terms of solution quality given the same amount of CPU time.