• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.11
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• pp.1760-1767
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• 2004

Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

• Journal of the Chungcheong Mathematical Society
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• v.33 no.4
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• pp.405-412
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• 2020

In this paper we introduce the category of closuresequential approach spaces, CSEQ. And study some properties of closure-sequential approach space.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.9 s.240
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• pp.1217-1224
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• 2005

To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.55-61
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• 2007

Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximum distance, mean squared error, and maximum entropy sequential samplings. Through this research we team that the proposed approach is computationally inexpensive and provides good prediction performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.119-130
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• 2012

Recently, the software system is getting complicating and the customers are requiring faster development. For the traditional sequential approach can't against this problem iterative approach is used instead. For the representative iterative approach, there is RUP (Rational's Unified Process). However, RUP standard practical methods are phase, iteration, and disciplines, sequentially. As a result, there's some waste of manpower when a discipline is executed in an iteration, it has to wait till the next same discipline is executed. There are linear approach, sequential approach, overlapped iteration approach, and time-boxed iteration for the efficient execution of RUP. However, they have some problems such as waste of manpower or difficulty in the project management. This paper suggests a method about how to execute the disciplines as a concurrent type. The concurrent approach prevents the waste of manpower and solves the difficulty of project management.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.131-137
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• 2005

In this paper, we propose a Bayesian approach to determine the adaptive preventive maintenance(PM) policy for a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) that PM not only reduces the effective age of the system but also changes the hazard rate function. Assuming that the failure times follow Weibull distribution, we adopt a Bayesian approach to update unknown parameters and determine the Bayesian optimal sequential PM policies. Finally, numerical examples of the optimal adaptive PM policy are presented for illustrative purposes.

• Journal of Distribution Research
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• v.10 no.3
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• pp.87-101
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• 2005

Market segmentation has been an important issue in marketing for a long time. Many models and statistical methods have been developed by many scholars. The purpose of this research provides one insight for market segmentation based on clustering technique in channel benefit sought. We proposed a sequential approach in market segmentation. A sequential approach means that we do market segmentation by multi-stage method based on the benefits sought in marketing channel. To achieve this approach, we divided the main benefits sought into subcategories. That is to say, after dividing each benefit sought into more detailed concepts, we did market segmentation sequentially.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.4
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• pp.581-590
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• 2014

In approach theory, we can provide arbitrary products of ${\infty}p$-metric spaces with a natural structure, whereas, classically only if we rely on a countable product and the question arises, then, whether properties which are derived from countability properties in metric spaces, such as sequential and countable compactness, can also do away with countability. The classical results which simplify the study of compactness in pseudometric spaces, which proves that all three of the main kinds of compactness are identical, suggest a further study of the category $pMET^{\infty}$.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.191-204
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• 2006

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.