• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.830-833
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• 2007

In this paper, we implemented a $17{\times}17b$ binary digital multiplier using radix-4 Booth;s algorithmand proposed an efficient testing methodology for the full-custom design. A two-stage pipeline architecture was applied to achieve higher throughput and 4:2 adders were used for regular layout structure in the Wallace tree partition. Several chips were fabricated using LG Semicon 0.6-um 3-Metal N-well CMOS technology. We did fault simulations efficiently using the proposed test method resulting in the reduction of the number of faulty nodes by 88%. The chip contains 9115 transistors and the core area occupies $1135^*1545$ mm2. The functional tests using ATS-2 tester showed that it can operate with 24 MHz clock at 5.0 V at room temperature.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.509-525
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• 2005

In this paper, we propose the index structure based on a vector approximation for efficiently supporting the similarity search of multi-dimensional data. The proposed index structure splits a region with the space partition method and allocates to the split region dynamic bits according to the distribution of data. Therefore, the index structure splits a region to the unoverlapped regions and can reduce the depth of the tree by storing the much region information of child nodes in a internal node. Our index structure represents the child node more exactly and provide the efficient search by representing the region information of the child node relatively using the region information of the parent node. We show that our proposed index structure is better than the existing index structure in various experiments. Experimental results show that our proposed index structure achieves about $40\%$ performance improvements on search performance over the existing method.

• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.783-790
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• 2009

The necessity of wireless sensor networks is increasing in the recent years. So many researches are studied in wireless sensor networks. The clustering algorithm provides an effective way to prolong the lifetime of the wireless sensor networks. The one-hop routing of LEACH algorithm is an inefficient way in the energy consumption of cluster-head, because it transmits a data to the BS(Base Station) with one-hop. On the other hand, other clustering algorithms transmit data to the BS with multi-hop, because the multi-hop transmission is an effective way. But the multi-hop routing of other clustering algorithms which transmits data to BS with multi-hop have a data bottleneck state problem. The unequal clustering algorithm solved a data bottleneck state problem by increasing the routing path. Most of the unequal clustering algorithms partition the nodes into clusters of unequal size, and clusters closer to the BS have small-size the those farther away from the BS. However, the energy consumption of cluster-head in unequal clustering algorithm is more increased than other clustering algorithms. In the thesis, I propose an energy efficient unequal clustering algorithm which decreases the energy consumption of cluster-head and solves the data bottleneck state problem. The basic idea is divided a three part. First of all I provide that the election of appropriate cluster-head. Next, I offer that the decision of cluster-size which consider the distance from the BS, the energy state of node and the number of neighborhood node. Finally, I provide that the election of assistant node which the transmit function substituted for cluster-head. As a result, the energy consumption of cluster-head is minimized, and the energy consumption of total network is minimized.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.608-616
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• 2016

The Smith-Watrman (SW) algorithm is a local alignment algorithm which is one of important operations in DNA sequence analysis. The SW algorithm finds the optimal local alignment with respect to the scoring system being used, but it has a problem to demand long execution time. To solve the problem of SW, some methods to perform SW in distributed and parallel manner have been proposed. The ADAM which is a distributed and parallel processing framework for DNA sequence has parallel SW. However, the parallel SW of the ADAM does not consider that the SW is a dynamic programming method, so the parallel SW of the ADAM has the limit of its performance. In this paper, we propose a method to enhance the parallel SW of ADAM. The proposed parallel SW (PSW) is performed in two phases. In the first phase, the PSW splits a DNA sequence into the number of partitions and assigns them to multiple nodes. Then, the original Smith-Waterman algorithm is performed in parallel at each node. In the second phase, the PSW estimates the portion of data sequence that should be recalculated, and the recalculation is performed on the portions in parallel at each node. In the experiment, we compare the proposed PSW to the parallel SW of the ADAM to show the superiority of the PSW.

• Journal of IKEEE
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• v.11 no.4
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• pp.348-357
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• 2007

Due to the improved performance and cost of personal mobile devices and rapid progress of wireless communication technology, the number of users who utilize these devices is increasing. This trend requires various types of services be available to users. So far, there have been many solutions provided for the shortest path problem. But, technologies which can offer various recommendation services to user depending on user’s current location are focused on Euclidean spaces rather than road network. Thus, in this paper, we extend the previous work to satisfy this requirement on road network database. Our proposed scheme requires pre-computation for the efficient query processing. In the preprocessing step, we first partition the input road network into a fixed number of Voronoi polygons and then pre-compute routing information for each polygon. In the meantime, we select the number of Voronoi polygons in proposition to the scale of road network. Through this selection, the required size of pre-computation is linearly increasing to the size of road network. Using this pre-computated information, we can process queries more quickly. Through experiments, we have shown that our proposed scheme can achieve excellent performance in terms of scheduling time and the number of visited nodes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.129-139
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• 2012

Given digraph network $D=(N,A),n{\in}N,a=c(u,v){\in}A$ with source s and sink t, the maximum flow from s to t is determined by cut (S, T) that splits N to $s{\in}S$ and $t{\in}T$ disjoint sets with minimum cut value. The Ford-Fulkerson (F-F) algorithm with time complexity $O(NA^2)$ has been well known to this problem. The F-F algorithm finds all possible augmenting paths from s to t with residual capacity arcs and determines bottleneck arc that has a minimum residual capacity among the paths. After completion of algorithm, you should be determine the minimum cut by combination of bottleneck arcs. This paper suggests maximum adjacency merging and compute cut value method is called by MA-merging algorithm. We start the initial value to S={s}, T={t}, Then we select the maximum capacity $_{max}c(u,v)$ in the graph and merge to adjacent set S or T. Finally, we compute cut value of S or T. This algorithm runs n-1 times. We experiment Ford-Fulkerson and MA-merging algorithm for various 8 digraph. As a results, MA-merging algorithm can be finds minimum cut during the n-1 running times with time complexity O(N).

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.