• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.81-98
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• 1997

Given a rooted tree T with profits and node demands, the capacitated subtree of a tree problem (GSTP) consists of finding a rooted subtree of maximum profit, subject to having total demand no larger than the given capacity H. We first define the so-called critical item for CSTP and find an upper bound on the optimal value of CSTP in O(n$^{2}$) time, where n is the number of nodes in T. We then present our branch and bound algorithm for solving CSTP and illustrate the algiruthm by using an example. Finally, we implement our branch-and-bound algorithm and compare the computational results with those for both CPLEX and a dynamic programming algorithm. The comparison shows that our branch-and-bound algorithm performs much better than both CPLEX and the dynamic programming algorithm, where n and H are the range of [50, 500] and [5000, 10000], respectively.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.331-343
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• 2013

In RFID systems, the anti-collision algorithm is being improved to recognize Tag's ID within recognition area of the reader quickly and efficiently. This paper focuses on Tag collision. Many studies have been carried out to resolve Tag collision. This paper proposes a new N-ary Query Tree Algorithm to resolve more than Tag collision simultaneously, according to the value of m(2 ~ 6). This algorithm can identify more tags than existing methods by treating a maximum 6 bit collision, regardless of the continuation/non-continuation Tag's ID patterns. So, it extracts maximumly different $2^6$ bit patterns per single prefix in recognition process. The performance of N-ary Query Tree Algorithm is evaluated by theoretical analysis and simulation program.

• Journal of Communications and Networks
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• v.11 no.5
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• pp.500-508
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• 2009

We have designed an algorithm for a problem in multicast communication. The problem is to construct a multicast tree while minimizing its cost, which is known to be NP-complete. Our algorithm, which employs new concepts defined as potential cost and spanning cost, generates a multicast tree more efficiently than the well-known heuristic called Takahashi and Matsuyama (TM) [1] in terms of tree cost. The time complexity of our algorithm is O($kn^2$) for an n-node network with k members in the multicast group and is comparable to the TM. Our empirical performance evaluation comparing the proposed algorithm with TM shows that the enhancement is up to 1.25%~4.23% for each best case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.31-39
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• 2015

The degree-constrained minimum spanning tree (d-MST) problem is considered NP-complete for no exact solution-yielding polynomial algorithm has been proposed to. One thus has to resort to an heuristic approximate algorithm to obtain an optimal solution to this problem. This paper therefore presents a polynomial time algorithm which obtains an intial solution to the d-MST with the help of Kruskal's algorithm and performs k-opt on the initial solution obtained so as to derive the final optimal solution. When tested on 4 graphs, the algorithm has successfully obtained the optimal solutions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2886-2896
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• 1996

The Type of Service based multicast routing algorithm is necessary to support efficiently herogeneous applications in ATM network. In this paper I propose the Constrained Multicast Tree with Virtual Destination(DMTVD) heuristic algorithm as least cost multicast routing algorithm. The service is categorized into two types, as delay sensitive and non in CMTVD algorithm. For the delay sensitive service type, the cost optimized route is the Minimum Cost Stenier Tree connecting all the destination node group, virtual destination node group and source node with least costs, subject to the delay along the path being less than the maximum allowable end to end delay. The other side for the non-delay sensitive service, the cost optimized route is the MCST connecting all the multicast groups with least costs, subject to the traffic load is balanced in the network. The CMTVD algorithm is based on the Constrained Multicasting Tree algorithm but regards the nodes branching multiple destination nodes as virtural destination node. The experimental results show that the total route costs is enhanced 10%-15% than the CTM algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.677-679
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• 2017

Data convergecast is an indispensable task for any WSN applications. Typically, scheduling in the WSN consists of two phases: tree construction and scheduling. The optimal tree structure and scheduling for the network is proven NP-hard. This paper focuses on the delay optimality while constructing the data convergecast tree. The algorithm can take any tree as the input, and by performing the switches (i.e. a node changes its parent), the expected aggregation delay is potentially reduced. Note that while constructing the tree, only the in-tree collisions between the child nodes sending data to their common parent is considered.

• ETRI Journal
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• v.43 no.6
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• pp.1024-1037
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• 2021

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+algorithms.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.949-959
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• 2012

This article is to find the right size of decision trees that performs better for boosting algorithm. First we defined the tree size D as the depth of a decision tree. Then we compared the performance of boosting algorithm with different tree sizes in the experiment. Although it is an usual practice to set the tree size in boosting algorithm to be small, we figured out that the choice of D has a significant influence on the performance of boosting algorithm. Furthermore, we found out that the tree size D need to be sufficiently large for some dataset. The experiment result shows that there exists an optimal D for each dataset and choosing the right size D is important in improving the performance of boosting. We also tried to find the model for estimating the right size D suitable for boosting algorithm, using variables that can explain the nature of a given dataset. The suggested model reveals that the optimal tree size D for a given dataset can be estimated by the error rate of stump tree, the number of classes, the depth of a single tree, and the gini impurity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3B
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• pp.305-313
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• 2004

Group multicast refers to the kind of multicast in which every member of a group is allowed to transmit data to the group. The goal of routing algorithms for group multicast is to construct a set of low cost multicast trees including all the group members with QoS (e.g., bandwidth) constraint. There have been several algorithms proposed: source tree and shared tree approaches. However, the latter approach has a low success rate in constructing a shared multicast tree, and the former approach suffers from high control overhead and low scalability as stoup size increases. In this paper, we present a heuristic algorithm which varies the number of multicast trees according to the network load. The simulation results show not only that our algorithm outperforms the shared tree approach in terms of the success rate, but also that it has lower control overhead than the source tree approach while guaranteeing the same success rate.