Armstrong Research, Technology, and Engineering Accomplishments 2014

No abstract availabl

2017 Intern Experience [at] Neil A. Armstrong Flight Research Center

These detailed individual abstracts are being included in the summer 2017 abstract book, demonstrating the knowledge learned during the summer 2017 AFRC STEM program

Detection of exposed steel rebars based on deep-learning techniques and unmanned aerial vehicles

In recent years deep-learning techniques have been developed and applied to inspect cracks in RC structures. The accuracy of these techniques leads to believe that they may also be applied to the identification of other pathologies. This article proposes a technique for automated detection of exposed steel rebars. The tools developed rely on convolutional neural networks (CNNs) based on transfer-learning using AlexNet. Experiments were conducted in large-scale structures to assess the efficiency of the method. To circumvent limitations on the proximity access to structures as large as the ones used in the experiments, as well as increase cost efficiency, the image capture was performed using an unmanned aerial system (UAS). The final goal of the proposed methodology is to generate orthomosaic maps of the pathologies or structure 3D models with superimposed pathologies. The results obtained are promising, confirming the high adaptability of CNN based methodologies for structural inspection.This work was financially supported by: Base Funding - UIDB/04708/2020 and Programmatic Funding - UIDP/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções funded by national funds through the FCT/MCTES (PIDDAC). Additionally, the author Rafael Cabral acknowledges the support provided by the doctoral grant UI/BD/150970/2021 - Portuguese Science Foundation, FCT/MCTES.info:eu-repo/semantics/publishedVersio

Marshall Space Flight Center Research and Technology Report 2017

This report features over 60 technology development and scientific research efforts that collectively aim to enable new capabilities in spaceflight, expand the reach of human exploration, and reveal new knowledge about the universe in which we live. These efforts include a wide array of strategic developments: launch propulsion technologies that facilitate more reliable, routine, and cost effective access to space; in-space propulsion developments that provide new solutions to space transportation requirements; autonomous systems designed to increase our utilization of robotics to accomplish critical missions; life support technologies that target our ability to implement closed-loop environmental resource utilization; science instruments that enable terrestrial, solar, planetary and deep space observations and discovery; and manufacturing technologies that will change the way we fabricate everything from rocket engines to in situ generated fuel and consumables

Modeling & Simulation Education for the Acquisition and T&E Workforce: FY07 Deliverable Package

This report was prepared for CAPT Mike Lilienthal, PhD, CPE, and funded by ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO).This technical report presents the deliverables for calendar year 2007 for the "Educating the Modeling and Simulation Workforce" project performed for the DoD Modeling and Simulation Steering Committee. It includes the results for spirals one and two. Spiral one is an analysis of the educational needs of the program manager, systems engineer, and test and evaluation workforces against a set of educational skill requirements developed by the project team. This is referred to as the 'learning matrix'. Spiral two is a set of module and course matrices, along with delivery options, that meets the educational needs indentified in spiral one. This is referred to as the 'learning architecture'. Supporting materials, such as case studies and a handbook, are included. These documents serve as the design framework for spirals three and four, to be completed in CY2008, and which involve the actual production and testing of the courses in the learning architecture and their longitudinal assessment. This report includes the creative work of a seven university consortium and a group of M&S stake-holders, together comprising over 60 personnel.ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO).This report was prepared for CAPT Mike Lilienthal, PhD, CPE, and funded by ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO)

Integrating Remote Sensing and Artificial Intelligence: A Review of Technological Innovations in Wild Life Crime Detection

Wildlife crime remains one of the biggest global challenges worldwide even up to the current decade. This is even worse when it is compounded by what is referred to as the \u27dark figure\u27 where cases go unreported or are not detected by law enforcement agencies due to victim reluctance or sheer inability of the police to arrest all offenders. To address this issue, scientists have used remote sensing techniques to detect illegality using satellite and other aerial images such as drone and airplane images. Since the amount of remote sensing data is growing very rapidly, and increasing even more in the future, efficient computational systems and sophisticated preprocessing methods are essential for handling and analyzing these data. Artificial Intelligence(AI) has been instrumental in this area through functions like object recognition, data integration, filtering, and anomaly detection that aid the efficiency and accuracy of remote sensing exercises. Overcoming scaling challenges, enhancing engagement, and navigating privacy hurdles remain vital for the implementation of live AI-based models. This review article will also try to give a measure of the likelihood of technological solutions to prevent wildlife crimes and the overriding issue of the \u27dark figure\u27 and the expanding mass of data through a critical analysis of the literature on the respective remote sensing devices and the AI algorithms that may be used to combine them

Brief Analysis on Application Advantages of UAV Surveying and Mapping Technology in Open-Pit Mines

UAV surveying and mapping technology, leveraging its efficiency, precision, and flexibility, demonstrates significant advantages in open-pit mine resource development, hazard prevention, and ecological restoration. This paper systematically reviews the evolution of UAV surveying and mapping technology, elaborates on its application workflow in open-pit mines, and summarizes nine core advantages. Concurrently, it analyzes challenges including environmental constraints, data processing bottlenecks, and regulatory gaps. Future directions are proposed across three dimensions: technological breakthroughs, application expansion, and cross-sector collaboration. By establishing a dynamic equilibrium mechanism for exploration-extraction-reclamation, UAV surveying catalyzes digital and intelligent transformation in open-pit mining, delivering innovative solutions for sustainable mineral industry development

Next generation mine countermeasures for the very shallow water zone in support of amphibious operations

This report describes system engineering efforts exploring next generation mine countermeasure (MCM) systems to satisfy high priority capability gaps in the Very Shallow Water (VSW) zone in support of amphibious operations. A thorough exploration of the problem space was conducted, including stakeholder analysis, MCM threat analysis, and current and future MCM capability research. Solution-neutral requirements and functions were developed for a bounded next generation system. Several alternative architecture solutions were developed that included a critical evaluation that compared performance and cost. The resulting MCM system effectively removes the man from the minefield through employment of autonomous capability, reduces operator burden with sensor data fusion and processing, and provides a real-time communication for command and control (C2) support to reduce or eliminate post mission analysis.http://archive.org/details/nextgenerationmi109456968N