• Journal of applied mathematics & informatics
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• 제9권1호
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• pp.167-183
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• 2002

A modified discretization ABS algorithm for solving a class of singular nonlinear systems, F($\chi$)=0, where $\chi$, F $\in$ $R^n$, is presented, constructed by combining a discretization ABS algorithm arid a method of Hoy and Schwetlick (1990). The second order differential operation of F at a point is not required to be calculated directly in this algorithm. Q-quadratic convergence of this algorithm is given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.872-885
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• 2015

The rapid increase of smart mobile devices and mobile applications has led to explosive growth of data traffic in cellular network. Offloading data traffic becomes one of the most urgent technical problems. Recent work has proposed to exploit opportunistic communications to offload cellular traffic for mobile data dissemination services, especially for accepting large delayed data. The basic idea is to deliver the data to only part of subscribers (called target-nodes) via the cellular network, and allow target-nodes to disseminate the data through opportunistic communications. Human mobility shows temporal and spatial characteristics and predictability, which can be used as effective guidance efficient opportunistic communication. Therefore, based on the regularity of human mobility we propose NodeRank algorithm which uses the encounter characteristics between nodes to choose target nodes. Different from the existing work which only using encounter frequency, NodeRank algorithm combined the contact time and inter-contact time meanwhile to ensure integrity and availability of message delivery. The simulation results based on real-world mobility traces show the performance advantages of NodeRank in offloading efficiency and network redundant copies.

• 한국IT서비스학회지
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• 제22권1호
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• pp.57-74
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• 2023

It has been pointed out as a limitation that the rank of some efficient DMUs(decision making units) cannot be discriminated due to the relativity nature of efficiency measured by DEA(data envelopment analysis), comparing the production structure. Recently, to solve this problem, a DEA-SNA(social network analysis) model that combines SNA techniques with data envelopment analysis has been studied intensively. Several models have been proposed using techniques such as eigenvector centrality, pagerank centrality, and hypertext induced topic selection(HITS) algorithm, but DMUs that cannot be ranked still remain. Moreover, in the process of extracting latent information within the DMU group to build effective network, a problem that violates the basic assumptions of the DEA also arises. This study is meaningful in finding the cause of the limitations by comparing and analyzing the characteristics of the DEA-SNA model proposed so far, and based on this, suggesting the direction and possibility to develop more advanced model. Through the results of this study, it will be enable to further expand the field of research related to DEA.

• 한국산업정보학회논문지
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• 제12권4호
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• pp.65-73
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• 2007

본 연구는 추천시스템에서 협력적 필터링 알고리즘인 이웃기반의 협력적 필터링 알고리즘과 대응평균 알고리즘의 선호도 예측 결과를 이용하여 예측결과의 순위 일치성과 실제 고객에 상품 추천인 Top-N 추천의 정확도에 대하여 연구하였다. 연구결과 대응평균 알고리즘에 의한 선호도 예측 정확도의 순위 일치성과 예측치를 이용한 Top-N 추천의 정확도가 기존의 이웃기반의 협력적 필터링 알고리즘의 결과보다 우수함을 알 수 있었다. 이는 협력적 필터링 추천시스템에서 대응평균 알고리즘을 이용한 선호도 예측 결과를 이용하여 고객에게 상품추천을 하는 것이 이웃기반의 협력적 필터링 알고리즘을 이용하는 것보다 더 효과적이며 추천시스템에 대한 고객의 만족을 향상시킬 수 있을 것으로 기대된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.105-108
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• 1997

This paper proposes a new approach of balanced model reduction using matrix pencil. The algorithm presented in this paper is to convert full-rank high-order system into rank-deficient system using perturbation made by matrix pencil method. Then the system can be truncated to a low-order system that we want via balanced realization. We discuss the comparison with other methods and the various observations by simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.523-525
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• 2005

This paper deals with a linear matrix inequality (LMI) approach to the design of a static output feedback controller that simultaneously stabilizes a finite collection of linear time-invariant plants. Simultaneous stabilization by static ouput feedback is represented in terms of LMIs with a rank condition. An iterative penalty method is proposed to solve the rank-constrained LMI problem. Numerical experiments show the effectiveness of the proposed algorithm.

• Journal of the Korean Statistical Society
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• 제26권3호
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• pp.333-354
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• 1997

Computational algorithms to calculate M-estimators and rank estimators of regression parameters from left-truncated and right-censored data are developed herein. In the case of M-estimators, new statistical methods are also introduced to incorporate leverage assements and concomitant scale estimation in the presence of left truncation and right censoring on the observed response. Furthermore, graphical methods to examine the residuals from these data are presented. Two real data sets are used for illustration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1454-1466
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• 2016

This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.489-496
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• 2006

The random-based cleaning algorithm is a simple algorithm widely used in commercial vacuum cleaning robots. This algorithm has two limitations, that is, cleaning takes a long time and there is no guarantee that the cleaning will cover the whole cleaning area. This has lead to customer dissatisfaction. Thus, in recent years, many intelligent cleaning algorithms that takes into consideration information gathered from the cleaning area environment have been proposed. The plowing-based algorithm, which is the most efficient algorithm known to date when there are no obstacles in the cleaning area, has a deficiency that when obstacle prevail, its performance is not guaranteed. In this paper, we propose the Group-k algorithm that is efficient for that situation, that is, when obstacle prevail. The goal is not to complete the cleaning as soon as possible, but to clean the majority of the cleaning area as fast as possible. The motivation behind this is that areas close to obstacles are usually difficult for robots to handle, and hence, many require human assistance anyway In our approach, obstacles are grouped by the complexity of the obstacles, which we refer to as 'complex rank', and then decide the cleaning route based on this complex rank. Results from our simulation-based experiments show that although the cleaning completion time takes longer than the plowing-based algorithm, the Group-k algorithm cleans the majority of the cleaning area faster than the plowing algorithm.

• 정보처리학회논문지A
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• 제18A권1호
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• pp.11-18
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• 2011

클라우드 컴퓨팅은 자원 공유를 위한 새로운 컴퓨팅 패러다임이다. 클라우드 서비스를 위해 사용 하는 다양한 어플리케이션들은 워크플로우들로 표현된다. 이러한 워크플로우 어플리케이션은 클라우드의 자원 또는 서비스들에 적절하게 할당되어야 한다. 본 논문에서는 클라우드 컴퓨팅 환경을 고려하여 다중 워크플로우 어플리케이션을 위한 새로운 스케줄링 알고리즘을 제안한다. 제안하는 알고리즘은 비용 기반 랭크 스케줄링 알고리즘으로서 다중 워크플로우 어플리케이션을 고려할 뿐만 아니라, 서비스 평가를 위한 다양한 QoS 메트릭을 고려한다. 실험결과에서 제안한 알고리즘은 다른 알고리즘들에 비해 평균 총 처리시간과 평균 가용성에서 향상된 결과를 보였다.