• Computational Structural Engineering : An International Journal
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• v.1 no.2
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• pp.81-87
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• 2001

A framework for reliability analysis of structural components and systems under conditions of statistical and model uncertainty is presented. The Bayesian parameter estimation method is used to derive the posterior distribution of model parameters reflecting epistemic uncertainties. Point, predictive and bound estimates of reliability accounting for parameter uncertainties are derived. The bounds estimates explicitly reflect the effect of epistemic uncertainties on the reliability measure. These developments are enhance-ments of second-moment uncertainty analysis methods developed by A. H-S. Ang and others three decades ago.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.605-613
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• 2014

Epistemic uncertainty, the lack of knowledge, is often more important than aleatory uncertainty, variability, in estimating reliability of a system. While the probability theory is widely used for modeling aleatory uncertainty, there is no dominant approach to model epistemic uncertainty. Different approaches have been developed to handle epistemic uncertainties using various theories, such as probability theory, fuzzy sets, evidence theory and possibility theory. However, since these methods are developed from different statistics theories, it is difficult to interpret the result from one method to the other. The goal of this paper is to compare different methods in handling epistemic uncertainty in the view point of calculating the probability of failure. In particular, four different methods are compared; the probability method, the combined distribution method, interval analysis method, and the evidence theory. Characteristics of individual methods are compared in the view point of reliability analysis.

• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.64-79
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• 2003

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.2
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• pp.131-137
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• 2018

This paper analyzes the probability of failure for the equivalence ratio error. The control error of the equivalence ratio is affected by the aleatory and epistemic uncertainties. In general, reliability analysis techniques are easily incorporated to handle the aleatory uncertainty. However, the epistemic uncertainty requires a new approach, as it does not provide an uncertainty distribution. The Bayesian inference incorporates the reliability analysis results to handle both uncertainties. The result gives a distribution of failure probability, whose equivalence ratio does not meet the requirement. This technique can be useful in the analysis of most engineering systems, where the aleatory and epistemic uncertainties exist simultaneously.

• Cross-Cultural Studies
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• v.25
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• pp.649-678
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• 2011

The purpose of this work is to explain categorial correlations between indirect evidentiality and epistemic modality on the basis of semantic, pragmatic usages of Russian so-called non-specialized lexical markers of evidentiality, such as kazhetsja, naverno, vidimo, poxozhe, dolzhno byt' etc. To do this, firstly I concentrated on the parameter of internal functional variation of a given parenthetic word. Secondly, I approached this topic from a typological perspective. Thirdly, I accepted Sweeter(1990)'s methodological assumption that etymological prototype of a given word plays a great role in grammatical, semantic, pragmatic changes. As a result, I could postulate general tendencies of grammaticalizations (or semantic, pragmatic, funtional changes) in the direction from epistemic modality to indirect evidentialty, which consists of inferentives, presumptives, and quotatives. For example, such a parenthetic word as kazhetsja can functions not only as a marker of epistemic modality of uncertainty, but also as inferentives. Besides, it is very interesting that this word lately has started to function as quotatives, too. This kind of functional variations are very characteristic in these spheres.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.602-607
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• 2008

A technique for reliability-based design optimization(RBDO) is developed based on the Bayesian approach, which can deal with the epistemic uncertainty arising due to the limited number of data. Until recently, the conventional RBDO was implemented mostly by assuming the uncertainty as aleatory which means the statistical properties are completely known. In practice, however, this is not the case due to the insufficient data for estimating the statistical information, which makes the existing RBDO methods less useful. In this study, a Bayesian reliability is introduced to take account of the epistemic uncertainty, which is defined as the lower confidence bound of the probability distribution of the original reliability. In this case, the Bayesian reliability requires double loop of the conventional reliability analyses, which can be computationally expensive. Kriging based dimension reduction method(KDRM), which is a new efficient tool for the reliability analysis, is employed to this end. The proposed method is illustrated using a couple of numerical examples.

• Korean Linguistics
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• v.77
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• pp.331-357
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• 2017

The purpose of this paper is to identify the workings of intonation realized in the endings through the spoken language. To achieve this objective, this paper has analyzed 300 minutes of spontaneous speech by women from Seoul and discussed the meanings of modality and their relationship with intonation. Intonation functions significantly in polysemous modal endings in epistemic and act modality. Epistemic modality is usually expressed through indirect and soft intonations such as L:, M: and LH, whereas act modality is expressed through direct and strong intonations such as H, HL and LHL. Intonation appears to be related to the Certainty degree of information, rather than classification of modality, Lengthening relate to indirectness, H with uncertainty, L with statements or affirmation, and HL and LHL relates to assertive attitude. This paper is significant as it has overcome the abstractness of existing modality studies and has engaged in objective and comprehensive analysis with actual spontaneous speech data.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.607-617
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• 2019

Global warming and climate change are increasing the intensity of typhoons and hurricanes and thus increasing the risk effects of typhoon and hurricane hazards on nuclear power plants (NPPs). To reflect these changes, a new NPP should be designed to endure design-basis hurricane wind speeds corresponding to an exceedance frequency of $10^{-7}/yr$. However, the short typhoon and hurricane observation records and uncertainties included in the inputs for an estimation cause significant uncertainty in the estimated wind speeds for return periods of longer than 100,000 years. A logic-tree framework is introduced to handle the epistemic uncertainty when estimating wind speeds. Three key parameters of a typhoon wind field model, i.e., the central pressure difference, pressure profile parameter, and radius to maximum wind, are used for constructing logic tree branches. The wind speeds of the simulated typhoons and the probable maximum wind speeds are estimated using Monte Carlo simulations, and wind hazard curves are derived as a function of the annual exceedance probability or return period. A logic tree decreases the epistemic uncertainty included in the wind intensity models and provides reasonably acceptable wind speeds.

• Journal of the Korean School Mathematics Society
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• v.19 no.3
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• pp.261-275
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• 2016

Many students have dualistic beliefs about mathematics and its learning- for example, there is always just one right answer in mathematics and their role in the classroom is receiving and absorbing knowledge from teacher and textbook. This article investigated some epistemic implications and limitations of common mathematics teaching practices, which often present mathematical facts(or procedures) and treat students' errors in a certain and absolute way. Langer and Piper's (1987) experiment and Oliveira et al.'s (2012) study suggested that presenting knowledge in conditional language which allows uncertainty can foster students' productive epistemological beliefs. Changing the focus and patterns of classroom communication about students' errors could help students to overcome their dualistic beliefs. This discussion will contribute to analyze the implicit epistemic messages conveyed by mathematics instructions and to investigate teaching strategies for stimulating students' epistemic development in mathematics.

• Structural Engineering and Mechanics
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• v.62 no.4
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• pp.507-517
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• 2017

Due to the discontinuous nature of uncertainty quantification in conventional evidence theory(ET), the computational cost of reliability analysis based on ET model is very high. A novel ET model based on fuzzy distribution and the corresponding combination rule to synthesize the judgments of experts are put forward in this paper. The intersection and union of membership functions are defined as belief and plausible membership function respectively, and the Murfhy's average combination rule is adopted to combine the basic probability assignment for focal elements. Then the combined membership functions are transformed to the equivalent probability density function by a normalizing factor. Finally, a reliability analysis procedure for structures with the mixture of epistemic and aleatory uncertainties is presented, in which the equivalent normalization method is adopted to solve the upper and lower bound of reliability. The effectiveness of the procedure is demonstrated by a numerical example and an engineering example. The results also show that the reliability interval calculated by the suggested method is almost identical to that solved by conventional method. Moreover, the results indicate that the computational cost of the suggested procedure is much less than that of conventional method. The suggested ET model provides a new way to flexibly represent epistemic uncertainty, and provides an efficiency method to estimate the reliability of structures with the mixture of epistemic and aleatory uncertainties.