• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.956-967
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• 2010

Rapid growth of internet applications has increased the importance of intrusion detection system (IDS) performance. String matching is the most computation-consuming task in IDS. In this paper, a new algorithm for multiple string matching is proposed. This proposed algorithm is based on the canonical Aho-Corasick algorithm and it utilizes a bidirectional and parallel processing structure to accelerate the matching speed. The proposed string matching algorithm was implemented and patched into Snort for experimental evaluation. Comparing with the canonical Aho-Corasick algorithm, the proposed algorithm has gained much improvement on the matching speed, especially in detecting multiple keywords within a long input text string.

• 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.

• Journal of Information Processing Systems
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• v.4 no.2
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• pp.67-76
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• 2008

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.1-7
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• 2018

Reference string recognition is to extract individual reference strings from a reference section of an academic article, which consists of a sequence of reference lines. This task has been attacked by heuristic-based, clustering-based, classification-based approaches, exploiting lexical and layout characteristics of reference lines. Most classification-based methods have used sequence labeling to assign labels to either a sequence of tokens within reference lines, or a sequence of reference lines. Unlike the previous token-level sequence labeling approach, this study attempts to assign different labels to the beginning, intermediate and terminating tokens of a reference string. After that, post-processing is applied to identify reference strings by predicting their beginning and/or terminating tokens. Experimental evaluation using English and German reference string recognition datasets shows that the proposed method obtains above 94% in the macro-averaged F1.

• Journal of Information Processing Systems
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• v.4 no.1
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• pp.17-26
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• 2008

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.46-49
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• 2003

The use of confidence measures for word/utterance verification has become art essential component of any speech input application. Confidence measures have applications to a number of problems such as rejection of incorrect hypotheses, speaker adaptation, or adaptive modification of the hypothesis score during search in continuous speech recognition. In this paper, we present a new utterance verification method using vowel string. Using subword HMMs of VCCV unit, we create anti-models which include vowel string in hypothesis words. The experiment results show that the utterance verification rate of the proposed method is about 79.5%.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.11
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• The KIPS Transactions:PartA
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• v.15A no.4
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• pp.239-242
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• 2008

We study the problem of finding the maximum suffix of a string on the external memory model of computation with one disk. In this model, we are primarily interested in designing algorithms that reduce the number of I/Os between the disk and the internal memory. A string of length N has N suffixes and among these, the lexicographically largest one is called the maximum suffix of the string. Finding the maximum suffix of a string plays a crucial role in solving some string problems. In this paper, we present an external memory algorithm for computing the maximum suffix of a string of length N. The algorithm uses four blocks in the internal memory and performs at most 4(N/L) disk I/Os, where L is the size of a block.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.955-961
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• 2017

In this paper, we propose a parallel approximate string matching algorithm with k-mismatches for multiple fixed-length patterns (PMASM) in DNA sequences. PMASM is developed from parallel single pattern approximate string matching algorithms to effectively calculate the Hamming distances for multiple patterns with a fixed-length. In the preprocessing phase of PMASM, all target patterns are binary encoded and stored into a look-up memory. With each input character from the input string, the Hamming distances between a substring and all patterns can be updated at the same time based on the binary encoding information in the look-up memory. Moreover, PMASM adopts graphics processing units (GPUs) to process the data computations in parallel. This paper presents three kinds of PMASM implementation methods in GPUs: thread PMASM, block-thread PMASM, and shared-mem PMASM methods. The shared-mem PMASM method gives an example to effectively make use of the GPU parallel capacity. Moreover, it also exploits special features of the CUDA (Compute Unified Device Architecture) memory structure to optimize the performance. In the experiments with DNA sequences, the proposed PMASM on GPU is 385, 77, and 64 times faster than the traditional naive algorithm, the shift-add algorithm and the single thread PMASM implementation on CPU. With the same NVIDIA GPU model, the performance of the proposed approach is enhanced up to 44% and 21%, compared with the naive, and the shift-add algorithms.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2946-2953
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• 1999

Finite automation is a mathematical model to represent a system with discrete inputs and outputs. Finite automata are a useful tool for solving problems such as text editor, lexical analyzer, and switching circuit. In this paper, given a deterministic finite automaton of an input string of length n and m states, we propose a constant time parallel algorithm that represents the transition states of finite automata and determines the acceptance of an input string on a reconfigurable mesh of size [nm/2]$\times$2m.