• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.2
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• pp.168-176
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• 2015

Due to the reduction in device feature size, transient faults (soft errors) in logic circuits induced by radiations increase dramatically. Many researches have been done in modeling and analyzing the susceptibility of sequential circuit elements caused by soft errors. However, to the best knowledge of the authors, there is no work which has well considerated the feedback characteristics and the multiple clock cycles of sequential circuits. In this paper, we present a new method for evaluating the susceptibility of sequential circuit elements to soft errors. The proposed method uses four Error Propagation Probability Matrixs (EPPMs) to represent the error propagation probability of logic gates and flip-flops in current clock cycle. Based on the predefined matrix union operations, the susceptibility of circuit elements in multiple clock cycles can be evaluated. Experimental results on ISCAS'89 benchmark circuits show that our method is more accurate and efficient than previous methods.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.564-571
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• 1996

The logic synthesis systems for table look up(TLU) type field programmable e gate arrays(FPGAs) have so farstudied mostly the combinational logic problem m. This paper presents for mapping a sequential circuit onto a popular table look up architecture, theXilinx 3090 architecture. In thefirst for solving this problem, combinational and sequential elements which have 6 or7 input combinational and sequential elements which haveless thanor equal to 5 inputs. We heavily use the combinational synthesis techniques tosolve the sequential synthesis problem. Our syntheisis approach is very simple, but its results are reasonable. We compare seveal benchmark Examples with sis-pga(map_together and map_separate) synthesis system and the experimental results show that our synthesis system is 17% betterthan sis-pga sequential synthesis system for TLU PGAs.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.115-118
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• 2003

In this paper we implemented a field-sequential stereoscopic endoscope system that can generate stereoscopic images with different perspective depth using LCD stutter. Re stereoscopic image is generated from stereoscopic adapter that has LCD shutter. We have compared the stereoscopic depth of a field-sequential stereoscopic endoscope system with that of the conventional endoscope system. And the implemented system is verified by evaluation the field-sequential stereoscopic image on a Monitor. This system will be use to medical instruments in time.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.271-279
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• 2001

Sequential pattern discovery is one of main interests in web usage mining. the technique of sequential pattern discovery attempts to find inter-session patterns such that the presence of a set of items is followed by another item in a time-ordered set of server sessions. In this paper, a tree-based sequential pattern finding method is proposed in order to discover navigation patterns in server sessions. At each learning process, the suggested method learns about the navigation patterns per server session and summarized into the modified Rymon's tree.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.479-488
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• 2007

A sequential procedure is proposed in order to construct one-sided confidence intervals for a normal mean with guaranteed coverage probability and ${\beta}-protection$ when the normal mean and variance are identical. First-order asymptotic properties on the sequential sample size are found. The derived results hold with uniformity in the total parameter space or its subsets.

• Korea Trade Review
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• v.45 no.6
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• pp.1-22
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• 2020

This paper analyzes the influence of entrepreneurial orientation, international market orientation, and investment performance of Korean SMEs in Vietnam on sequential investment. As a result of analyzing the research model using PLS SEM, it was found that Korean SMEs make sequential investments based on investment performance rather than entrepreneurial orientation or international market orientation. In addition, entrepreneurial orientation increased internationa market orientation and had a positive effect on investment performance, which was found to have a positive effect on sequential investment. Through this study, it was clarified that there is a difference between the determinants of initial investment and sequential investment, and it was confirmed that Korean companies show stable and strategic sequential investment tendency rather than proactive and bold investment in Vietnam. In addition, the mediating effect of international market orientation and investment performance in sequential investment was confirmed. In addition, it was confirmed that entrepreneurial orientation was a valid factor in the indirect effect of sequential investment. In the future, for high entrepreneurial orientation Korean companies entered the Vietnamese market, there will be a need for policy support to provide information on Global Value Chain in Vietnam and establish networks on the country.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4A
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• pp.558-565
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• 2000

While research of ATPG(automatic test pattern generation) for combinational circuits almost reaches a satisfiable level, one for sequential circuits still requires more research. In this paper, we propose new algorithm for sequential ATPG based on state information learning. By efficiently storing the information of the state searched during the process of test pattern generation and using the state information that has been already stored, test pattern generation becomes more efficient in time, fault coverage, and the number of test patterns. Through some experiments with ISCAS '89 benchmark circuits, the efficiency of the proposed method is shown.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.29-36
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• 2009

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, is an important data mining problem with broad applications. Since a sequential pattern in DNA sequences can be a motif, we studied to find sequential patterns in DNA sequences. Most previously proposed mining algorithms follow the exact matching with a sequential pattern definition. They are not able to work in noisy environments and inaccurate data in practice. Theses problems occurs frequently in DNA sequences which is a biological data. We investigated approximate matching method to deal with those cases. Our idea is based on the observation that all occurrences of a frequent pattern can be classified into groups, which we call approximated pattern. The existing PrefixSpan algorithm can successfully find sequential patterns in a long sequence. We improved the PrefixSpan algorithm to find approximate sequential patterns. The experimental results showed that the number of repeats from the proposed method was 5 times more than that of PrefixSpan when the pattern length is 4.

• 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.