• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.266-277
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• 2019

In modern warfare, securing time for preemptive response is recognized as an important factor of victory. The naval combat system, the core of naval forces, also strives to increase the effectiveness of engagement by improving its real-time information processing capabilities. As part of that, it is considered to use the TPR-tree in the naval combat system's target indexing because spatio-temporal searches can be performed quickly even as the number of target information increases. However, because the TPR-tree is slow to process updates, there is a limitation to handling frequent updates. In this paper, we present a method for improving the update performance of TPR-tree by applying the bottom-up update scheme, previously proposed for R-tree, to the TPR-tree. In particular, we analyze the causes of overlaps occurring when applying the bottom-up updates and propose ways to limit the MBR expansion to solve it. Our experimental results show that the proposed technique improves the update performance of TPR-tree from 3.5 times to 12 times while maintaining search performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.117-123
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• 1996

In this paper, we present a new learning method for the fuzzy ARTMAP which is effective for the noisy input patterns. Conventional fuzzy ARTMAP employs only fuzzy AND operation between input vector and weight vector in learning both top-down and bottom-up weight vectors. This fuzzy AND operation causes excessive update of the weight vector in the noisy input environment. As a result, the number of spurious categories are increased and the recognition ratio is reduced. To solve these problems, we propose a new method in updating the weight vectors: the top-down weight vectors of the fuzzy ART system are updated using weighted average of the input vector and the weight vector itself, and the bottom-up weight vectors are updated using fuzzy AND operation between the updated top-down weitht vector and bottom-up weight vector itself. The weighted average prevents the excessive update of the weight vectors and the fuzzy AND operation renders the learning fast and stble. Simulation results show that the proposed method reduces the generation of spurious categories and increases the recognition ratio in the noisy input environment.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.477-490
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• 2006

Existing index structures need very much update cost because they repeat delete and insert operations in order to update continuously moving objects. In this paper, we propose a new index structure which reduces the update cost of continuously moving objects. The proposed index structure consists of a space partitioning index structure that stores the location of the moving objects and an auxiliary index structure that directly accesses to their current positions. In order to increase the fanout of the node, it stores not the real partitioning area but kd-tree as the information about the child node of the node. In addition, we don't traverse a whole index structure, but access the leaf nodes directly and accomplish a bottom-up update strategy for efficiently updating the positions of moving objects. We show through the various experiments that our index structure outperforms the existing index structures in terms of insertion, update and retrieval.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.141-152
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• 2004

In this paper, we propose the index structure supporting the future position retrievals with efficiently updating continuous positions of moving objects in location based services. For reducing update costs of moving objects, our index structure directly accesses to the leaf node with moving objects using secondary index structure and performs bottom up update when node information is changed. Positions of moving objects are stored in primary index structure. In primary index structure, the split information similar to kd-tree is stored to internal node for increasing node's fanout. And the proposed index structure supports the future position retrievals using velocity of moving objects in the child node.

• 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:Software and Applications
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• v.35 no.10
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• pp.636-652
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• 2008

In this paper, we present a modular pointer analysis algorithm based on the update history. We use the term 'module' to mean a set of mutually recursive procedures and the term 'modular analysis' to mean a program analysis that does not need the source codes of the other modules to analyze a module. Since a modular pointer analysis does not utilize any information on the callers, it is difficult to design a precise analysis that does not lose the information related to the program flow or the calling context. In this paper, we propose a modular and flow- and context-sensitive pointer analysis algorithm based on the update history that can memory states of a procedure independently of the information on the calling context and keep the information on the order of side effects performed. Such a memory representation not only enables the analysis to be formalized as a modular analysis, but also helps the analysis to effectively identify killed side effects and relevant alias contexts.