1,399 research outputs found

    Air Force Institute of Technology Research Report 2000

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    This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

    Virtual Battlespace Behavior Generation Through Class Imitation

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    Military organizations need realistic training scenarios to ensure mission readiness. Developing the skills required to differentiate combatants from non-combatants is very important for ensuring the international law of armed conflict is upheld. In Simulated Training Environments, one of the open challenges is to correctly simulate the appearance and behavior of combatant and non-combatant agents in a realistic manner. This thesis outlines the construction of a data driven agent that is capable of imitating the behaviors of the Virtual BattleSpace 2 behavior classes while our agent is configured to advance to a geographically specific goal. The approach and the resulting agent promotes and motivates the idea that Opponent and Non-Combatant behaviors inside of simulated environments can be improved through the use of behavioral imitation

    Virtual Reality Games for Motor Rehabilitation

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    This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

    Naval Research Program 2019 Annual Report

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    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.

    Implementing autonomous crowds in a computer generated feature film

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    The implementation of autonomous, flocking crowds of background characters in the feature film ÂRobots is discussed. The techniques for obstacle avoidance and goal seeking are described. An overview of the implementation of the system as part of the production pipeline for the film is also provided

    Retention Prediction and Policy Optimization for United States Air Force Personnel Management

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    Effective personnel management policies in the United States Air Force (USAF) require methods to predict the number of personnel who will remain in the USAF as well as to replenish personnel with different skillsets over time as they depart. To improve retention predictions, we develop and test traditional random forest models and feedforward neural networks as well as partially autoregressive forms of both, outperforming the benchmark on a test dataset by 62.8% and 34.8% for the neural network and the partially autoregressive neural network, respectively. We formulate the workforce replenishment problem as a Markov decision process for active duty enlisted personnel, then extend this formulation to include the Air Force Reserve and Air National Guard. We develop and test an adaptation of the Concave Adaptive Value Estimation (CAVE) algorithm and a parameterized Deep Q-Network on the active duty problem instance with 7050 dimensions, finding that CAVE reduces costs from the benchmark policy by 29.76% and 17.38% for the two cost functions tested. We test CAVE across a range of hyperparameters for the larger intercomponent problem instance with 21,240 dimensions, reducing costs by 23.06% from the benchmark, then develop the Stochastic Use of Perturbations to Enhance Robustness of CAVE (SUPERCAVE) algorithm, reducing costs by another 0.67%. Resulting algorithms and methods are directly applicable to contemporary USAF personnel business practices and enable more accurate, less time-intensive, cogent, and data-informed policy targets for current processes

    Recent progress in biohydrometallurgy and microbial characterisation

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    Since the discovery of microbiological metal dissolution, numerous biohydrometallurgical approaches have been developed to use microbially assisted aqueous extractive metallurgy for the recovery of metals from ores, concentrates, and recycled or residual materials. Biohydrometallurgy has helped to alleviate the challenges related to continually declining ore grades by transforming uneconomic ore resources to reserves. Engineering techniques used for biohydrometallurgy span from above ground reactor, vat, pond, heap and dump leaching to underground in situ leaching. Traditionally biohydrometallurgy has been applied to the bioleaching of base metals and uranium from sulfides and biooxidation of sulfidic refractory gold ores and concentrates before cyanidation. More recently the interest in using bioleaching for oxide ore and waste processing, as well as extracting other commodities such as rare earth elements has been growing. Bioprospecting, adaptation, engineering and storing of microorganisms has increased the availability of suitable biocatalysts for biohydrometallurgical applications. Moreover, the advancement of microbial characterisation methods has increased the understanding of microbial communities and their capabilities in the processes. This paper reviews recent progress in biohydrometallurgy and microbial characterisatio

    Spinoff 2010

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    Topics covered include: Burnishing Techniques Strengthen Hip Implants; Signal Processing Methods Monitor Cranial Pressure; Ultraviolet-Blocking Lenses Protect, Enhance Vision; Hyperspectral Systems Increase Imaging Capabilities; Programs Model the Future of Air Traffic Management; Tail Rotor Airfoils Stabilize Helicopters, Reduce Noise; Personal Aircraft Point to the Future of Transportation; Ducted Fan Designs Lead to Potential New Vehicles; Winglets Save Billions of Dollars in Fuel Costs; Sensor Systems Collect Critical Aerodynamics Data; Coatings Extend Life of Engines and Infrastructure; Radiometers Optimize Local Weather Prediction; Energy-Efficient Systems Eliminate Icing Danger for UAVs; Rocket-Powered Parachutes Rescue Entire Planes; Technologies Advance UAVs for Science, Military; Inflatable Antennas Support Emergency Communication; Smart Sensors Assess Structural Health; Hand-Held Devices Detect Explosives and Chemical Agents; Terahertz Tools Advance Imaging for Security, Industry; LED Systems Target Plant Growth; Aerogels Insulate Against Extreme Temperatures; Image Sensors Enhance Camera Technologies; Lightweight Material Patches Allow for Quick Repairs; Nanomaterials Transform Hairstyling Tools; Do-It-Yourself Additives Recharge Auto Air Conditioning; Systems Analyze Water Quality in Real Time; Compact Radiometers Expand Climate Knowledge; Energy Servers Deliver Clean, Affordable Power; Solutions Remediate Contaminated Groundwater; Bacteria Provide Cleanup of Oil Spills, Wastewater; Reflective Coatings Protect People and Animals; Innovative Techniques Simplify Vibration Analysis; Modeling Tools Predict Flow in Fluid Dynamics; Verification Tools Secure Online Shopping, Banking; Toolsets Maintain Health of Complex Systems; Framework Resources Multiply Computing Power; Tools Automate Spacecraft Testing, Operation; GPS Software Packages Deliver Positioning Solutions; Solid-State Recorders Enhance Scientific Data Collection; Computer Models Simulate Fine Particle Dispersion; Composite Sandwich Technologies Lighten Components; Cameras Reveal Elements in the Short Wave Infrared; Deformable Mirrors Correct Optical Distortions; Stitching Techniques Advance Optics Manufacturing; Compact, Robust Chips Integrate Optical Functions; Fuel Cell Stations Automate Processes, Catalyst Testing; Onboard Systems Record Unique Videos of Space Missions; Space Research Results Purify Semiconductor Materials; and Toolkits Control Motion of Complex Robotics

    A Multi Agent System for Flow-Based Intrusion Detection

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    The detection and elimination of threats to cyber security is essential for system functionality, protection of valuable information, and preventing costly destruction of assets. This thesis presents a Mobile Multi-Agent Flow-Based IDS called MFIREv3 that provides network anomaly detection of intrusions and automated defense. This version of the MFIRE system includes the development and testing of a Multi-Objective Evolutionary Algorithm (MOEA) for feature selection that provides agents with the optimal set of features for classifying the state of the network. Feature selection provides separable data points for the selected attacks: Worm, Distributed Denial of Service, Man-in-the-Middle, Scan, and Trojan. This investigation develops three techniques of self-organization for multiple distributed agents in an intrusion detection system: Reputation, Stochastic, and Maximum Cover. These three movement models are tested for effectiveness in locating good agent vantage points within the network to classify the state of the network. MFIREv3 also introduces the design of defensive measures to limit the effects of network attacks. Defensive measures included in this research are rate-limiting and elimination of infected nodes. The results of this research provide an optimistic outlook for flow-based multi-agent systems for cyber security. The impact of this research illustrates how feature selection in cooperation with movement models for multi agent systems provides excellent attack detection and classification
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