• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.27-33
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• 2011

In this paper, we propose an efficient on-line handwritten digit recognition base on Convex-Concave curves feature which is extracted by a chain code sequence using Smith-Waterman alignment algorithm. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Convex-Concave curves feature extraction. This feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a Smith-Waterman alignment algorithm, which in turn classifies it as one of the nine digits. In comparison with backpropagation neural network, Smith-Waterman alignment has the more outstanding performance.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.339-344
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• 2019

Global sequence alignment in search of similarity or homology favors larger size of the sequence because it keeps looking for more similar section between two sequences in the hope that it adds up scores for matched part in the rest of the sequence. If a substantial size of mismatched section exists in the middle of the sequence, it greatly reduces the total alignment score. In this case a whole sequence would be better to be divided into multiple sections. Overall alignment score over the multiple sections of the sequence would increase as compared to global alignment. This method is called multiple local alignment. In this paper, we implement a multiple local alignment algorithm, an extension of Smith-Waterman algorithm and show the experimental results for the algorithm that is able to search for sub-optimal sequence.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.730-732
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• 2002

서열 배치 문제는 두 개의 서열에서 가장 유사한 부분을 찾는 문제이다. 이 문제를 푸는 알고리즘으로 가장 많이 쓰이는 것은 Smith-Waterman 알고리즘이다. Smith-Waterman 알고리즘은 동적 프로그래밍을 이용하여 두 서열에서 유사한 부분을 찾아낸다. 그러나 Smith-Waterman 알고리즘은 서열을 이루는 문자들의 품질 정보를 사용하지는 않는다. 각 문자가 얼마 정도의 신뢰도를 가지고 있는지를 나타내는 품질 정보는 생물학에서는 중요한 정보이다. 본 논문에서는 각 문자에 주어지는 품질이 서로 다를 때에, 품질 정보를 이용하여 가장 적합한 부분 배치를 찾아내는 알고리즘을 제시한다. 실제로 현재 서열 배치에 가장 많이 사용되고 있는 프로그램 중 하나인, Phred/Phrap에서 사용하는 LLR 값을 이용해서 비교했을 때, 본 논문에서 제시한 알고리즘은 기존의 Smith-Waterman 알고리즘보다 더 좋은 결과를 얻었다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.592-594
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• 2001

두 문자열의 부분배치(local alignment)를 찾는 대표적인 알고리즘인 Smith-Waterman 알고리즘(SW 알고리즘)은 정규화된 최적부분배치를 찾지 못하는 단점이 있다. 최근에 fractional programming 기법을 이용하여 여러 번의 SW 알고리즘을 수행함으로써 정규화된 최적부분배티를 찾는 알고리즘이 제시되었지만 이는 매우 많은 시간이 걸린다. 본 논문에서는 fractional programming 기법을 이용하여 정규화된 최적부분배치를 찾는 알고리즘에, 완전매치(Exact Match)을 이용한 휴리스틱 기법인 Banded SW 알고리즘을 적용하여, 낮은 오차를 가지면서 실용적으로 매우 빠른 정규화된 최적부분배치를 찾는 알고리즘을 제시하고 이 알고리즘과 제시하고 이 알고리즘과 기존의 알고리즘을 직접 구현하여 실험한 결과를 비교 분석한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.1073-1076
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• 2005

Since the size of biosequence database grows exponentially every year, it becomes impractical to use Smith-Waterman algorithm for exact sequence similarity search. For fast sequence similarity search, researchers have been proposed heuristic methods that use the frequency of characters in subsequences. These methods have the defect that different sequences are treated as the same sequence. Because of using only the frequency of characters, the accuracy of these methods are lower than Smith-Waterman algorithm. In this paper, we propose an algorithm which processes query efficiently by indexing the frequency of characters including the positional information of characters in subsequences. The experiments show that our algorithm improve the accuracy of sequence similarity search approximately 5${\sim}$20% than heuristic algorithms using only the frequency of characters.

• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.26-34
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• 2016

The alignment of DNA sequences is one of the important processes in the field of bioinformatics. The Smith-Waterman algorithm (SWA) performs optimally for aligning sequences but is computationally expensive. Field programmable gate array (FPGA) performs the best on parameters such as cost, speed-up, and ease of re-configurability to implement SWA. The performance of FPGA-based SWA is dependent on efficient cell-basic implementation-unit design. In this paper, we present an optimized systolic cell design while avoiding oversimplification, very large-scale integration (VLSI)-level design, and direct mapping of iterative equations such as previous cell designs. The proposed design makes efficient use of hardware resources and provides portability as the proposed design is not based on gate-level details. Our cell design implementing a linear gap penalty resulted in a performance improvement of 32× over a GPP platform and surpassed the hardware utilization of another implementation by a factor of 4.23.

• 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.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.471-472
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• 2014

• Genomics & Informatics
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• v.1 no.1
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• pp.25-31
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• 2003

Local alignment is an important task in molecular biology to see if two sequences contain regions that are similar. The most popular approach to local alignment is the use of dynamic programming due to Smith and Waterman, but the alignment reported by the Smith-Waterman algorithm has some undesirable properties. The recent approach to fix these problems is to use the notion of normalized scores for local alignments by Arslan, Egecioglu and Pevzner. In this paper we consider the problem of finding all local alignments whose normalized scores are above a given threshold, and present a fast heuristic algorithm. Our algorithm is 180-330 times faster than Arslan et al.'s for sequences of length about 120 kbp and about 40-50 times faster for sequences of length about 30 kbp.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.970-972
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• 2005

유전자 데이터베이스의 서열의 길이가 수백만에서 수백억 정도의 대용량 텍스트이기 때문에 기존의 Smith-waterman 알고리즘으로 정확한 서열의 유사성을 검색하는 것은 매우 비효율적이다. 따라서 빠른 유사성 검색을 위해 데이터베이스에 저장된 문자열에 대해 특정 길이의 모든 부분문자열에 나타나는 문자의 출현 빈도를 이용한 휴리스틱 방법들이 제안되었다. 이러한 방법들은 질의 서열과 일치될 가능성이 높은 후보들만을 추출한 후 이들 각각에 대하여 질의 서열과의 일치 여부를 조사하므로 빠르게 유사성 검색을 할 수 있다. 그러나 이 방법은 문자의 출현 빈도만을 사용하므로 서로 다른 서열을 같은 서열로 취급하는 단점이 있어 정확도가 Smith-Waterman 알고리즘에 비해 떨어진다. 본 논문에서는 문자가 부분문자열에 나타나는 위치 정보를 포함하여 문자의 출현빈도를 인덱싱함으로써 질의 처리를 효율적으로 수행하는 알고리즘을 제안한다. 실험결과 제안된 알고리즘은 문자 빈도만을 사용하는 알고리즘에 비해 $5\~15\%$정도 정확성이 향상되었다.