• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.53-63
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• 2008

In this paper, we propose the regional travel time measurement algorithm using the sequence pattern matching to the type of vehicles between the origin of the region and the end of the region, that could be able to overcome the limit of conventional method such as Probe Car Method or AVI Method by License Plate Recognition. This algorithm recognizes the vehicles as a sequence group with a definite length, and measures the regional travel time by searching the sequence of the origin which is the most highly similar to the sequence of the end. According to the assumption of similarity cost function, there are proposed three types of algorithm, and it will be able to estimate the average travel time that is the most adequate to the information providing period by eliminating the abnormal value caused by inflow and outflow of vehicles. In the result of computer simulation by the length of region, the number of passing cars, the length of sequence, and the average maximum error rate are measured within 3.46%, which means that this algorithm is verified for its superior performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.85-91
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• 2016

Typically, radar systems change the pulse repetition interval of their modulated signal in order to avoid detection. On the other hand the radar-signal detection system tries to detect the modulation pattern. The histogram or auto-correlation methods are usually used to detect the PRI pattern of the radar signal. However these methods tend to lost the sequence information of the PRI pulses. This paper proposes a PRI-sequence detection algorithm based on the finite-state machine that could detect not only the PRI pattern but also their sequence.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Korean Institute of Interior Design Journal
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• no.30
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• pp.78-85
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• 2002

Human exists in environment. As environment affects in human movement, human reacts to everything happens in environment especially by the view point of visual continuity and changeability. This study has two purposes. The one is to clarify the visual changeability due to the Human movement from the visual point based on checking the visual field. And the other is to understand the applicable possibility of Philip Thiel's method through the experiment in passing ways. Condition of this study is that colors and figures are affective elements of visual environmental sequence by the Human movement. The Human movement is due to the visual phenomenon. That means it is not limited in Philip Theil's method(Node, District). In particular, the chroma which is checked by the BPA(Basic-Pattern-Area) is the most affective visual environmental element in contemporary shopping mall. Also, everything in visual environment and the movement is connected by the time axis. As an analytical method, the sequence notation devised by Philip Thiel was applied.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1527-1543
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• 2017

MiRNA is a biological short sequence, which plays a crucial role in almost all important biological process. MiRNA patterns are common sequence segments of multiple mature miRNA sequences, and they are of significance in identifying miRNAs due to the functional implication in miRNA patterns. In the proposed approach, the primary miRNA patterns are produced from sequence alignment, and they are then cut into short segment miRNA patterns. From the segment miRNA patterns, the candidate miRNA patterns are selected based on estimated probability, and from which, the potential miRNA patterns are further selected according to the classification performance between authentic and artificial miRNA sequences. Three parameters are suggested that bi-nucleotides are employed to compute the estimated probability of segment miRNA patterns, and top 1% segment miRNA patterns of length four in the order of estimated probabilities are selected as potential miRNA patterns.

• 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.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.77-80
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• 2000

In this paper, the speaker-recognition process based on both DTW and discrete HMM was performed using the method to evaluate state-dependent parameter weighting from training data so as the personal audio-characteristics are to be well reflected. In the suggested method below, we found the optimal state sequence using the Viterbi algorithm. The optimal path could be evaluated after comparing the sequence of base pattern which already have, with that of the other patterns. After that the frame of which the pattern was matched with the base pattern in the same state are to be found so that the reference pattern can be gained by weighting on the numbers of matched frames.

• ETRI Journal
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• v.32 no.5
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• pp.676-686
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• 2010

Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high-utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.

• Korean Journal of Microbiology
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• v.34 no.1_2
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• pp.6-10
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• 1998

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.237-248
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• 2009

Recently, based on dynamic location or mobility of moving object, many researches on pattern mining methods actively progress to extract more available patterns from various moving patterns for development of location based services. The performance of moving pattern mining depend on how analyze and process the huge set of spatio-temporal data. Some of traditional spatio-temporal pattern mining methods[1-6,8-11]have proposed to solve these problem, but they did not solve properly to reduce mining execution time and minimize required memory space. Therefore, in this paper, we propose new spatio-temporal pattern mining method which extract the sequential and periodic frequent moving patterns efficiently from the huge set of spatio-temporal moving data. The proposed method reduces mining execution time of $83%{\sim}93%$ rate on frequent moving patterns mining using the moving sequence tree which generated from historical data of moving objects based on hash tree. And also, for minimizing the required memory space, it generalize the detained historical data including spatio-temporal attributes into the real world scope of space and time using spatio-temporal concept hierarchy.