• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.743-752
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• 2006

This paper considers a Bayesian approach to replacement policy model with minimal repair. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal maintenance policy. Especially, the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.202-208
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• 2001

One of the most frequent uses of Internet is data gathering. Data can be about many themes but perhaps one of the most demanded fields is the tourist information. Normally, databases that support these systems are maintained manually. However, there is other approach, that is, to extract data automatically, for instance, from textual public information existing in the Web. This approach consists of extracting data from textual sources(public or not) and to serve them totally or partially to the user in the form that he/she wants. The obtained data can maintain automatically databases that support different systems as WAP mobile telephones, or commercial systems accessed by Natural Language Interfaces and others. This process has three main actors. The first is the information itself that is present in a particular context. The second is the information supplier (extracting data from the existing information) and the third is the user or information searcher. This added value chain reuse and give value to existing data even in the case that these data were not tough for the last use by the use of the described technology. The main advantage of this approach is that it makes independent the information source from the information user. This means that the original information belongs to a particular context, not necessarily the context of the user. This paper will describe the application based on this approach developed by the authors in the FLEX EXPRIT IV n$^{\circ}$EP29158 in the Work-package "Knowledge Extraction & Data mining"where the information captured from digital newspapers is extracted and reused in tourist information context.

• 한국IT서비스학회지
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• 제11권3호
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• pp.1-16
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• 2012

This paper is aimed to present a fuzzy decision-making approach to deal with disaster recovery priority decision problem in information system. We derive an evaluation approach based on TOPSIS(Technique for Order Performance by Similarity to Ideal Solution), to help disaster recovery priority decision of total information system for port logistics in a fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by trapezoidal fuzzy numbers. This study applies the fuzzy multi-criteria decision-making method to determine the importance weight of evaluation criteria and to synthesize the ratings of candidate disaster recovery system. Aggregated the evaluators' attitude toward preference, then TOPSIS is employed to obtain a crisp overall performance value for each alternative to make a final decision. This approach is demonstrated with a real case study involving 4 evaluation criteria(system dependence, RTO, loss, alternative business support), 7 information systems for port logistics assessed by 5 evaluators from Maritime Affairs and Port Office.

• 한국정보통신학회논문지
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• 제10권1호
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• pp.206-211
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• 2006

수량화 문제는 n개의 성질을 갖는 m개의 개체들을 각 개체들의 유사도(similarity)를 가장 잘 반영하도록 p차원의 공간 상에 대응시키는 문제이다. 본 논문에서는 물리학에서의 열평형 상태(thermal equilibrium state)에서 분자시스템의 해석적 근사 움직임에 대한 이론인 평균장 이론(mean field theory)에 의한 분자의 평균 변화량 계산과 어닐링(annealing) 방법에 의한 평균장 신경회로망(mean field neural network)을 수량화 문제(quantification analysis problem)의 해결에 적용하였다. 그 결과, 제안한 최적화 응용기법 이 기존의 고유치 분석방법(eigen value analysis)에 비해 비용측면에서 좀 더 최적에 가까운 해답을 찾아낼 수 있음을 확인하였다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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• pp.55-62
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• 2006

Huang proposed a grey-based nearest neighbor approach to predict accurately missing attribute value in 2004. Our study proposes which way to decide the number of nearest neighbors using not only the dong's grey relational grade but also the wen's grey relational grade. Besides, our study uses not an arithmetic(unweighted) mean but a weighted one. Also, GRG is used by a weighted value when we impute a missing values. There are four different methods - DU, DW, WU, WW. The performance of WW(wen's GRG & weighted mean) method is the best of my other methods. It had been proven by Huang that his method was much better than mean imputation method and multiple imputation method. The performance of our study is far superior to that of Huang.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.467-469
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• 1989

A new singular value approach is presented and analized in order to determine the number of multi pie sinsoidal frequencies from the finite noisy data. Simulations are conducted for Akaike's information criterion(AIC), Rissanen's shortest data description(MDL) and a new singular value approach, in covariance matrix based methods. And then performances are compared.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.650-651
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• 2015

Value Engineering has been recognized as one of the construction management techniques for improving the value and benefits of whole project. However, due to the lack of the past data and inefficient free-thinking techniques, the idea generation during VE workshop is still inefficient and ineffective. Even though various studies related to theoretical methodology and technical systems relevant to database were conducted, VE team still mainly rely on their experience for idea generation. With this regard, this study suggests an approach of BIM-based idea bank and assesses its effectiveness by interviewing 20 VE experts in the industry. This approach covers the three steps of idea generation, consisting of 1) Developing BIM based VE database, 2) Generating VE ideas, 3) Updating VE ideas. The result showed that the proposed approach has great potentials to support VE team and improve the quality of VE ideas during creativity phase.

• Structural Engineering and Mechanics
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• 제74권1호
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• pp.55-67
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• 2020

The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.

• Asia pacific journal of information systems
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• 제26권4호
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• pp.577-592
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• 2016

The proliferation of mobile applications and the unique characteristics of the mobile environment have attracted significant research interest in understanding customers' purchasing behaviors in mobile commerce. In this study, we extend customer value theory by combining the predictors of product performance with customer value framework to investigate how in-store information creates value for customers and influences mobile application downloads. Using a data set collected from the Google Application Store, we find that customers value both text and non-text information when they make downloading decisions. We apply latent semantic analysis techniques to analyze customer reviews and product descriptions in the mobile application store and determine the embedded valuable information. Results show that, for mobile applications, price, number of raters, and helpful information in customer reviews and product descriptions significantly affect the number of downloads. Conversely, average rating does not work in the mobile environment. This study contributes to the literature by revealing the role of in-store information in mobile application downloads and by providing application developers with useful guidance about increasing application downloads by improving in-store information management.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1331-1334
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• 2002

Micropayment is an electronic payment system for small value transaction. It needs to use a little amount of resources, such as communication and computation due to its small value. In other words, the processing cost for the micropayment must be less than the value of the payment. Several kinds of transactions are suitable for micropayment, eg. the purchasing of train tickets or digital newspapers. Since micropayment systems are designed for small-amount payment the key factor for any micropayment system design is believed to be the minimization of resource consumption without compromising the standard security. In this paper, we propose an adaptive agent approach to credit-based micropayment system, which employs the concept of dynamic balancing between the resource consumption and the risk in the system. As a result of the dynamic balancing, our system not only solves the problem of global overspending but also uses fewer amount of resources than existing approaches. Our approach limits the amount of money spent by untrusted customers to all merchants. Thus, our approach provides a boundary of the global overspending. In addition, for trusted customers, our approach requires less scale of communication for verifying authorizations than all existing approaches.