An extended framework for evidential reasoning systems

Liu, W.; Hong, Jin; McTear, Michael F.

doi:10.1109/tai.1990.130429

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…The Dempster Shafer theory (DST) of evidence [2][3][4][5][6][11][12][13][14][15] was introduced from the work of Dempster and extended by Shafer. It is a generalization of probability theory that allows us to better quantify uncertainty.…”

Section: Dempster Shafer Theory Of Evidencementioning

confidence: 99%

“…Among multi-sensor fusion techniques, we can find Bayesian methods [1] and the Theory of Evidences based on the Dempster-Shafer theory (DST) [2][3][4][5][6][11][12][13][14][15], which are commonly used to process and estimate degrees of uncertainty in the fusion process [6]. These theories are based on graphical representations: Bayesian Networks [1] and Evidential Networks (EN) [2][3][4][5][6]15].…”

Section: Introductionmentioning

confidence: 99%

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Evidential Network-Based Multimodal Fusion for Fall Detection

Aguilar

Boudy

Istrate

et al. 2013

International Journal of E-Health and Medical Communications

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The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and Evidence theories such as Dempster-Shafer Theory (DST) are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called Evidential Networks, we propose a structure of heterogeneous multi-sensor fusion for falls detection. The proposed Evidential Network (EN) can handle the uncertainty present in a mobile and a fixed sensor-based remote monitoring systems (fall detection) by fusing them and therefore increasing the fall detection sensitivity compared to the a separated system alone.

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Section: Dempster Shafer Theory Of Evidencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evidential Network-Based Multimodal Fusion for Fall Detection

Aguilar

Boudy

Istrate

et al. 2013

International Journal of E-Health and Medical Communications

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“…Sometimes, the relationship between an element e i of Θ A and a subset B ij of Θ B may be uncertain. Thus, an evidential mapping in [4] assigns probabilities to an elements e i of Θ A instead of a set of subsets to represent such uncertain relationships. A piece of evidence on Θ A is also propagated to Θ B through an evidential mapping when the relationship is uncertain.…”

Section: Evidential Operationsmentioning

confidence: 99%

Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care

Lee

Choi

Elmasri

2009

2009 International Conference on Advanced Information Networking and Applications

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Reliable contextual information of remotely monitored patients should be generated to prevent hazardous situations and to provide pervasive services in home-based care. This is difficult for several reasons. First, low level data obtained from heterogeneous sensors have different degrees of uncertainty. Second, generated contexts can be corrupted or conflicted even if they are acquired by simultaneous operations. In this paper, we utilize Dezert-Smarandache Theory (DSmT) as an evidence fusion approach to reduce ambiguous or imperfect information then to get higher belief levels in the data fusion process of contextual information. To analyze the improvement of DSmT fusion process, we compare DSmT with Dempster-Shafer Theory (DST) using PCR5 rule of combination and Dempster's rule of combination respectively.

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“…Let us now look at several situations when this propagation can take place. Mapping is referred to as a compatibility mapping [LGS86,LH+93]. Equally, a compatibility mapping can be defined from ª ¾ to ª ½ .…”

Section: Propagation Operation In Ds Theorymentioning

confidence: 99%

Merging uncertain information with semantic heterogeneity in XML

Hunter

Liu

2005

Knowl Inf Syst

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Semi-structured information in XML can be merged in a logic-based framework [Hun02,Hun02b]. This framework has been extended to deal with uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e., textentries) in the XML documents [HL04a]. In this paper we further extend this approach to modelling and merging uncertain information that is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled using logic-based fusion techniques.

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An extended framework for evidential reasoning systems

Cited by 9 publications

References 6 publications

Evidential Network-Based Multimodal Fusion for Fall Detection

Evidential Network-Based Multimodal Fusion for Fall Detection

Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care

Merging uncertain information with semantic heterogeneity in XML

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