• 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.

• 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 Intelligence and Information Systems
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• v.7 no.1
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• pp.27-45
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• 2001

Sequential discovery from time series data has mainly concerned about events or item sets. Recently, the research has stated to applied to the numerical data. An example is sensor information generated by checking a machine state. The numerical data hardly have the same valuers while making patterns. So, it is important to extract suitable number of pattern features, which can be transformed to events or item sets and be applied to sequential pattern mining tasks. The popular methods to extract the patterns are sliding window and clustering. The results of these methods are sensitive to window sine or clustering parameters; that makes users to apply data mining task repeatedly and to interpret the results. This paper suggests the method to retrieve pattern features making numerical data into vector of an angle and a magnitude. The retrieved pattern features using this method make the result easy to understand and sequential patterns finding fast. We define an inclusion relation among pattern features using angles and magnitudes of vectors. Using this relation, we can fad sequential patterns faster than other methods, which use all data by reducing the data size.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.805-812
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• 2005

Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.85-110
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• 2005

OWL-S has a major leadership in the field of Web Service discovery and is being actively studied in LARKS and METEOR-S projects. These researches do not consider all components of OWL-S standards, and it is needed to enhance their discovery algorithms. In this paper, we propose matching algorithms based on process information such as process structure matching, service classification matching and business pattern matching algorithms. We also improve the QoS matching algorithm of METEOR-S project. Finally, we integrate these two kinds of matching algorithms as accommodate users preferences.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.3
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• pp.328-337
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• 2017

Objective: An experiment was conducted to determine the relationship between the KAP11.1 and the regulation wool fineness. Methods: In previous work, we constructed a skin cDNA library and isolated a full-length cDNA clone termed KAP11.1. On this basis, we conducted a series of bioinformatics analysis. Tissue distribution of KAP11.1 mRNA was performed using semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) analysis. The expression of KAP11.1 mRNA in primary and secondary hair follicles was performed using real-time PCR (real-time polymerase chain reaction) analysis. The expression location of KAP11.1 mRNA in primary and secondary hair follicles was performed using in situ hybridization. Results: Bioinformatics analysis showed that KAP11.1 gene encodes a putative 158 amino acid protein that exhibited a high content of cysteine, serine, threonine, and valine and has a pubertal mammary gland) structural domain. Secondary structure prediction revealed a high proportion of random coils (76.73%). Semi-quantitative RT-PCR showed that KAP11.1 gene was expressed in heart, skin, and liver, but not expressed in spleen, lung and kidney. Real time PCR results showed that the expression of KAP11.1 has a higher expression in catagen than in anagen in the primary hair follicles. However, in the secondary hair follicles, KAP11.1 has a significantly higher expression in anagen than in catagen. Moreover, KAP11.1 gene has a strong expression in inner root sheath, hair matrix, and a lower expression in hair bulb. Conclusion: We conclude that KAP11.1 gene may play an important role in regulating the fiber diameter.

• The Journal of the Korea Contents Association
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• v.8 no.9
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• pp.19-26
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• 2008

With the development of ubiquitous computing and the construction of infrastructure for one-to-one personalized services, the importance of context-aware services based on user's situation and environment is being spotlighted. The smart home technology connects real space and virtual space, and converts situations in reality into information in a virtual space, and provides user-oriented intelligent services using this information. In this paper, we proposed the discovery of the behavior sequence pattern using the mining in the smart home. We discovered the behavior sequence pattern by using mining to add time variation to the association rule between locations that occur in location transactions. We can predict the path or behavior of user according to the recognized time sequence and provide services accordingly. To evaluate the performance of behavior consequence pattern using mining, we conducted sample t-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.115-126
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• 2000

In General, data mining is defined as the knowledge discovery or extracting hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important works is to find association rules in data mining. Association Rule is mainly being used in basket analysis. In addition, it has been used in the analysis of web-log and user-pattern. This paper provides the application method in the field of marketing through the analysis of data using association rule as a technique of data mining.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.13 no.1
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• pp.48-53
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• 2017

We diagnosed the defect using the data obtained from the nuclear power plant simulator. In this paper, we diagnosed faults in the nuclear power plant system for discovery instead of the traditional single-component or device unit. We created the six fault scenarios and used a fault simulator to obtain the fault data. It was extracted pattern from acquired failure data. Neural network model was trained and simple pattern matching algorithm was applied. We presented a simulation result and confirmed that the applied algorithm works correctly.

• The Journal of Pediatrics of Korean Medicine
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• v.33 no.3
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• pp.42-55
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• 2019

Objectives The purpose of this study is to figure out the tendency of the commonly-used-pattern-identification and treatment for Hyperhidrosis by reviewing Korean clinical studies. Methods 18 articles which were published from August, 2004 to December, 2018. were obtained from the National discovery for science leader (NDSL), Research information sharing service (RISS), and Oriental medicine advanced searching integrated system (OASIS) by using keyword 'hyperhidrosis'. Results The most commonly-used-pattern-identification were the patterns with 'Heart' and 'Spleen-stomach'. Hyungbangsabaek-san and Taeeumjowi-tang were the most frequently used herbal medicine. The most common acupoints was LI4. The most common method of assessment was VAS. Conclusions This study identifies the most common pattern identification and treatment for hyperhidrosis. Developing systematic standards of pattern identification and treatment can be possible with further studies.