• Journal of Communications and Networks
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• v.14 no.3
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• pp.225-229
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• 2012

We examine spectrum sensing in a situation of uncertain channel model. In particular, we assume that, besides additive noise, the observed signal contains an interference term whose probability distribution is unknown, and only its range and maximum power are known. We discuss the evaluation of the detector performance and its design in this situation. Although this paper specifically deals with the design of spectrum sensors, its scope is wider, as the applicability of its results extends to a general class of problems that may arise in the design of receivers whenever there is uncertainty about how to model the environment in which one is expected to operate. The theory expounded here allows one to determine the performance of a receiver, by combining the available (objective) probabilistic information with (subjective) information describing the designer's attitude.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.473-476
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• 2003

This paper presents a systematic risk assessment procedure with uncertainty modeling for general construction projects. Since the approach is able to effectively deal with all the related construction risks in terms of the assumed probability with conditional probability concept that systematically incorporate expert's experiences and subjective judgement, the proposed methods with uncertainty modeling is able to apply to all the construction projects inherent in lots of uncertain risk events. The fuzzy set theory is adopted to enhance risk assessment to effectively handle the vague and dynamic phenomenon of an event Therefore, the fuzzy-based risk assessment is very useful, for those countries, such as Korea, where objective probabilistic data for risk assessment is extremely rare, and thus the utilization of subjective judgmental data based on expert's experiences is inevitable.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.61-65
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• 2006

This paper is concerned with a quantitative computational trust model which lakes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust value for entities. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the outcome probability distributions and the entity's preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper presents in detail how the trust model works.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.150-159
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• 2005

This paper addresses a voice personality transformation algorithm which makes one person's voices sound as if another person's voices. In the proposed method, one person's voices are represented by LPC cepstrum, pitch period and speaking rate, the appropriate transformation rules for each Parameter are constructed. The Gaussian Mixture Model (GMM) is used to model one speaker's LPC cepstrums and conditional probability is used to model the relationship between two speaker's LPC cepstrums. To obtain the parameters representing each probabilistic model. a Maximum Likelihood (ML) estimation method is employed. The transformed LPC cepstrums are obtained by using a Minimum Mean Square Error (MMSE) criterion. Pitch period and speaking rate are used as the parameters for prosody transformation, which is implemented by using the ratio of the average values. The proposed method reveals the superior performance to the previous VQ-based method in subjective measures including average cepstrum distance reduction ratio and likelihood increasing ratio. In subjective test. we obtained almost the same correct identification ratio as the previous method and we also confirmed that high qualify transformed speech is obtained, which is due to the smoothly evolving spectral contours over time.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1529-1532
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• 2007

Failure Mode, Effects, and Criticality Analysis (FMECA) is an extension of FMEA which includes a criticality analysis. The criticality analysis is used to chart the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. However, there are several limitations. Measuring severity of failure consequences is subjective and linguistic. Since The result of FMECA only gives qualitative and quantitative informations, it should be re-analysed to prioritize critical units. Fuzzy set theory has been introduced by Lotfi A. Zadeh (1965). It has extended the classical set theory dramatically. Based on fuzzy set theory, fuzzy logic has been developed employing human reasoning process. IF-THEN fuzzy rule based assessment approach can model the expert's decision logic appropriately. Fault tree analysis (FTA) is one of most common fault modeling techniques. It is widely used in many fields practically. In this paper, a simple fault tree analysis is proposed to measure the severity of components. Fuzzy rule based assessment method interprets linguistic variables for determination of critical unit priorities. An rail-way transforming system is analysed to describe the proposed method.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.948-958
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• 2022

Since reliability and security of man-machine system increasingly depend on reliability of human, human reliability analysis (HRA) has attracted a lot of attention in many fields especially in nuclear engineering. Dependence assessment among human tasks is a important part in HRA which contributes to an appropriate evaluation result. Most of methods in HRA are based on experts' opinions which are subjective and uncertain. Also, the dependence influencing factors are usually considered to be constant, which is unrealistic. In this paper, a new model based on Dempster-Shafer evidence theory (DSET) and fuzzy number is proposed to handle the dependence between two tasks in HRA under uncertain and dynamic situations. First, the dependence influencing factors are identified and the judgments on the factors are represented as basic belief assignments (BBAs). Second, the BBAs of the factors that varying with time are reconstructed based on the correction BBA derived from time value. Then, BBAs of all factors are combined to gain the fused BBA. Finally, conditional human error probability (CHEP) is derived based on the fused BBA. The proposed method can deal with uncertainties in the judgments and dynamics of the dependence influencing factors. A case study is illustrated to show the effectiveness and the flexibility of the proposed method.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.737-746
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• 2013

The Bayesian method can be applied successfully to the estimation of the censored regression model introduced by Tobin (1958). The Bayes estimates show improvements over the maximum likelihood estimate; however, the performance of the Bayesian interval estimation is questionable. In Bayesian paradigm, the prior distribution usually reflects personal beliefs about the parameters. Such subjective priors will typically yield interval estimators with poor frequentist properties; however, an objective noninformative often yields a Bayesian procedure with good frequentist properties. We examine the performance of frequentist properties of noninformative priors for the Tobit regression model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3791-3805
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• 2016

The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.653-658
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• 2006

Trust can be defined as the level of subjective probability with which an agent will perform a particular action. This paper proposes a comprehensive fuzzy trust model which incorporates dispositional trust, general trust, and situational trust and reputation information. In the model, the preference degrees for the interaction outcomes with respect to the evaluation criteria are expressed in a fuzzy set, and Sugeno's fuzzy integral is employed to aggregate the satisfaction degrees with respect to the importance of evaluation criteria which can be assigned in a way to preserve the properties of the ${\lambda}-fuzzy$ measure.