• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.319-326
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• 2009

Since string operations were applied to computational biology area, various data structures and algorithms for computing efficient string operations have been studied. The longest common substring problem is an operation to find the longest matching substring in more than two strings, and maximal repeat of string problem is an operation to find substrings repeated more than once in the given string. These operations are importantly used in the string processing area such as pattern matching and likelihood measurement. In this paper, we present algorithms to compute the longest common substring of two strings and to find the maximal repeat of string using three-dimensional $n{\times}n{\times}n$ processors on RMESH(Reconfigurable MESH). Our algorithms have O(1) time complexity.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.15-18
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• 2021

최장 공통 부분 서열(Longest Common Subsequence, LCS)은 서열 유사도(Similarity)를 측정하기 위한 주요 지표 중 하나로 특별한 가정이 없는 한 두 문자열의 LCS 를 계산하기 위해서는 두 문자열의 길이의 곱에 비례하는 시간이 필요하다. 최근 최장(longest)이라는 조건을 극대(maximal)로 완화한 극대 공통 부분 서열(Maximal Common Subsequence, MCS)이 제시되었고, 두 문자열의 MCS 를 선형에 가까운 시간에 찾는 알고리즘이 개발되었다. 극대는 최장을 보장하지 않기 때문에 두 문자열의 MCS 길이는 LCS 길이와 달리 유일하지 않을 수 있고, LCS 길이가 매우 길어도 길이가 1인 MCS가 존재할 수도 있다. 본 논문에서는 기존 알고리즘에 의해 계산되는 MCS 의 효용성을 알아보기 위해, DNA 등 여러 종류의 실제 데이터와 랜덤 생성된 데이터에 대해 LCS 와 MCS 의 길이를 비교했다. MCS 길이는 LCS 길이 대비 실제 데이터에서 32.1 ~ 60.2%, 랜덤 데이터에서는 27.5 ~ 62.9%로 나타났다. 이 비율은 문자열을 이루고 있는 알파벳 수가 많을수록, 문자열의 길이가 길어질수록 감소했다.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.6
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• pp.268-278
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• 2005

We consider constructing the generalized suffix way of strings A and B when the suffix arrays of A and B are given, j.e., merging two suffix arrays of A and B. There are efficient algorithms to merge some special suffix arrays such as the odd array and the even array. However, for the general case that A and B are arbitrary strings, no efficient merging algorithms have been developed. Thus, one had to construct the generalized suffix arrays of A and B by constructing the suffix array of A$\#$B$\$$ from scratch, even though the suffix ways of A and B are given. In this paper, we Present efficient merging algorithms for the suffix arrays of two arbitrary strings A and B drawn from constant and integer alphabets. The experimental results show that merging two suffix ways of A and B are about 5 times faster than constructing the suffix way of A$\#$B$\$$ from scratch for constant alphabets. Our algorithms include searching all suffixes of string B in the suffix array of A. To do this, we use suffix links in suffix ways and we developed efficient algorithms for computing the suffix links. Efficient computation of suffix links is another contribution of this paper because it can be used to solve other problems occurred in bioinformatics that should search all suffixes of a given string in the suffix array of another string such as computing matching statistics, finding longest common substrings, and so on. The experimental results show that our methods for computing suffix links is about 3-4 times faster than the previous fastest methods.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.305-310
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• 2010

In this paper, we propose a heuristic algorithm for motion correspondence based on a point series similarity. A point series is a sequence of points which are sorted in the ascending order of their x-coordinate values. The proposed algorithm clusters the points of a previous frame based on their local adjacency. For each group, we construct several potential point series by permuting the points in it, each of which is compared to the point series of the following frame in order to match the set of points through their similarity based on a proximity constraint. The longest common subsequence between two point series is used as global information to resolve the local ambiguity. Experimental results show an accuracy of more than 90% on two image sequences from the PETS 2009 and the CAVIAR data sets.