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Situational Awareness for Mission Critical Ship Systems using Probabilistic Knowledge Graph

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0691
Agency Tracking Number: N18A-009-0128
Amount: $125,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T009
Solicitation Number: 2018.0
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-26
Award End Date (Contract End Date): 2019-01-22
Small Business Information
20271 Goldenrod Lane
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Genshe Chen
 (240) 481-5397
Business Contact
 Yingli Wu
Phone: (301) 515-7261
Research Institution
 George Mason University
 Kuo-Chu Chang
4400 University Drive,
Fairfax, VA 22030
United States

 (703) 993-1639
 Nonprofit College or University

This effort proposes to develop situational awareness methodologies for mission critical ship system based on the state-of-the-art probabilistic knowledge graph (KG) and deep learning. The proposed KG approach can incorporate various data fusion technologies for analysis of unstructured data (text, images, etc.) and structured data (signal feeds, database items, etc.) for automated decision support and predictive capabilities. Specifically, the effort proposes to design and develop a general and configurable KG framework that can be integrated and applied to specific operational machinery control and condition monitoring systems (MCS). With deep neural network learning integrated in several key components of the system, such as knowledge fusion, pattern discovery, and prioritized action recommendation, the proposed KG-based cognitive framework permits a rich representation and reasoning of the semantics context in the data. The resulting KG MCS products developed under this effort are capable to enhance state and situational awareness of shipboard machinery control operations for real-time decision support. It is expected that the final products could be incorporated and integrated into the algorithmic base of the standardized MCS baseline.

* Information listed above is at the time of submission. *

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