boeing has developed a concept of operations that leverages advanced avionics and available or technologically mature ATM automation tools. Relying on voice communications, the concept is feasible to implement in the ...
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ISBN:
(纸本)1563479060
boeing has developed a concept of operations that leverages advanced avionics and available or technologically mature ATM automation tools. Relying on voice communications, the concept is feasible to implement in the 2008-2012 time frame. A model of arrival operations under the concept has also been developed to study its performance. The model allows for a quantification of the effects of lateral navigation performance, wind variability, trajectory prediction performance, arrival management strategies, and airspace design on key traffic metrics such as delivery accuracy, inter-arrival spacing and delay via Monte-Carlo simulation methodology. The model is described in details and illustrative examples of its capabilities are provided.
boeing Commercial Airplanes (BCA) has evaluated the airport capacity and NAS-wide delay benefits of an operational concept for Air Traffic Management (ATM) for implementation in the 2008 - 2012 timeframe. The concept ...
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ISBN:
(纸本)1563478250
boeing Commercial Airplanes (BCA) has evaluated the airport capacity and NAS-wide delay benefits of an operational concept for Air Traffic Management (ATM) for implementation in the 2008 - 2012 timeframe. The concept enables increased airport and airspace capacity through the integration of Flight Management System (FMS) Required Navigation Performance (RNP) capabilities, ground-based Air Traffic Management (ATM) automation tools, 3D path-based operations, Local Area Augmentation System (LAAS), and advanced runway concepts for closely-spaced parallel, converging, and crossing runways. Benefit applications for these near-term capabilities are proposed and the increase in airport capacity for the 35 U. S. National Airspace System (NAS) benchmark airports is evaluated using the boeing Airport Capacity Constraints Model. The increased airport capacities are then used by the National Flow Model (NFM) to evaluate the NAS-wide delay benefits. Results for annualized average arrival delay show that a 20 - 25% increase in airport capacity associated with the implementation of the near-term operational concept produces a nearly 50% reduction in delay by 2020. The capacity increases enabled by the implementation of this near-term operational concept in 2012 are adequate to serve the anticipated traffic growth. However, by 2015, additional system improvements will be needed to maintain delay performance at or below today's levels.
Many decision tools and complex decision systems require components that use learning technology to improve the quality of the decisions, based on observations (such as sensor data). In order to employ these tools and...
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ISBN:
(纸本)0780390482
Many decision tools and complex decision systems require components that use learning technology to improve the quality of the decisions, based on observations (such as sensor data). In order to employ these tools and systems in high- or medium-risk applications the design, implementation, and deployment process needs to follow principled verification, validation, and testing procedures that assure a reliable operation. This task is far from being trivial because of the very nature of learning - a technology that provides tools for making decisions under uncertainty. Only little research efforts have been dedicated so far to validating and testing learning-based systems. This paper describes a novel tool for the testing and the validation of learning systems and a set of statistical tests that are employed by this tool for the assessment of learned classification decisions. We will also describe some aspects of the underlying theoretical and experimental framework for the validation and testing of systems that learn.
Many decision tools and complex decision systems require components that use learning technology to improve the quality of the decisions, based on observations (such as sensor data). In order to employ these tools and...
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Many decision tools and complex decision systems require components that use learning technology to improve the quality of the decisions, based on observations (such as sensor data). In order to employ these tools and systems in high- or medium-risk applications, the design, implementation, and deployment process needs to follow principled verification, validation, and testing procedures that assure a reliable operation. This task is far from being trivial because of the very nature of learning - a technology that provides tools for making decisions under uncertainty. Only little research efforts have been dedicated so far to validating and testing learning-based systems. This paper describes a novel tool for the testing and the validation of learning systems and a set of statistical tests that are employed by this tool for the assessment of learned classification decisions. We also describe some aspects of the underlying theoretical and experimental framework for the validation and testing of systems that learn.
This paper describes the application of the transcription method to compute an optimal low thrust transfer from a geostationary transfer orbit around the earth to a specified lunar mission orbit. It is representative ...
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This paper describes the application of the transcription method to compute an optimal low thrust transfer from a geostationary transfer orbit around the earth to a specified lunar mission orbit. It is representative of the SMART-1 mission that will be conducted by ESA early in 2003. The spacecraft is equipped with electric propulsion powered by solar array. So the available thrust is no more than 0.073 Newton, while the initial mass of the satellite is 350 kg. This is a challenging class of optimal control problems since significant orbit manipulations require very long duration trajectories. The problem is also demanding because realistic forces due to earth oblateness and third-body perturbation often dominate the thrust. This approach combines a sparse nonlinear programming algorithm with a discretization of the trajectory and automated mesh refinement. The problem will be presented in detail and the optimization performance along with the optimized trajectory will be discussed.
Neural networks are employed to predict the amount and location of propulsion system rotor unbalance. Vibration data used to train and test inverse system models are generated via a high-order structural dynamic finit...
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Neural networks are employed to predict the amount and location of propulsion system rotor unbalance. Vibration data used to train and test inverse system models are generated via a high-order structural dynamic finite element model. Several neural network methods, including feed forward neural network using back propagation, node-decoupled Kalman filter (NDEKF) and support vector machines (SVMs) are investigated. Training results and performance among the various methods are compared. Original applications to nonlinear structural models and damaged structure models are shown.
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