• Journal of Ocean Engineering and Technology
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• v.15 no.4
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• pp.108-114
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• 2001

Petri nets have been introduced as a powerful analyzing and design tool for the discrete systems such as sequential control systems. However, one of the important problems in its applications is that the model can be analyzed hardly when we deal with large scale systems because of increase of the number of Petri net components. To overcome this problem, some methods for dividing or reducing of Petri net have been suggested. In this paper, an approach for hierarchical expression of Petri net based on Sequential function Chart(SFC) and Bottom Up oriented Mehodology(BUM) is proposed. Especially, some definitions and rules are defined in order to divide and compose sub Petri nets. A measuring tank system will be described as a typical kind of discrete systems and modeled by some sub Petri nets based on the SFC and BUM by the proposed method in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1341-1345
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• 2005

Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.26-31
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• 2001

Petri nets(PN) have been introduced as a poweful analyzing and design tool for the discrete systems such as sequential control systems. However, one of the important problems in its applications is that the model can not be analyzed easily when we deal with large scale systems because of increase of the number of components of the systems. To overcome this problem, some methods for dividing or reducing of PN have been suggested. In this paper, an approach for hierarchical expression of PN based on Sequential Function Chart(SFC) and Bottom Up oriented Mehodology(BUM) is proposed. Especially, some definition and rules are defined in order to divide and compose sub PN. A measuring tank system will be described as a typical kind of discrete systems and modeled by some sub PN based on the SFC and BUM by the proposed method in this paper.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.20-22
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• 2015

Recently, the study of smart phone user habits has become a highly focused topic due to the rapid growth of the smart phone market. Indeed, sequential pattern analysis methods were efficiently used for web-based user habit mining long time ago. However, by means of simulations, it has been observed that these methods might fail for smart phone-based user habit mining. In this paper, we propose a novel approach that leads to a considerably increased performance of the traditional sequential pattern analysis methods by reasonably cutting off each chronological sequence of user logs on a device into shorter ones, which represent the sequential user activities in various periods of a day.

• Asia-Pacific Journal of Business
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• v.11 no.1
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• pp.77-91
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• 2020

Purpose - The purpose of this study is to examines the effect of corruption pressure in host country on sequential investment. The study further investigates how the information acquisition capacity of MNEs and the political tie in the host country had a moderating effect on the relationship between corruption and sequential investment. Design/methodology/approach - Ordered logistic regression is hired to analyze 1,260 MNEs' sequential investment in Vietnam. Findings - The empirical results of this study demonstrate the more MNEs perceive the strong level of pressure to be corrupt in the local market, the less they are likely to invest. The information acquisition capacity of MNEs has been shown to mitigate the negative effects of corruption pressures on sequential investments, while the moderating effect of political tie in host country is partially supported. Research implications or Originality - This study identified that the corruption pressures of host countries negatively affect not only MNEs that are entering, but also the ones that have already entered host countries; the corruption discourages any sequential investment for existing MNEs. By suggesting two moderating variables, this study will provide managerial implications for MNEs and managers who face corruption pressure in host countries.

• Journal of Ocean Engineering and Technology
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• v.13 no.3B
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• pp.106-115
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• 1999

Modeling a discrete event system such as a sequential control system is difficult compared with a continuous system. Petri nets have been introduced as an analyzing and design tool for the discrete systems. One of the problems in its applications is that the model can not be analyzed easily in the case of large scale or complicated systems because of increase of the number of components of the system. To overcome this problem, some methods for dividing or reducing Petri nets have been suggested. In this paper, an approach for a hierarchical expression of Petri nets based on Sequential Function Chart(SFC) is proposed. A measuring tank system will be described as a typical kind of discrete systems. The system is modeled by sub Petri nets based on SFC in order to analyze and visualize efficiently about the dynamic behaviors of the system. Some numerical simulations using state equations are performed to prove the validity of the proposed method.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1464-1470
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• 2009

RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.