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Situational Awareness for Mission Critical Ship Systems

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-18-C-0694
Agency Tracking Number: N18A-009-0294
Amount: $124,934.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N18A-T009
Solicitation Number: 18.A
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-26
Award End Date (Contract End Date): 2019-09-30
Small Business Information
2904 Westcorp Blvd Suite 210
Huntsville, AL 35805
United States
DUNS: 832864370
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael Pukish Michael Pukish
 Research Engineer
 (256) 319-2026
Business Contact
 Billy Todd
Phone: (256) 319-2026
Research Institution
 Florida State University
 Elizabeth Slack Elizabeth Slack
874 Traditions Way MC 4166,
Tallahassee, AL 32306
United States

 (850) 644-8948
 Nonprofit College or University

The US Navy operates a vast fleet of combat and support vessels with complex power control systems under the control and decision authority of human operators. Several current resources such as SPY-1D radar and Vertical Launch System (VLS) and future resources such as railgun, AMDR, and high energy laser (HEL) are energy hungry, exceeding current and planned power generation capability when deployed during dynamic combat operations. As power and energy storage systems grow more complex and the operational requirements during conflicts tax both the ability of the power system to supply resources to disparate systems and the operators to make intelligent sense of the incoming data, machine-intelligence technologies provide an opportunity to ensure operators can make optimal decisions in a timely manner. The IERUS team proposes to develop a robust machine learning based resource management and recommendation system to assist and augment power control system operators. IERUS will employ a proven development strategy enabled by our Machine Learning Toolkit to evaluate numerous ML approaches with little training overhead based on power control system simulation data. The designed algorithm will be tested on realistic reference missions.

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

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