• Journal of Digital Convergence
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• v.12 no.12
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• pp.365-370
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• 2014

The existing system consumes a considerable amount time and cost for extracting the patterns from whole sequences or misaligned sequences. In this paper, We propose the classification system, which creates the partition sequence sections using multiple sequence alignment method and extracts the sequential patterns from these section. These extracted patterns are accumulated motif candidate sets and then used the training sets of SVM classifier. This proposed system predicts a HPV-type(high/low) using the learned knowledges from known/unknown protein sequences and shows more improved precision, recall than previous system in 30% minimum support.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.3
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• pp.517-526
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• 1987

This paper proposes a new testable design for Zipper CMOS circuits. This design provides an additional feedback loop (called self oscillation loop) whichin the circuit, for testability. The circuit is tested only by observing the oscillation on the loop. The design can be applied to the multistage as well as the single stage, and can detect multiple faults which are undetectable by the conventional testing method. The application and evaluation of test patterns become easy and fault-free responses are not necessary. If the conventional testing method is applied to the sequential Zipper CMOS circuit with the LSSD design technique, it has the serious defect that the initial value may change due to intermediate test patterns and much time taken to apply the necessary test patterns. By using the proposed design, however, the sequential Zipper CMOS circuit with the LSSD design technique can be easily tested without such a defect. Also, the validity of the design is verified by performing the circuit level simulation.

• Proceedings of the KSRS Conference
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• v.2
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• pp.627-630
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.457-462
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• 2006

Sequential Pattern Mining is the mining approach which addresses the problem of discovering the existent maximal frequent sequences in a given databases. In the daily and scientific life, sequential data are available and used everywhere based on their representative forms as text, weather data, satellite data streams, business transactions, telecommunications records, experimental runs, DNA sequences, histories of medical records, etc. Discovering sequential patterns can assist user or scientist on predicting coming activities, interpreting recurring phenomena or extracting similarities. For the sake of that purpose, the core of sequential pattern mining is finding the frequent sequence which is contained frequently in all data sequences. Beside the discovery of frequent itemsets, sequential pattern mining requires the arrangement of those itemsets in sequences and the discovery of which of those are frequent. So before mining sequences, the main task is checking if one sequence is a subsequence of another sequence in the database. In this paper, we implement the subsequence matching method as the preprocessing step for sequential pattern mining. Matched sequences in our implementation are the normalized sequences as the form of number chain. The result which is given by this method is the review of matching information between input mapped sequences.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1919-1922
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• 2003

Bypass anastomosis are frequently adopted for surgical treatments. After the bypass grafting, the bypass artery is often occluded due to restenosis and/or anastomotic neointimal fibrous hyperplasia phenomena. Optimal coronary bypass anastomosis should be investigated to improve the patency for the arterial bypass techniques. The objective of this study is to investigate the influence of bypass with sequential bypass effects in the stenosed coronary artery. Numerical analyses are focused on the understanding of the flow patterns for different sequential anastomosis techniques. Blood flow field is treated as two-dimensional incompressible laminar flow. The finite volume method is adopted for discretization of the governing equations. The Carreau model is employed as the constitutive equation for blood. To find the optimal sequential bypass anastomotic configurations, the mass flow rates at the outlet of different models are compared quantitatively.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.741-753
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• 2006

As the total amount of traffic data in network has been growing at an alarming rate, many researches to mine traffic data with the purpose of getting useful information are currently being performed. However, network users' privacy can be compromised during the mining process. In this paper, we propose an efficient and practical privacy preserving sequential pattern mining method on network traffic data. In order to discover frequent sequential patterns without violating privacy, our method uses the N-repository server model and the retention replacement technique. In addition, our method accelerates the overall mining process by maintaining the meta tables so as to quickly determine whether candidate patterns have ever occurred. The various experiments with real network traffic data revealed tile efficiency of the proposed method.

• Journal of Internet Computing and Services
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• v.10 no.2
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• pp.1-13
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• 2009

Intrusion detection is a process that identifies the attacks and responds to the malicious intrusion actions for the protection of the computer and the network resources. Due to the fast development of the Internet, the types of intrusions become more complex recently and need immediate and correct responses because the frequent occurrences of a new intrusion type rise rapidly. Therefore, to solve these problems of the intrusion detection systems, we propose a sequential pattern miner for analysis of the alert data in order to support intelligent and automatic detection of the intrusion. Sequential pattern mining is one of the methods to find the patterns among the extracted items that are frequent in the fixed sequences. We apply the prefixSpan algorithm to find out the alert sequences. This method can be used to predict the actions of the sequential patterns and to create the rules of the intrusions. In this paper, we propose an extended prefixSpan algorithm which is designed to consider the specific characteristics of the alert data. The extended sequential pattern miner will be used as a part of alert data analyzer of intrusion detection systems. By using the created rules from the sequential pattern miner, the HA(high-level alert analyzer) of PEP(policy enforcement point), usually called IDS, performs the prediction of the sequence behaviors and changing patterns that were not visibly checked.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.29-34
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• 2001

In this paper, a new associative memory system for analog time-sequential data processing is proposed. This system effectively associate time-sequential data using not only matching with present data but also matching with past data. Furthermore in order to improve error correction ability, weight varying in time domain is introduced in this system. The network is simulated with several periodic time-sequential input patterns including noise. The results show that the proposed system has ability to correct input errors. We expect that the proposed system may be applied for a real time processing of analog time-sequential information.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.19-29
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• 2011

Due to the development of web technologies and the increasing use of smart devices such as smart phone, in recent various web services are widely used in many application fields. In this environment, the topic of supporting personalized and intelligent web services have been actively researched, and an analysis technique on a web-click stream generated from web usage logs is one of the essential techniques related to the topic. In this paper, for efficient analyzing a web-click stream of sequences, a sequential pattern mining technique is proposed, which satisfies the basic requirements for data stream processing and finds a refined mining result. For this purpose, a concept of interesting sequential patterns with a time-interval constraint is defined, which uses not on1y the order of items in a sequential pattern but also their generation times. In addition, A mining method to find the interesting sequential patterns efficiently over a data stream such as a web-click stream is proposed. The proposed method can be effectively used to various computing application fields such as E-commerce, bio-informatics, and USN environments, which generate data as a form of data streams.

• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.