Efficient Designs (SEED) Center website (http://harvest.nps.edu).

In 2003, the US Army began using the Integrated Mechanical Diagnostics Health and Usage Management System (IMD-HUMS), an integrated airborne and ground-based system developed by Goodrich Corporation, to support maintenance of the UH-60L. IMD-HUMS is responsible for collecting, processing, analyzing, and storing an enormous amount of vibratory and flight regiime data obtained from sensors located throughout the aircraft. The purpose of this research is to predict failures of the UH-60L's electrical generators, applying Airtificial Neural Networks (ANN) on the IMD-HUMS-produced data. Artificial NNs are data based vice rule based, thereby possessing the potential capability to operate where analytical solutions are inadequate. They are reputed to be robust and highly tolerant of noisy data. Software tools such as Clementine 10.0, S-Plus 7.0, and Excel are used to establish these predictions. This research has verified that ANNs have a position in machinery condiiton monitoring and diagnostics. However, the limited nature of these results indicates that ANNs will not solve all machinery condition monitoring and diagnostics problems by themselves. They certainly will not completely replace conventional rule-based expert systems. Ultimately, it is anticipated that a symbiotic combination of these two technologies will provide the optimal solution to the machinery condition monitoring and diagnostics problem.

A small, low-power Object-Oriented Programmable integrated circuit (OOPic) microcontroller was integrated and tested with the architecture for an autonomous ground vehicle (AGV). Sensors with the OOPic, and the XBee Wireless Suite were included in the integration. Tests were conducted, including range and time operation analysis for wireless communications for comparison with the legacy BL2000 microcontroller. Results demonstrated long battery life for the electronics of the robot, as well as communication ranges exceeding high power modems. The OOPic was limited by processing power and an ability to interpret some incoming form data. Consequently its use as a one for one replacement for the BL2000 is limited. However combined use with the BL2000 shows promise as a replacement for sensor monitoring and a hardware substitute for the legacy Pulse Width Modulator.

Approved for public release; distribution is unlimited / The concept of employing ground swarm robotics to accomplish tasks has been proposed for future use in humanitarian de-mining, plume monitoring, searching for survivors in a disaster site, and other hazardous activities. More importantly in the military context, with the development of advanced explosive detectors, swarm robotics with autonomous search and detection capability could potentially address the improvised explosive device (IED) problem faced by foot patrols, and aid in the search for hidden ammunition caches and weapons of mass destruction (WMDs). The intent of this research is to leverage on agent based simulation to model a ground robotic swarm on a search and detection mission in a semi-urban environment rigged with stationary IEDs. Efficient design of experiment (DOE) techniques and data farming are engaged to help identify controllable factors and capabilities that have the most impact on overall effectiveness. The focus of this thesis is to explore agent based simulation applied to swarm robotics; the technological and algorithmic aspects are not delved on. Results from the simulations provide several insights on the impact of both decision and noise factors on the performance of the swarm. Incorporation of virtual pheromones as a shared memory map is modeled as an additional capability that is found to enhance the robustness and reliability of the swarm. / Outstanding Thesis

Approved for public release; distribution is unlimited / Usage Monitoring requires accurate regime recognition. For each regime, there is a usage assigned for each component. For example, the damage accumulated at a component is higher if the aircraft is undergoing a high G maneuver than in level flight. The objective of this research is to establish regime recognition models using classification algorithms. The data used in the analysis are the parametric data collected by the onboard system and the actual data, consisting of the correct regime collected from the flight cards. This study uses Rpart (with a tree output) and C5.0 (with a ruleset output) to establish two different models. Before model fitting, the data was divided into smaller datasets that represent regime families by subsetting using important flight parameters. Nonnormal tolerance intervals are constructed on the uninteresting values; then these values in the interval are set to zero to be muted (e.g. excluded). These processes help reduce the effect of noise on classification. The final models had correct classification rates over 95%. The number of bad misclassifications were minimized (e.g. the number of bad misclassifications of a level flight regime as a hover regime was minimized), but the models were not as powerful in classifying the low-speed regimes as in classifying high-speed regimes. / Outstanding Thesis

RA requested that these models be examined in an effort to combine the functionality of each. This thesis builds a model that does just that. The fundamental concept of the model involves taking the current inventory of Marines (by military occupational specialty [MOS] and grade) and applying transition rates to each of them in order to determine how many are in what state at the end of the upcoming year. The necessary number of reenlistments is then calculated by subtracting the forecasted inventory from a desired force structure known as the Grade Adjusted Recapitulation. Manpower planners can use the results of this model to establish the number of boat spaces for each of the first-term MOSs as well as recommended reenlistment goals for the subsequent-term MOSs.

Approved for public release; distribution is unlimited / The U.S. Army Training and Doctrine Command (TRADOC) Analysis Center (TRAC) and the Modeling, Virtual Environments, and Simulations Institute (MOVES) at the Naval Postgraduate School, Monterey, California developed the Assignment Scheduling Capability for UAVs (ASC-U) simulation to assist in the analysis of unmanned aerial vehicle (UAV) requirements for the current and future force. ASC-U employs a discrete event simulation coupled with the optimization of a linear objective function. At regular intervals, ASC-U obtains an optimal solution to a simplified problem that assigns available UAVs to missions that are available or will be available within a future time horizon. This thesis simultaneously explores the effects of 26 simulation and UAV factors on the mission value derived when allocating UAVs to mission areas. The analysis assists in defining the near term (2008) UAV force structure and the investment strategy for the mid term (2013), and far term (2018). We combine an efficient experimental design, exploratory modeling, and data analysis to examine 514 variations of a scenario involving five UAV classes and over 21,000 mission areas. The conclusions suggest the following: the optimization interval significantly influences the quality of the solution, percent mission coverage may depend on a few UAV performance factors, small time horizons increase percent mission coverage, and carefully planned designs assist in the exploration of the outer and interior regions of the response surface. / Outstanding Thesis

<p> Disasters strike suddenly and cause destruction which disrupts the availability of basic survival supplies for people living in affected areas. The efficiency of humanitarian organizations in providing relief has a direct and crucial impact on the survival, health, and recovery of affected people and their communities. To better prepare to respond to disasters, many relief organizations use supply pre-positioning. However, the real and potential needs of different locations change over time and when an organization uses traditional warehouse pre-positioning, relief operations are limited by set inventory locations that are difficult to alter. For this reason, a well known organization recently considered including a large supply holding ship in its operations. By holding inventory on a ship, the organization would be able to dynamically relocate its inventory over time in response to changing relief supply demand forecasts. </p><p> To our knowledge, the research contained herein is the first to examine dynamic inventory relocation for responding to disasters over time. Specifically, we examine how to optimally relocate and manage inventory for a single mobile inventory to serve stochastic demand at a number of potential disaster sites over time. While we keep in mind the motivating example of a supply holding ship in the disaster relief setting throughout this dissertation, the model and most of the results are applicable to any type of mobile inventory, facility, or server in any setting. </p><p> We first examine the dynamic relocation problem. We model the problem using dynamic programming and develop analytical and numerical results regarding optimal relocation policies, the optimal path and speed of relocation decisions, and the value of inventory mobility over traditional warehouse pre-positioning. To help overcome the computational complexity of the problem, we develop a heuristic which solves relatively large problem instances in our numerical experiments within 0.5% of optimality in less than 0.1% of the time required by an exact algorithm. </p><p> As it is suboptimal to consider relocation decisions and inventory management decisions separately, we also examine the joint dynamic relocation and inventory management problem. To our knowledge, we are the first to examine the dynamic relocation and inventory management problem with stochastic demand. Similarly to the dynamic relocation problem, we model this problem using dynamic programming. We develop a number of analytical results characterizing the optimal relocation and inventory management policies. </p><p> As the first to examine these problems, we hope this research serves as a catalyst for other research in this area; accordingly, we conclude this dissertation by discussing a number of areas for future research.</p>

<p>I explore and analyze a problem of finding the socially optimal capital requirements for financial institutions considering two distinct channels of contagion: direct exposures among the institutions, as represented by a network and fire sales externalities, which reflect the negative price impact of massive liquidation of assets.These two channels amplify shocks from individual financial institutions to the financial system as a whole and thus increase the risk of joint defaults amongst the interconnected financial institutions; this is often referred to as systemic risk. In the model, there is a trade-off between reducing systemic risk and raising the capital requirements of the financial institutions. The policymaker considers this trade-off and determines the optimal capital requirements for individual financial institutions. I provide a method for finding and analyzing the optimal capital requirements that can be applied to arbitrary network structures and arbitrary distributions of investment returns.</p><p>In particular, I first consider a network model consisting only of direct exposures and show that the optimal capital requirements can be found by solving a stochastic linear programming problem. I then extend the analysis to financial networks with default costs and show the optimal capital requirements can be found by solving a stochastic mixed integer programming problem. The computational complexity of this problem poses a challenge, and I develop an iterative algorithm that can be efficiently executed. I show that the iterative algorithm leads to solutions that are nearly optimal by comparing it with lower bounds based on a dual approach. I also show that the iterative algorithm converges to the optimal solution.</p><p>Finally, I incorporate fire sales externalities into the model. In particular, I am able to extend the analysis of systemic risk and the optimal capital requirements with a single illiquid asset to a model with multiple illiquid assets. The model with multiple illiquid assets incorporates liquidation rules used by the banks. I provide an optimization formulation whose solution provides the equilibrium payments for a given liquidation rule.</p><p>I further show that the socially optimal capital problem using the ``socially optimal liquidation" and prioritized liquidation rules can be formulated as a convex and convex mixed integer problem, respectively. Finally, I illustrate the results of the methodology on numerical examples and</p><p>discuss some implications for capital regulation policy and stress testing.</p> / Dissertation

<p> A mixed-integer linear program is proposed to model the dynamic network expansion problem of improving oil spill response capabilities to support energy exploration in the Arctic. Oil spill response operations in this region can be hampered by a lack of existing infrastructure, limited pre-positioned response equipment, and the possibility that response equipment might not arrive in time to mitigate the impact of a spill because of distance and infrastructure limitations. These considerations are modeled by two inter-related constraint sets with the objective of minimized total weighted response time for a set of potential oil spill incidents. One constraint set determines how to dynamically allocate response equipment and improve the infrastructures necessary to stockpile them within a network of response sites. The other set determines how to utilize this stockpile to respond to each task necessary for an incident by scheduling the equipment to complete tasks. These task completion times are subject to deadlines which, if not met, can, instead, require costlier follow-on tasks to be scheduled. The model, its assumptions, and data requirements were assessed by subject matter experts in the United States (U.S.) Coast Guard and a major Oil Spill Response Organization in the context of oil spill response logistics to support energy exploration initiatives in the U.S. Arctic.</p>