• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.158-162
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• 2009

In this paper, we propose a quaternary sequence with good autocorrelation property. New quaternary sequence is generated by using inverse Gray mapping and binary sequence with ideal autocorrelation. And the maximum magnitude of nontrivial correlation value is 3. Proposed quaternary sequence has only one 1 in one period, so called almost binary sequence. Therefore the balance property is not good, but value of its weight function is nearly 0.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.221-229
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• 2004

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, only a few of the existing clustering algorithms consider sequentiality. This study presents a method for clustering such sequence datasets. The similarity between sequences must be decided before clustering the sequences. This study proposes a new similarity measure to compute the similarity between two sequences using a sequence element. Two clustering algorithms using the proposed similarity measure are proposed: a hierarchical clustering algorithm and a scalable clustering algorithm that uses sampling and a k-nearest neighbor method. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed clustering algorithms is better than that of clusters produced by traditional clustering algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4645-4664
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• 2019

It is significant to construct deterministic measurement matrices with easy hardware implementation, good sensing performance and good cryptographic property for practical compressed sensing (CS) applications. In this paper, a deterministic construction method of bipolar chaotic measurement matrices is presented based on binary sequence family (BSF) and Chebyshev chaotic sequence. The column vectors of these matrices are the sequences of BSF, where 1 is substituted with -1 and 0 is with 1. The proposed matrices, which exploit the pseudo-randomness of Chebyshev sequence, are sensitive to the initial state. The performance of proposed matrices is analyzed from the perspective of coherence. Theoretical analysis and simulation experiments show that the proposed matrices have limited influence on the recovery accuracy in different initial states and they outperform their Gaussian and Bernoulli counterparts in recovery accuracy. The proposed matrices can make the hardware implement easy by means of linear feedback shift register (LFSR) structures and numeric converter, which is conducive to practical CS.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.391-396
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• 2008

Motion of object in a video clip often plays an important role in characterizing the content of the clip. A number of methods have been developed to analyze and retrieve video contents using motion information. However, most of these methods focused more on the analysis of direction or trajectory of motion but less on the analysis of the movement of an object itself. In this paper, we propose the shape sequence descriptor to describe and compare the movement based on the shape deformation caused by object motion along the time. A movement information is first represented a sequence of 2D shape of object extracted from input image sequence, and then 2D shape information is converted 1D shape feature using the shape descriptor. The shape sequence descriptor is obtained from the shape descriptor sequence by frequency transform along the time. Our experiment results show that the proposed method can be very simple and effective to describe the object movement and can be applicable to semantic applications such as content-based video retrieval and human movement recognition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.104-111
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• 2008

A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about processes from event-log, and to discover process of alternative, concurrent and hidden activities. Some numerical examples are presented to show the effectiveness and efficiency of the algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1588-1595
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• 2019

This paper presents the technique on structuring information of the diagram repository for UML diagrams. Because object interactions are the body of object-oriented programming, this paper handles especially the sequence diagrams and class diagrams among UML diagrams. Based on class diagrams, sequence diagrams represent the procedure of object interactions in run-time and then the corresponding codes are generated from the contents of those sequence diagrams. To do this work, this paper presents a method to construct the information repository for generating code from the contents of sequence diagrams. This paper classifies the five message types of sequence diagrams and then extracts the needed information including items and values on the corresponding message types for constructing message repositories. Because sequence diagram is composed of messages included, the final repository is constructed by collecting each of structured repositories on messages sequentially.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2283-2291
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• 2010

The shrinking generator which is one of clock-controlled generator is a very simple generator with good cryptographic properties. A nonlinear sequence generator based on two 90/150 maximum length cellular automata can generate pseudorandom sequences at each cell of cellular automata whose characteristic polynomials are same. The nonlinear sequence generated by cellular automata has a larger period and a higher linear complexity than shrunken sequence generated by LFSRs. In this paper we analyze shrunken-interleaved sequence based on 90/150 maximum length cellular automata. We show that the sequence generated by nonlinear sequence generator based on cellular automata belongs to the class of interleaved sequence. And we give an effective algorithm for reconstructing unknown bits of output sequence based on intercepted keystream bits.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.1-10
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• 2005

Multiple sequence alignment(MSA) is a fundamental technique of DNA and Protein sequence analysis. Biological sequences are aligned vertically in order to show the similarities and differences among them. In this Paper, we Propose an effcient group alignment method, which is based on clustering divergency, to Perform the alignment between two groups of sequences. The Proposed algorithm is a clustering divergence(CDMS)-based multiple sequence alignment and a top-down approach. The algorithm builds the tree topology for merging. It is so based on the concept that two sequences having the longest distance should be spilt into two clusters. We expect that our sequence alignment algorithm improves its qualify and speeds up better than traditional algorithm Clustal-W.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1C
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• pp.9-14
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• 2009

In this paper, using the binary (N,M,L,1) low correlation zone(LCZ) sequence set with specific property, we propose the construction method of a quaternary LCZ sequence set with parameters (2N,2M,L,2). The binary LCZ sequence using this method must have period $N{\equiv}3$ mod 4, balance property, and specific correlation property. The proposed method is modified from the construction method of binary LCZ sequence set by using binary LCZ sequence with specific condition proposed by Kim, Jang, No, and Chung[4].

• Genomics & Informatics
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• v.10 no.1
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• pp.51-57
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• 2012

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.