A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique

Amir Shirkhodaie; Vinayak Elangovan; Amjad Alkilani; Mohammad Habibi

doi:10.1117/12.2019828

28 May 2013 A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique

Amir Shirkhodaie, Vinayak Elangovan, Amjad Alkilani, Mohammad Habibi

Author Affiliations +

Proceedings Volume 8758, Next-Generation Analyst; 87580A (2013) https://doi.org/10.1117/12.2019828
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

This paper presents an ongoing effort towards development of an intelligent Decision-Support System (iDSS) for fusion of information from multiple sources consisting of data from hard (physical sensors) and soft (textural sources. Primarily, this paper defines taxonomy of decision support systems for latent semantic data mining from heterogeneous data sources. A Probabilistic Latent Semantic Analysis (PLSA) approach is proposed for latent semantic concepts search from heterogeneous data sources. An architectural model for generating semantic annotation of multi-modality sensors in a modified Transducer Markup Language (TML) is described. A method for TML messages fusion is discussed for alignment and integration of spatiotemporally correlated and associated physical sensory observations. Lastly, the experimental results which exploit fusion of soft/hard sensor sources with support of iDSS are discussed.

Citation Download Citation

Amir Shirkhodaie, Vinayak Elangovan, Amjad Alkilani, and Mohammad Habibi "A decision support system for fusion of hard and soft sensor information based on probabilistic latent semantic analysis technique", Proc. SPIE 8758, Next-Generation Analyst, 87580A (28 May 2013); https://doi.org/10.1117/12.2019828

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