• 대한기계학회논문집A
• /
• 제28권11호
• /
• pp.1760-1767
• /
• 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.

• 충청수학회지
• /
• 제33권4호
• /
• pp.405-412
• /
• 2020

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

• 대한기계학회논문집A
• /
• 제29권9호
• /
• pp.1217-1224
• /
• 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
• /
• 제32권5호
• /
• pp.676-686
• /
• 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.

• 대한기계학회논문집A
• /
• 제31권1호
• /
• pp.55-61
• /
• 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.

• 한국인터넷방송통신학회논문지
• /
• 제12권4호
• /
• pp.119-130
• /
• 2012

최근 들어 소프트웨어 시스템은 점차적으로 복잡해지고 있으며, 고객(customer)은 보다 빠른 개발을 요구하고 있다. 전통적인(traditional) 순차적 접근법 (Sequential Approach)으로는 이러한 압력에 효과적으로 대처할 수 없어 대안으로 반복적 접근법 (Iterative Approach)이 적용되고 있다. 대표적인 반복적 접근법으로는 래쇼날의 단일화된 프로세스 (Rational Unified Process, RUP)가 있다. 그러나 RUP의 표준화된 수행방법은 단계, 반복과 활동들을 모두 순차적으로 수행하는 형태이다. 그 결과, 하나의 반복에서 수행된 하나의 활동은 다음 반복의 해당 활동이 수행될 때까지 기다려야 하는 인력낭비 현상이 발생한다. RUP를 수행하는 방법으로는 선형 접근법, 순차적 접근법, 중첩된 반복 접근법과 Time-boxed 반복 접근법이 제안되었다. 그러나 이들 방법은 인력낭비 현상 또는 적용시 프로젝트 관리의 어려움이라는 문제점을 갖고 있다. 본 논문은 활동들을 동시에 수행하는 방법을 제안하였다. 동시개발 접근법은 인력 낭비 현상을 방지할 수 있으며, 프로젝트 관리의 어려움도 해결할 수 있는 장점을 갖고 있다.

• 한국신뢰성학회:학술대회논문집
• /
• 한국신뢰성학회 2005년도 학술발표대회 논문집
• /
• pp.131-137
• /
• 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.

• 한국유통학회지:유통연구
• /
• 제10권3호
• /
• pp.87-101
• /
• 2005

본 연구에서는 시장세분화의 기준들이 가질 수 있는 다차원적인 특성들을 고려하여 시장 세분화를 순차적으로 접근하였다. 소비자들이 유통형태를 선택할 때 추구하는 해택을 중심으로 순차적 세분화를 분석하기 위하여 서울시내의 20세 이상 55세 미만의 가정주부들이 모집단으로 정의되고, 이 중에서 할당추출방법에 의해 수집된 1000명의 표본이 이용되었다. 애프터서비스 추구혜택 세분시장 이외에 4개의 세분시장을 각 세분시장의 1차 추구혜택변수에 부가된 2차적 혜택변수를 기준으로 군집분석을 실시한 결과, 각 세분시장은 2개의 또 다른 하위세분시장으로 나누어졌다. 끝으로 이러한 결과의 시사점이 논의되고 연구의 한계점과 미래의 연구방향이 제시되었다.

• 충청수학회지
• /
• 제27권4호
• /
• pp.581-590
• /
• 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
• /
• 제13권1호
• /
• pp.191-204
• /
• 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.