Big Data Meet ML and AI for Decision Superiority at the Tactical Edge – Algorithm Design, Demonstrate and Concept Model

NPS NRP Executive SummaryBig Data Meet ML and AI for Decision Superiority at the Tactical Edge – Algorithm Design, Demonstrate and Concept ModelN2/N6 - Information WarfareThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Naval Research Program 2019 Annual Report

NPS NRP Annual ReportThe Naval Postgraduate School (NPS) Naval Research Program (NRP) is funded by the Chief of Naval Operations and supports research projects for the Navy and Marine Corps. The NPS NRP serves as a launch-point for new initiatives which posture naval forces to meet current and future operational warfighter challenges. NRP research projects are led by individual research teams that conduct research and through which NPS expertise is developed and maintained. The primary mechanism for obtaining NPS NRP support is through participation at NPS Naval Research Working Group (NRWG) meetings that bring together fleet topic sponsors, NPS faculty members, and students to discuss potential research topics and initiatives.Chief of Naval Operations (CNO)This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

ADVANCED TECHNOLOGIES TO ENABLE OPTIMIZED MAINTENANCE PROCESSES IN EXTREME CONDITIONS: MACHINE LEARNING, ADDITIVE MANUFACTURING, AND CLOUD TECHNOLOGY

The way routine maintenance is conducted is not an optimal way to handle maintenance in extreme battlefield conditions. This is a common maintenance problem across various domains, such as repairing battle damage to aircraft or ships without access to a port or depot. The extreme conditions context can also include repairing the Alaska pipeline in the extreme cold, or handling repairs during COVID-19. The researcher examined how modern technology can optimize productivity and reduce the cycle time of the extreme maintenance process. The results of this research found that three emerging technologies, additive manufacturing, cloud in a box, and machine learning (ML), could improve process value, save labor costs, and reduce cycle time. ML had the most significant impact on improving productivity and cycle time. When all technologies were utilized together, productivity and cycle time improvement were more significant and consistent. The research accounted for the riskiness of these technologies, which is necessary to accurately forecast the value added for this extreme maintenance process context. This research is vital because getting correct valued repairs done quickly for the Department of Defense can make the difference between winning and losing a conflict.Distribution Statement A. Approved for public release: Distribution is unlimited.Civilian, Department of the Nav

Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)

The article of record as published may be located at http://dx.doi.org/10.5220/0006086904430449Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 443-449Accurate combat identification (CID) enables warfighters to locate and identify critical airborne objects as friendly, hostile or neutral with high precision. The current CID processes include processing and analysing data from a vast network of sensors, platforms, and decision makers. CID plays an important role in generating the Common Tactical Air Picture (CTAP) which provides situational awareness to air warfare decision-makers. The Big “CID” Data and complexity of the problem pose challenges as well as opportunities. In this paper, we discuss CTAP and CID challenges and some Big Data and Deep Analytics solutions to address these challenges. We present a use case using a unique deep learning method, Lexical Link Analysis (LLA), which is able to associate heterogeneous data sources for object recognition and anomaly detection, both of which are critical for CTAP and CID applications.OPNAV Combat Identification Capability Organization.Naval Postgraduate School Research Progra