Naval Postgraduate School
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Faces of NPS: Capt. Janet Days, USN (Ret.)
No full textFaces of NPS features interviews spotlighting the students, faculty, staff and alumni of our Nation's premier defense education and research institution
Scalable Microfluidic Double-Helix Weave Architecture for 3-D Printable Biomimetic Artificial Muscles
No full textA double-helix weave architecture for an artificial muscle is described. The artificial muscle includes a number of microfluidic channels that are arranged into artificial muscles fibers, where each artificial muscle fiber includes two independent mutually-unconnected microfluidic channels that are entwined in a double helix weave and maintained at opposite electrical polarity
National Security Affairs
No full textIncludes an image of the main page on this date and compressed file containing additional web pages
In Commencement Address, Commander Task Force 66 Celebrates Graduates, Underscores Strategic Advantage of NPS
Get PDFTactical Edge Clouds for C5ISR Battlespace Management in Contested Environments
Get PDFNPS NRP Executive SummaryWe examined infrastructure development for distributed maritime operations (DMO), expeditionary advanced base operations (EABO), littoral operations in a contested environment (LOCE), and combined joint all-domain command and control (CJADC2), focusing on mobile cloud computing with integrated command, control, computers, communications, cyber, intelligence, surveillance, and reconnaissance (C5ISR) for denied-disconnected intermittent and limited (D-DIL) communication and power environments. Artificial intelligence/machine learning (AI/ML) is evaluated for tactical and edge cloud services for decision support, recommendations, prediction, and automation for battlespace management.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N2/N6 - Information Warfar
Evaluating Choices on Cyber Operations and New Weapon Technologies
No full textThis report provides the findings of an examination of how the distribution of offensive and defensive cyber operations (OCO & DCO) contributes to the achievement of strategic goals. Drawing on established theories of the relationship of offensive and defensive weaponry in terrestrial conflict domains, this examination develops a methodological framework to assess the relative contributions of OCO and DCO to offensive and defensive cyber strategies and overall multi-domain outcomes. The report identifies challenges and opportunities in associating offensive and defensive cyber capabilities with appropriate offensive and defensive strategies. Some challenges are intrinsic to the dynamic effects of specific weapons technologies on conflict outcomes, while other challenges flow from the conditions of the cyber domain. The report identifies principal complicating factors in associating OCO and DCO selections with strategic outcomes, including the dual-use and indistinguishable nature of some of the most sophisticated cyber weapons; the opacity of operations incumbent to the cyber domain; complexities and data acquisition impediments in calculating precise relative costs associated with developing and utilizing offensive and defensive cyber capabilities; information paucity exacerbation of motivated analytical biases; and the sometimes inverted relationship of OCO and DCO to offensive and defensive strategies, respectively. These findings support the importance of developing a precise and empirical evaluation methodology associating objectives achievement in the distribution and balance of OCO and DCO missions to the underlying operational and strategic objectives of those missions. Such development will advance FCC/C10F’s evaluation of U.S. Navy choices on the incorporation and utilization of cyber capabilities in naval operations.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Postgraduate School, Naval Research ProgramU.S. Fleet Cyber Command / U.S. TENTH Flee
Alternative Fuels Enabling Unmanned Concept of Operations
Get PDFNPS NRP Executive SummaryThis project applies a systems engineering process to assess objectives and requirements for in-theater fuel generation to enable future unmanned systems concepts of operation (CONOPS). The Navy is developing new CONOPS that rely on teams of manned and unmanned systems to increase warfighting capability. Implementation of those new CONOPS will place new burdens on the platforms and processes that the Navy employs to fuel and sustain its systems. This project developed a discrete-event simulation that modeled three-day persistent intelligence, surveillance, and reconnaissance (ISR) operations from Arleigh Burke class destroyers using four different classes of unmanned aerial vehicles (UAVs), the MQ-35A V-BAT, the RQ-7 Shadow, the RQ-21 Blackjack, and the MQ-27A/B ScanEagle. A commercially available combination of a Cummins HySTAT and a Linde compressor is proposed as a viable combination of systems to support in-theater hydrogen generation. Results indicate that the RQ-7 Shadow is likely to stress the system beyond its capabilities, negatively impacting operational availability and diesel fuel usage. The fuel capacity of individual UAVs does not have a statistically significant impact on results. The burn rate of hydrogen fuel for individual UAVs needs to be reduced by 25% to realize acceptable performance in operational availability and diesel fuel usage. Considered together, this suggests that UAVs may be able to support ISR operations using hydrogen fuel without modification to their current fuel capacities provided technological advancement is made to current expected fuel burn rates.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)ASN(RDA) - Research, Development, and Acquisitio
Use AI/ML/Automation to Improve C4ISR Interoperability Mapping and Testing Capabilities (Continuation)
No full textThe operation of the Marine Corps Air Ground Task Force involves creation of test plans from requirements for each Program of Record, which is currently a manual process. To assist in the process of Test & Evaluation planning by Marine Corps Tactical Systems Support Activity, clustering and vector analysis techniques were employed to compare requirement similarities, consequently automating the prioritization of decision-making procedures. The requirements were organized in a Relational Database Schema and presented through a graphical user interface, ensuring data accessibility, visualization and analytics.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Research Program
Marine Corps System Command
Marine Corps Tactical System Support Activit
Design, Force Regeneration, and Strategic Planning
Get PDFNPS NRP Executive SummaryAs currently envisioned, Fleet Design 2.0 (and beyond) ensures that long-range planning processes will help guide decision-making on fleet architecture. This may dramatically change the shape and content of the Navy’s fleet – which today remains based on legacy platforms and concepts of employment rooted in the experiences of the Cold War and World War II. Fleet Design 2.0 must respond to evolving geopolitics while integrating advances in technologies, and producing at scale within the nation's industrial base. This project analyzes the intersection between long-term strategic planning and the features of the industrial base that may affect force regeneration and sustainment in peace and in war. Inherent characteristics of the industrial base should figure prominently in helping to support decision-making that will determine the shape of fleet architecture, which itself must be tied to a coherent theory of war. This project seeks to unpack and operationalize considerations surrounding the industrial base in the Fleet Design 2.0 planning process. The report emphasizes an historical perspective as illustrative in developing ways of breaking down these issues for the U.S. Navy to sort through as it prepares to more coherently link its fleet architecture with war plans and regeneration.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N7 - Warfighting Developmen
Building Supervised ML Models with Geospatial & Time Series Analyses to Predict Adversary’s Action
No full textNPS NRP Executive SummaryIntelligence analysists have access to vast amounts of data; however, which data is useful, for what purpose, and analyzing the data in a timely manner to provide input for command decisions is challenging to balance and manage. Currently, the intelligence community leverages heuristics and historic information to inform analysis of data and provide intelligence recommendations to commanders. This research was requested directly by U.S. Pacific Fleet (PACFLEET) to provide a reliable predictive model for the intelligence community to leverage data more fully in a quantitative and analytical manner and provide probabilities with confidence. Without this research, the community would continue to use non-automated systems. Therefore, we propose machine learning models to automated systems to assist decision making based on datasets. To develop predictive models, we propose to build machine learning models based on the classified data provided by the Commander, U.S. Pacific Fleet (COMPACFLT) staff, collected from 2016 to 2021 related to a specific maritime event. Using such information, first, we can apply classification models, such as random forests model, logistic regression model, and support vector machine model, to see which factors strongly correlate to each event. Second, we can conduct time series analyses to see any patterns of events in terms of time horizon such as seasonal patterns. Third, we conduct survival analyses, such as Cox regression models and random forest survival analysis models to predict how long it will take between events. Finally, combining all such supervised models, we can build ensemble models to predict an event to happen associated with its probability from observed factors, and we can predict its location and time. Based upon the success of the models developed in this research, we recommend expanding the methodology to predict the occurrence of infrequent events of interest, such as the deployment of units, ships, or other events of interest. Events with clear indications and warnings, such as logistics preparatory actions, would be ideal to expand this methodology to.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N2/N6 - Information Warfar