The power transformer is one of the most critical and expensive components for the stable operation of the power system. Hence, how to obtain the health condition of transformer is of great importance for power utilities. Multi-attribute decision-making (MADM), due to its ability of solving multi-source information problems, has become a quite effective tool to evaluate the health condition of ...

In Dempster-Shafer theory, belief functions induced by distinct pieces of evidence can be combined by Dempster's rule of combination. The concept of distinctness has not been formally defined. We present a tentative definition of the concept of distinctness and compare this definition with the definition of stochastic independence described in probability theory.

Efficient modeling of uncertain information in real world is still an open issue. Dempster-Shafer evidence theory is one of the most commonly used methods. However, the Dempster-Shafer evidence theory has the assumption that the hypothesis in the framework of discernment is exclusive of each other. This condition can be violated in real applications, especially in linguistic decision making sin...

In this paper a new method for image retrieval using high level color semantic features is proposed. It is based on extraction of low level color characteristics and their conversion into high level semantic features using Johannes Itten theory of color, Dempster-Shafer theory of evidence and fuzzy production rules.

The semantics of similarity measures is studied and reduced to the evidence theory of Dempster and Shafer. Applications are given for classification and configuration, the latter uses utility theory in addition. In: Mathematical and Statistical Methods in Artificial Intelligence (eds. G. della Riccia, R. Kruse, R. Viertl), Springer Verlag 1995, pp. 171-184

We propose a new classifier based on Dempster-Shafer (DS) theory and convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the evidential deep-learning pooling layers first extract high-dimensional features from input data. The are then converted into mass functions aggregated by Dempster’s rule in DS layer. Finally, an expected utility layer ...