Dempster -Shafer Theory of Evidence is introduced, and the problem of application DST to online diagnosis for operation condition monitoring and failure detection and recognition is an alyzed. Here we purposed a temporal weighted evidence combination method together with the procedure of application. In this paper we propose a model for relevance feedback. Our model combines evidence from user's relevance assessments with algorithms describing how words are used within documents. We motivate the use of the Dempster-Shafer framework as an appropriate theory for modelling combination of evidence. Expert finding by the Dempster?Shafer theory for evidence combination. View Enhanced PDF Access article on Wiley Online Library it is shown that the Dempster?Shafer combination creates a desired synergy between 2 bodies of knowledge, which improves the precision of the top?ranked The SDL Combination Rule of Evidence Dempster-Shafer theory of evidence [9] and the recent theory of plausible and paradoxical reasoning (DSmT) [10]. A detailed presentation of these rules can be found combination of N ? 2 independent and equi-reliable Dempster-Shafer theory allows one to specify a degree of ignorance in this situation instead of being forced to supply prior probabilities that add to unity. The normalization factor above, 1 ? K, can run into the danger of ignoring the conflicting questions and quantify it as a null set. This combination rule for evidence can, therefore In this paper, it is proposed to apply the Dempster-Shafer Theory (DST) or the theory of evidence to map vegetation, aquatic and mineral surfaces with a view to detecting potential areas of observation of outcrops of geological for-mations (rocks, breastplates, regolith, etc.). The prop osed approach consists in In practice, different sources of evidence may provide information about the same question of interest. Assuming independence of evidence sources, Dempster?Shafer theory introduces a combination rule to combine them to obtain a single belief for the purpose of statistical inference. A Dempster-Shafer Method for Multi-Sensor Fusion I. Introduction In his 1976 book, Glenn Shafer introduced the Theory of Evidence, later referred to as the Dempster-Shafer Theory (DST). This data fusion method is based on the idea of belief and as such can handle ignorance. According to Shafer, this theory was a • Dempster-Shafer method, especially the weighted Dempster-Shafer method, is suitable for sensor fusion tasks in context-sensing architectures with highly dynamic sensor configurations • we expect the weighted Dempster-Shafer evidence combination rule will outperform linear summation and other sensor fusion methods in planned research The focus of Dempster-Shafer theory revolves around this probability mass, which constitutes evidence. In the current work, the evidence is a combination of logical state-ments over which a probability mass can be de?ned. As in standard Dempster-Shafer theory, we use the probability mass to determine how much certain interesting hypothe- Evidence theory, or Dempster-Shafer (D-S) theory, was de­ veloped as an attempt to generalize probability theory by introducing a rule for combining distinct bodies ofevidence [Dempster 1967, Shafer 1976]. As a formal system, D-S theory is distinguished from other uncertainty manage