In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a propositio...

This paper presents the computational stochastic homogenization of a heterogeneous 3D-linear anisotropic elastic microstructure that cannot be described in terms constituents at microscale, as live tissues. The random apparent elasticity field mesoscale is then modeled class non-Gaussian positive-definite tensor-valued homogeneous fields. We present an extension previous works consisting novel ...

Today we have seen so much digital uncertain data produced. Handling of this uncertain data is very difficult. Commonly, the distance between these uncertain object descriptions are expressed by one numerical distance value. Clustering on uncertain data is one of the essential and challenging tasks in mining uncertain data. The previous methods extend partitioning clustering methods like k-mean...

Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The previous methods extend traditional partitioning clustering methods like k-means and density-based clustering methods like DBSCAN to uncertain data, thus rely on geometric distanc...

this article proposes composition of fuzzy sets theory with discrete-event simulation and puts it into application in order to model uncertain activity duration in simulating a real-world system. the purpose of this paper is to assess the usefulness of fuzzy simulation for modeling uncertain activity duration, especially when insufficient or no sample data is available. in this research, fuzzy ...

Through a Dynamical Systems approach to robotics, we introduce a measure of distinguishability between different behaviours of a robot, in an uncertain context, thanks to interval analysis.

In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the fre...

We discuss the use of the fuzzy measure for representing information about uncertain variables. We describe the relationship between the fuzzy measure and the Dempster-Shafer belief structure. A method for generating the fuzzy measures associated with a D-S belief structure is described. We discuss the use of the Shapley index as a means for providing an entropy for fuzzy measures. We introduce...

Validation is the assessment of the match between a model’s predictions and any empirical observations relevant to those predictions. This comparison is straightforward when the data and predictions are deterministic, but is complicated when either or both are expressed in terms of uncertain numbers (i.e., intervals, probability distributions, p-boxes, or more general imprecise probability stru...

In the 21st century, as technologies of perceptual recognition develops, devices of information generation begin to accurately sense, measure and monitor the physical world in real time.Complex Event Processing(CEP), which can be used to extract user level information from raw data, becomes the key part of the IoT middleware. Most of the current study of complex event processing has not focus o...