• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2052-2067
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• 2011

Complementary trees are two spanning trees rooted at the sink node satisfying that any source node's two paths to the sink node on the two trees are node-disjoint. Complementary trees routing strategy is a special node-disjoint multi-path routing approach. Several complementary trees routing algorithms have been proposed, in which path discovery methods based on depth first search (DFS) or Dijkstra's algorithm are used to find a path for augmentation in each round of path augmentation step. In this paper, a novel path discovery method based on multi-tree-growing (MTG) is presented for the first time to our knowledge. Based on this path discovery method, a complementary trees routing algorithm is developed with objectives of low average path length on both spanning trees and low complexity. Measures are employed in our complementary trees routing algorithm to add a path with nodes near to the sink node in each round of path augmentation step. The simulation results demonstrate that our complementary trees routing algorithm can achieve low average path length on both spanning trees with low running time, suitable for wireless sensor networks in industrial scenarios.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.606-619
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• 2001

This paper presents an efficient algorithm to compute difference between old and new versions of an SGML/XML document. The difference between the two versions can be considered to be an edit script that transforms some document tree into another The proposed algorithm is based on hybridization of bottom-up and top-down methods: matching relationships between nodes in the two versions are producted in a bottom-up manner and top-down breadth -first search computes an edit script. Because the algorithm does not need to investigate possible existence of matchings for all nodes, faster matching can be achieved . Furthermore, it can detect more structurally meaningful changes such as subtree move and copy as well as simple changes to the node itself like insert, delete, and update.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.255-259
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• 2005

To search a pattern P in a text, such index data structures as suffix trees and suffix arrays are widely used in diverse applications of string processing and computational biology. It is well known that searching in suffix trees is faster than suffix ways in the aspect of time complexity, i.e., it takes O(${\mid}P{\mid}$) time to search P on a constant-size alphabet in a suffix tree while it takes O(${\mid}P{\mid}+logn$) time in a suffix way where n is the length of the text. In this paper we present a linear-tim8 search algorithm in suffix arrays for constant-size alphabets. For a gene.al alphabet $\Sigma$, it takes O(${\mid}P{\mid}log{\mid}{\Sigma}{\mid}$) time.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.5
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• pp.260-267
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• 2005

To search efficiently a text T of length n for a pattern P over an alphabet 5, suffix trees and suffix arrays are widely used. In case of a large text, suffix arrays are preferred to suffix trees because suffix ways take less space than suffix trees. Recently, O(${\mid}P{\mid}{\codt}{\mid}{\Sigma}{\mid}$-time and O(${\mid}P{\mid}P{\cdot}log{\mid}{\Sigma}{\mid}$)-time search algorithms in suffix ways were developed. In this paper we present time and space efficient search algorithms in suffix arrays. One algorithm runs in O(${\mid}P{\mid}$) time using O($n{\cdot}{\mid}{\Sigma}{\mid}$)-bits space, and the other runs in O($n{\cdot}{\mid}{\Sigma}{\mid}$ time using O($nlog{\mid}{\Sigma}{\mid}+{\mid}{\Sigma}{\mid}{\cdot}$nlog log n/logn)-bits space, which is more space efficient and still fast. Experiments show that our algorithms are efficient in both time and space when compared to previous algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.2
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• pp.313-320
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• 2011

SPH(Smoothed Particle Hydrodynamics) is a gridless Lagrangian technique that is useful as an alternative numerical analysis method used to analyze high deformation problems as well as astrophysical and cosmological problems. In SPH, all points within the support of the kernel are taken as neighbours. The accuracy of the SHP is highly influenced by the method for choosing neighbours from all particle points considered. Typically a linked-list method or tree search method has been used as an effective tool because of its conceptual simplicity, but these methods have some liability in anisotropy situations. In this study, convex hull algorithm is presented as an improved method to eliminate this artifact. A convex hull is the smallest convex set that contains a certain set of points or a polygon. The selected candidate neighbours set are mapped into the new space by an inverse square mapping, and extract a convex hull. The neighbours are selected from the shell of the convex hull. These algorithms are proved by Fortran programs. The programs are expected to use as a searching algorithm in the future SPH program.

• Journal of the Korea Computer Graphics Society
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• v.14 no.3
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• pp.11-17
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• 2008

Many methods which represent complex surfaces using height map without a number of vertex have been researched. However, a single layer height map cannot present more complex objects because it has only one height value on each position. In this paper, we introduce the new approach to render more complex objects, which are not generated by single layer height map, using multi layer height map. We store height values of the scene to each texture channel by the ascending order. A pair of ordered height values composes a geometry block and we use this property. For accurate ray search, we store the highest value in odd channels and the lowest value in even channels to generate quad tree height map. Our ray search algorithm shows accurate intersections between viewing ray and height values using quad tree height map. We solve aliasing problems on grazing angles occurred in previous methods and render the result scene on real-time.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.427-432
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• 2005

Packet arrival rates in internet routers have been dramatically increased due to the advance of link technologies, and hence wire-speed packet processing in Internet routers becomes more challenging. As IP address lookup is one of the most essential functions for packet processing, algorithm and architectures for efficient IP address lookup have been widely studied. In this paper, we Propose an efficient I address lookup architecture which shows yeW good Performance in search speed while requires a single small-size memory The proposed architecture is based on multi-way tree structure which performs comparisons of multiple prefixes by one memory access. Performance evaluation results show that the proposed architecture requires a 280kByte SRAM to store about 40000 prefix samples and an address lookup is achieved by 5.9 memory accesses in average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11A
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• pp.1120-1129
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• 2006

It is very important to maximize the battery durability of mobile host in wireless ad-hoc network environments, because it extends the durability and Performance of the system. Since mobile hosts play a routing role, the network structure and the location of mobile hosts create a difference of energy consumption of mobile hosts. In this paper, each mobile host maintains energy tree and evaluates the amount of the energy of the neighboring mobile hosts by using message tree packet by periods. When mobile host sets up a routing path to send a packet, it selects the most proper path in order to consume energy effectively by using energy tree and breadth first search. In this paper, we suggest that, in wireless ad-hoc network environments, if each mobile host consumes balanced energy, mobile hosts of which energy capacity is limited can work as long as it can. Therefore, the durability and performance of the system can be extended.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.367-380
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• 2002

Due to recent increase in applications requiring huge amount of data such as spatial data analysis and image analysis, clustering on large databases has been actively studied. In a hierarchical clustering method, a tree representing hierarchical decomposition of the database is first created, and then, used for efficient clustering. Existing hierarchical clustering methods mainly adopted the bottom-up approach, which creates a tree from the bottom to the topmost level of the hierarchy. These bottom-up methods require at least one scan over the entire database in order to build the tree and need to search most nodes of the tree since the clustering algorithm starts from the leaf level. In this paper, we propose a novel top-down hierarchical clustering method that uses multidimensional indexes that are already maintained in most database applications. Generally, multidimensional indexes have the clustering property storing similar objects in the same (or adjacent) data pares. Using this property we can find adjacent objects without calculating distances among them. We first formally define the cluster based on the density of objects. For the definition, we propose the concept of the region contrast partition based on the density of the region. To speed up the clustering algorithm, we use the branch-and-bound algorithm. We propose the bounds and formally prove their correctness. Experimental results show that the proposed method is at least as effective in quality of clustering as BIRCH, a bottom-up hierarchical clustering method, while reducing the number of page accesses by up to 26～187 times depending on the size of the database. As a result, we believe that the proposed method significantly improves the clustering performance in large databases and is practically usable in various database applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.