AN AUTOMATED MACHINE LEARNING APPROACH FOR MORE EFFICIENT MARINE CORPS RECRUITER PROSPECTING

The military recruiting environment is facing significant challenges, making recruitment goals more difficult to obtain. Due to these difficulties, the Marine Corps must find new ways to target the right demographics effectively. This thesis serves as a proof of concept for recruiting: can we employ automated machine learning to accurately prioritize public high schools using publicly available data? Current methods by Marine Corps Recruiting Command to prioritize high schools are largely unsystematic, potentially leading to inefficient allocation of recruiting resources. This study employs Microsoft Azure to demonstrate how we can use automated machine learning to enhance the efficiency of recruiting efforts. I find that automated machine learning using publicly available data may be an effective tool for predicting which public high schools to prioritize. Additionally, the automated machine learning predictions produced more contracts than the Marine Corps’ choices of priority schools. I recommend that the Marine Corps and other service branches further explore the use of automated machine learning and open-source data to enhance their recruitment strategies. Additionally, the key predictive variables identified by the automated machine learning model align closely with the criteria used by Recruiting Station leaders. However, the model provides a more granular analysis, enabling the identification of subtle patterns and interactions between each variable.Distribution Statement A. Approved for public release: Distribution is unlimited.Captain, United States Marine Corp

AUTONOMOUS UNMANNED SURFACE VESSELS IN NAVAL WARFARE: SYSTEM SAFETY AND ETHICAL IMPLICATIONS IN CONGESTED AND LITTORAL WATERS

The growing desire of deploying unmanned surface vessels (USVs) in naval operations is attributed to their effectiveness demonstrated in capability development trials. However, to remove human-in-the-loop in current USV operations presents significant ethical challenges, including attributing responsibility, complying with international law, operating safely, and distinguishing non-combatants in congested and littoral straits. This thesis adds to the understanding of key enablers for small and medium-sized autonomous USV deployment with a complementary system safety approach. First, we build a notional vignette based on ethical challenges and hazards identified using system theoretic process analysis (STPA). We then measure the utility and complexity of USVs conducting autonomous launch and recovery (LAR), navigation, intelligence, surveillance, and reconnaissance (ISR), and firing. Using our system-level model, our analysis finds humans must retain active control when resorting to firing, exert supervisory control during navigation and ISR, and allow zero human control in LAR operations. While human operators can be the moral agents to augment artificial intelligence (AI)'s lack of emotion and reasoning, the concept of meaningful human control is central to our recommendations. We recommend additional measures such as clarity in roles and responsibilities, along with training and certification, to prevent humans from being the moral crumple zone of AI.Distribution Statement A. Approved for public release: Distribution is unlimited.Military Expert 5, Republic of Singapore NavyMajor, Republic of Singapore Nav

MACHINE LEARNING METHOD TO OPTIMIZE TARGETING OF PHYSICAL NETWORK WITH STOCHASTIC OUTCOME

Physical network infrastructure transporting resources, such as fuel, electricity and water, are critical to the survivability of a nation. These infrastructures could be attractive targets for the adversary. One such attempt could be launching of artilleries to interdict these networks. Such an attack is stochastic with certain probability of kill on the arcs subjected to different errors. A defender would want to ensure that the critical networks are robust against such interdictions. While arcs of the physical network may not appear physically connected, they can often be situated close to each other geographically; a single attack may simultaneously disrupt multiple arcs. Therefore, analyzing only the network's physical connectivity, without considering the geographical dependencies of the arcs, could lead to an overly optimistic assessment of the network's robustness. One way to analyze the robustness of the network would be to assume the role of an attacker with full knowledge of the network infrastructure. The attacker would identify aim points to target the network with the objective of minimizing the expected maximum flow of resources from the source to sink. The objective of the thesis is to investigate the potential of utilizing machine learning methods to discern sets of viable aim points that can effectively fulfill the attacker's goal. The trained model achieved an accuracy of 85%, indicating a promising foundation for further enhancements in future iterations.Distribution Statement A. Approved for public release: Distribution is unlimited.Civilian, DSO National Laboratories, Singapor

CREAM SKIMMING OR BARREL SCRAPING? AN ANALYSIS OF LATERAL CAREER MOVES AMONGST MARINE CORPS OFFICERS

Force Design 2030 and subsequent associated directives have ushered in significant changes across the Marine Corps, both in personnel and equipment. One of the greatest personnel changes is the emphasis on lateral moves, in which Marines shift from one primary military occupational specialty to another. These lateral moves allow the service to align interest and talent to address manpower deficiencies. In this thesis, I analyze performance records of Marine Corps Ground Officers from 1999 to 2022 and employ regression analysis to examine pre- and post-move performance trends of officers who undertake lateral moves. This analysis aims to determine whether these moves are characterized by "cream skimming", with high performers leaving certain fields, or by "barrel scraping", with low performers moving. My research indicates that from 1999 to 2006 there was a modest but statistically significant negative trend in performance of those opting for lateral moves relative to their peers who did not. However, these effects were not observed during more recent time periods. Further regression results indicate a mostly negative, but statistically insignificant coefficient for performance among those who lateral move, indicating no substantial difference between them and their peers who remain in their original field. Furthermore, those that execute a lateral move tend to receive performance evaluations comparable to their peers in their new job field immediately after a lateral move.Distribution Statement A. Approved for public release: Distribution is unlimited.Captain, United States Marine Corp

3D-Printable artificial muscles based on microfluidic microcapacitors

A microcapacitor array for providing artificial muscles is described. The microcapacitor array includes a dielectric body with electrode chambers, positive electrodes in posi­tive electrode chambers, the positive electrodes being con­nected by a first set of channels in the dielectric frame; negative electrodes in negative electrode chambers, the negative electrodes being connected by a second set of channels in the dielectric frame. The first and second set of channels are arranged so that application of a voltage differential between the positive electrodes and the negative electrodes generates an attractive force between each set of adjacent positive and negative electrodes

DEVELOPMENT OF A SMALL, SPACE-BASED TERAHERTZ-TO-INFRARED IMAGING SYSTEM

Direct imaging of terahertz (THz) radiation has useful applications in space-based remote sensing of the upper ionosphere. Researchers at the Naval Postgraduate School have developed a terahertz-to-infrared band-converting metamaterial focal plane array (FPA) using micro-electromechanical systems (MEMS) fabrication techniques. The FPA is designed to absorb selective narrowband THz radiation at 2.06 THz and 4.75 THz, associated with atomic oxygen electron transitions, and convert it to long-wave infrared radiation (LWIR). The emitted LWIR can then be imaged by an uncooled microbolometer infrared camera. This effectively converts the THz emitting scene into an IR image that can be directly interpreted. In order for this technology to be tested in space, an optical system was designed, tested, and built to effectively utilize the band-converting capability of the FPA. The form factor of the resulting optical system, called the Terahertz Imaging Camera (TIC), is compatible with standard 6U CubeSat bus size, weight, and power requirements. The system is divided into two distinct sections, independent of each other due to separation by the FPA. The THz section focuses distant THz radiation onto the FPA via a 90°-fold parabolic mirror, while the IR section focuses the backside of the FPA onto the LWIR camera for imaging via two Ge meniscus lenses. The sensitivity of the system was found to be 1.0 K/µW, which translates to a 50 nW minimum detectable power by the LWIR camera.Distribution Statement A. Approved for public release: Distribution is unlimited.Major, United States Air Forc

CANARY IN THE COAL MINE: ANIMAL BEHAVIOR SOUNDING THE ALARM FOR NATURAL DISASTERS

The persistent loss of life and widespread damage caused by natural disasters highlight the unpredictable and severe nature of these events and the need for better forecasting methodologies. This critical homeland security concern drives researchers to innovate, with pre-disaster anomalous animal behavior emerging as a key area of interest. This thesis investigates the potential of leveraging changes in animal behavior prior to natural disasters to improve the accuracy of disaster forecasting. Through a meta-narrative review, the thesis synthesizes observations of adaptive animal behavior among various avian, terrestrial, and aquatic species before tropical cyclones and earthquakes—the world’s most dangerous natural hazards to life and property. Several species exhibit conspicuous behavioral changes (e.g., variations in breeding patterns and activity levels) in anticipation of these natural disasters. Collectively, these animal-based indicators could enhance the reliability of disaster forecasting, offering potential insights across varying lead times and locations. This thesis advocates for more research that systematically explores adaptive animal behavior, as integrating bioindicators into forecast models could revolutionize forecasting and strengthen disaster preparedness in the United States.Distribution Statement A. Approved for public release: Distribution is unlimited.Outstanding ThesisCivilian, Department of Homeland Securit

MONGOLIA’S SECURITY AND FOREIGN POLICY IN THE ERA OF GREAT POWER COMPETITION

The rise of China and the resurgence of Russia vying with the United States indicates a renewed era of competition among the great powers aiming to set the standards for the global order. This competition, including the covert rivalry between China and Russia within Russia’s spheres of influence, presents significant challenges for Mongolia’s security and foreign policy. This thesis reviews the literature regarding competition among the great powers and small-state strategies to examine the impact of competition on Mongolia’s security and foreign policy. It also covers Finland, Nepal, and Bhutan as cases to explore policy options for Mongolia. The case studies highlight that small states adopt a mix of strategies, including a smart-state, shelter seeking, and hedging according to the geopolitical landscape set by the great powers. The success of these strategies, however, depends on a pragmatic and adaptable political culture, along with the capabilities and readiness to prioritize national interests. This thesis illustrates that the great power competition has significantly influenced Mongolia’s policy decisions, yet it does not necessitate a fundamental change because of the stability of Mongolia’s security environment. This thesis makes recommendations on Mongolia’s policy going forward, emphasizing the importance of bolstering democratic institutions and enhancing resilience against hybrid threats as part of a hedging strategy to manage geopolitical pressures.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant Colonel, Mongolian Armed Force

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

METHODS FOR IMPROVING THE PERFORMANCE OF OUTDOOR VISIBLE LIGHT COMMUNICATION FOR LOW-COST VEHICULAR APPLICATIONS

Visible light communication reuses illumination fixtures to communicate, augmenting wireless communications in congested radio frequency bands. Vehicular applications could increase safety and efficiency in transportation systems but must compete with strong background noise from daylight. This dissertation investigates means to mitigate solar noise and increase the received signal-to-noise ratio for outdoor visible light communication systems using low-cost cameras and vehicle headlights. We establish a statistical model of daylight noise from empirical measurements captured across a full solar day. Various techniques to improve received signal performance are tested, including the use of spectral and polarization filters, optics, and digital processing techniques for region of interest selection and multi-receiver aggregation. We find attenuating filters help combat quantization noise in bright scenes. Polarization filtering provides selective gain by rejecting background light more than the signal. Using optics, data transmissions at 1.6 km were achieved from a car LED headlight to a smartphone camera as a receiver—four times farther than other outdoor visible light communication in the literature. Demonstrated signal-to-noise ratio enhancements allow improved performance in speed and range for all outdoor visible light communication systems being developed, supporting progress toward safer roadways, lower costs, and reduced radio frequency crowding.Distribution Statement A. Approved for public release: Distribution is unlimited.Commander, United States NavyNaval Cyber Warfare Development Group, Suitland, M