• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.6
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• pp.433-441
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• 2020

This study proposes a constant power tracking algorithm to compensate for the intermittent characteristics of Photovoltaic connected to a DC micro grid. A PV-ESS integrated module in which distributed ESS is additionally connected is utilized for the proposed algorithm. PV performs P&O MPPT control at all times. To supplement the intermittent characteristics of PV, the proposed constant power tracking algorithm maintains constant power by operating the distributed ESS of the PV-ESS integrated module in accordance with the output state of the PV. By performing PSIM simulation and an experiment, this study verifies the performance of the integrated module of PV-ESS for DC micro grids applying the constant power tracking algorithm.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.7
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• pp.377-383
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• 2002

The TSP(Traveling Salesman Problem) has been known as NP-complete, there have been various studies to find the near optimal solution. The nearest neighbor heuristic is more simple than the other algorithms which are to find the optimal solution. This paper designs and implements a new distributed nearest neighbor heuristic with bounding function for the TSP using the master/slave model of PVM(Parallel Virtual Machine). Distributed genetic algorithm obtains a near optimal solution and distributed nearest neighbor heuristic finds an optimal solution for the TSP using the near optimal value obtained by distributed genetic algorithm as the initial bounding value. Especially, we get more speedup using a new genetic operator in the genetic algorithm.

• Journal of KIISE
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• v.43 no.10
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• pp.1131-1143
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• 2016

How can we effectively compress big graphs composed of billions of edges? By concentrating non-zeros in the adjacency matrix through vertex rearrangement, we can compress big graphs more efficiently. Also, we can boost the performance of several graph mining algorithms such as PageRank. SlashBurn is a state-of-the-art vertex rearrangement method. It processes real-world graphs effectively by utilizing the power-law characteristic of the real-world networks. However, the original SlashBurn algorithm displays a noticeable slowdown for large-scale graphs, and cannot be used at all when graphs are too large to fit in a single machine since it is designed to run on a single machine. In this paper, we propose a distributed SlashBurn algorithm to overcome these limitations. Distributed SlashBurn processes big graphs much faster than the original SlashBurn algorithm does. In addition, it scales up well by performing the large-scale vertex rearrangement process in a distributed fashion. In our experiments using real-world big graphs, the proposed distributed SlashBurn algorithm was found to run more than 45 times faster than the single machine counterpart, and process graphs that are 16 times bigger compared to the original method.

• IE interfaces
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• v.14 no.2
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• pp.148-157
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• 2001

This paper considers a network restoration algorithm for all-optical WDM networks. As the increasing traffic and transmission speed, any failure on the networks will lead to loss of huge data and disruption of the services. Therefore, a network restoration algorithm is necessary for the high-speed all-optical networks. This paper suggests a distributed restoration algorithm for line or channel level failures under the dynamic rerouting. For the algorithm, some measures for performance evaluation are explicitly derived and simulation studies are exploited to evaluate its usability by SLAM(Simulation Language for Alternative Modeling) II.

• Journal of IKEEE
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• v.22 no.3
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• pp.558-564
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• 2018

In distributed estimation where each node can collect only partial information on the parameter of interest without communication between nodes and quantize it before transmission to a fusion node which conducts estimation of the parameter, we consider a novel quantization technique employed at local nodes. It should be noted that the performance can be greatly improved if each node can transmit its measurement to one designated node (namely, head node) which can quantize its estimate using the total rate available in the system. For this case, the best strategy at the head node would be simply to partition the parameter space using the generalized Lloyd algorithm, producing the global codewords, one of which is closest to the estimate is transmitted to a fusion node. In this paper, we propose an iterative design algorithm that seeks to efficiently assign the codewords into each of quantization partitions at nodes so as to achieve the performance close to that of the system with the head node. We show through extensive experiments that the proposed algorithm offers a performance improvement in rate-distortion perspective as compared with previous novel techniques.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.257-267
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• 2004

Most algorithms for handling distributed deadlock problem in the generalized request model use the diffusing computation technique where propagation of probes and backward propagation of replies carrying dependency information between processes are both required to detect deadlock Since fast deadlock detection is critical, we propose an algorithm that lets probes rather than replies carry the information required for deadlock detection. This helps to remove the backward propagation of replies and reduce the time cost for deadlock detection to almost half of that of the existing algorithms. Moreover, the proposed algorithm is extended to deal with concurrent executions, which achieves further improvement of deadlock detection time, whereas the current algorithms deal only with a single execution. We compare the performance of the proposed algorithm with that of the other algorithms through simulation experiments.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2561-2564
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• 2003

Data mining is an effective method of the discovery of useful information such as rules and previously unknown patterns existing in large databases. The discovery of association rules is an important data mining problem. We have developed a new parallel mining called Distributed Frequent Pattern Tree (abbreviated by DFPT) algorithm on a distributed shared nothing parallel system to detect association rules. DFPT algorithm is devised for parallel execution of the FP-growth algorithm. It needs only two full disk data scanning of the database by eliminating the need for generating the candidate items. We have achieved good workload balancing throughout the mining process by distributing the work equally to all processors. We implemented the algorithm on a PC cluster system, and observed that the algorithm outperformed the Improved Count Distribution scheme.

• International Journal of Contents
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• v.2 no.1
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• pp.39-44
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• 2006

Load sharing is a critical resource in computer system. In sender-initiated load sharing algorithms, the sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver initiated load sharing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, we propose a genetic algorithm based approach for improved sender-initiated and receiver-initiated load sharing in distributed systems. And we expand this algorithm to an adaptive load sharing algorithm. Compared with the conventional sender-initiated and receiver-initiated algorithms, the proposed algorithm decreases the response time and task processing time.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.115-117
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• 2004

Recently, mining far association rules in distributed database environments is a central problem in knowledge discovery area. While the data are located in different share-nothing machines, and each data site grows by time. Mining global frequent itemsets is hard and not efficient in large number of distributed sewen. In many distributed databases. time component(which is usually attached to transactions in database), contains meaningful time-related rules. In this paper, we design a new DTA(distributed temporal association) algorithm that combines temporal concepts inside distributed association rules. The algorithm confirms the time interval for applying association rules in distributed databases. The experiment results show that DTA can generate interesting correlation frequent itemsets related with time periods.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.632-639
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• 2011

Searching distributed resource efficiently is very important in distributed computing, cloud computing environment. Distributed resource searching may have system overheads and take much time in proportion to the searching number, because distributed resource searching has to circuit many peers for searching information. The open-source community project JXTA defines an open set of standard protocols for ad hoc, pervasive, peer-to-peer computing as a common platform for developing a wide variety of decentralized network applications. In this paper, we proposed peer search algorithm based on JXTA-Sim. original JXTA peer searching algorithm selected a loosely-consistent DHT. Our Lookup algorithm decreases message number of WALK_LOOKUP and reduce the network system overload, and we make a result of same performance both original algorithm and our proposed algorithm.