NDLI: An Evidential Fusion Network based context reasoning for smart media service

Content Provider	IEEE Xplore Digital Library
Author	Hyojin Park Jinhong Yang Sanghong An Jun Kyun Choi
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. & Commun. Eng., KAIST, Daejeon, South Korea (Hyojin Park; Jinhong Yang; Sanghong An; Jun Kyun Choi)
Abstract	For effective smart media service, a reliable and confidential context recognition is required to prepare and react properly. However, it is difficult to achieve a higher confidence level for several reasons. First, raw data from multiple sensors have different degrees of uncertainty. Second, generated contexts can indicate conflicting results, even though they are acquired by simultaneous operations. In this paper, we demonstrate an Evidential Fusion Network (EFN) based context reasoning for smart media service. For this we conduct the context classification and state-space based context modelling. Then, we perform the static evidential fusion process (SEFP) to obtain a higher confidence level of contextual information. It processes sensor data with an evidential form based on the Dezert-smarandache theory (DSmT). The execution with proposed example scenario demonstrates that the DSmT approach based on PCR5 rule performs better than the DST approach based on Dempster's rule.
Starting Page	986
Ending Page	989
File Size	409289
Page Count	4
File Format	PDF
ISBN	9781467331487
ISSN	17389445
e-ISBN	9788996865018
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-27
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	GIRI
Subject Keyword	Reliability Cybernetics Artificial neural networks smart media service Context reasoning sensor data fusion
Content Type	Text
Resource Type	Article

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