Probabilistic safety assessment (PSA) which plays a crucial role in risk evaluation is a quantitative approach intended to demonstrate how a nuclear reactor meets the safety margins as part of the licensing process. Despite PSA merits, some shortcomings associated with the final results exist. Conventional PSA uses crisp values to represent the failure probabilities of basic events. This causes...

Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations ar...

In many real-life situations, we cannot directly measure or estimate the desired quantity r. In these situations, we measure or estimate other quantities r 1 ; : : :; r n related to r, and then reconstruct r from the estimates for r i. This reconstruction is called data processing. Often, we only have fuzzy information about r i. In such cases, we have fuzzy data processing. Fuzzy data means th...

Results of the codes simulating transients and abnormal conditions in nuclear power plants are inevitably uncertain. In application to thermal-hydraulic calculations by thermal-hydraulics codes, uncertainty importance analysis can be used to quantitatively confirm the results of qualitative phenomena identification and ranking table (PIRT). Several methodologies have been developed to address u...

Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromi...

The paper focuses on modelling uncertainty typical of the aircraft industry. The design problem involves maximizing a safety measure of an isotropic plate for a given weight. Additionally, the dependence of the weight on the level of uncertainty, for a specified allowable possibility of failure, is also studied. It is assumed that the plate will be built from future materials, with little infor...

Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems. Kernel methods map data into a higher dimensional feature space, where the resulting structure of the classification task is linearly separable; while rough sets granulate the universe with the use of relations and employ the induced knowledge granules to approximate arbitrary conce...

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valu...

A granular based semantics for fuzzy measures is introduced in which the measure of a set of propositions approximates the probability of the disjunction of these propositions. This approximation is derived from known probabilities across a granular partition of the set of possible worlds. This interpretation is then extended to allow for the case where there is uncertainty regarding the meanin...

A complex system usually encompasses agents, as they are known from modern Artificial Intelligence. Within the system dynamics, interactions between agents are affected by perturbations (uncertainty), which often make modeling difficult. This paper introduces a modeling approach based on a function that quantifies uncertainty: the fuzzy measure of ambiguity. A model based on ambiguity minimizat...