Advances and Applications of DSmT for Information Fusion, by editors F. Smarandache & J. Dezert

By editors F. Smarandache & J. Dezert

This quantity has approximately 760 pages, break up into 25 chapters, from forty-one individuals. First a part of this publication offers advances of Dezert-Smarandache idea (DSmT) that's changing into the most finished and versatile fusion concept in response to trust capabilities. it could actually paintings in all fusion areas: energy set, hyper-power set, and super-power set, and has quite a few fusion and conditioning ideas that may be utilized counting on each one software. a few new generalized principles are brought during this quantity with codes for imposing a few of them. For the qualitative fusion, the DSm box and Linear Algebra of subtle Labels (FLARL) is proposed that can convert any numerical fusion rule to a qualitative fusion rule. whilst one must paintings on a cultured body of discernment, the refinement is completed utilizing Smarandache s algebraic codification. New interpretations and implementations of the fusion principles in response to sampling recommendations and referee features are proposed, together with the probabilistic proportional clash redistribution rule. a brand new probabilistic transformation of mass of trust is usually offered which outperforms the classical pignistic transformation in time period of probabilistic details content material. the second one a part of the e-book offers purposes of DSmT in goal monitoring, in satellite tv for pc photograph fusion, in snow-avalanche chance evaluate, in multi-biometric fit ranking fusion, in review of an characteristic info retrieved in keeping with the sensor info or human originated info, in sensor administration, in automated aim allocation for a planetary rover, in computer-aided scientific analysis, in a number of digital camera fusion for monitoring gadgets on floor airplane, in item id, in fusion of digital aid Measures allegiance record, in map regenerating wooded area stands, and so on.

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Extra info for Advances and Applications of DSmT for Information Fusion, Collected Works, Vol. 3

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Therefore, working on power set 2Θ as Glenn Shafer has proposed in his Mathematical Theory of Evidence [25]) is equivalent to work on hyper-power set DΘ with the assumption that all elements of the frame are exclusive. This is what we call Shafer’s model of the frame Θ, written M0 (Θ), even if such model/assumption has not been clearly stated explicitly by Shafer himself in his milestone book. )) The notion of super-power set has been introduced by Smarandache in the Chapter 8 of [36]. It corresponds actually to the theoretical construction of the power set of the minimal3 refined frame Θref of Θ.

1 ..... 1 2 3 2 3 .. ... .... ..... 2 ......... ... 3 ... ... . ... ... ... ... . . ... . . p123 ... p12 . p13 ... .. ... . ... ... ... . . .... ... ... . . ... . .. ... θ1 ∧ θ2 ∧ θ3 .... ... ...... .......... ........ ...... . . . .... ................ ........................................... ... ... ... ... . ... . ... .. . . . ¬θ ∧ θ ∧ ¬θ 1 2 3 .... ¬θ1 ∧ θ2 ∧ θ3 .... ¬θ1 ∧ ¬θ2 ∧ θ3.... ... . .. ... ... ... . . p2 p23 p3 ...

As already stated, in DSmT framework it is also possible to deal directly with complements if necessary depending on the problem under consideration and the information provided by the sources of evidence themselves. The first and simplest way is to work with S Θ on Shafer’s model when a minimal refinement is possible and makes sense. The second way is to deal Chapter 1: An introduction to DSmT 24 with partially known frame and introduce directly the complementary hypotheses into the frame itself.

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