The autonomous operations of intelligent unmanned aerial and space access vehicles demand fast online trajectory
computations, which rely heavily upon precise and expedited computation of aerodynamic coefficients. Traditional
methods use tabular data and linear interpolations, which are slow and, even worse, cannot produce smooth aerodynamic
functions that are highly demanded for trajectory computation. In this paper, we introduce neural network and PiecewiseSmooth Function based approaches to approximate these coefficients. Although in the past, neural networks have been
applied to aerodynamic coefficient modeling, they have not been considered for the purpose of trajectory design, which
generate large amounts of data during the flight envelope. In this paper, we present an efficient approach to reduce the
overwhelming amount of data requirements so that the training and testing of the proposed solutions are more
manageable and feasible. The preliminary testing results on the six aerodynamic coefficients show that the pitching
moment coefficient Cm and the axial force coefficient Ca are the most challenging to approximate, while the other four
coefficients are easily approximated. In this paper we have focused on improving approximation models for Cm with
promising results. In the future, we will continue our research on developing models for approximating Ca.
The advances in video surveillance technology have lead to the proliferation of surveillance video cameras for the
purposes of viewing areas of interest. Counter terrorism and surveillance applications require video forensics capabilities
like querying and searching video data for events, people or objects of interest. A human analyst may accurately spot a
suspicious activity in a small segment of video. However, due to the large volume of data collected in real-time video
surveillance, it is impractical for human analysts to watch or tag the entire video collected as this can lead to human
errors, lower throughput and inconsistencies in the level of scrutiny. In this paper, we introduce an ontology-based video
retrieval approach, which represents videos with object ontologies and event ontologies, and annotates videos
accordingly. We also describe a user-friendly interface for querying surveillance videos using event dictionaries. Our
approach leverages the capabilities of ontologies in specifying knowledge at different levels, and, in this way, provides
flexibility to a user while forming a query. It is also capable of detecting undefined events such as not previously
conceived abnormal events.
Online aerial vehicle trajectory design and reshaping are crucial for a class of autonomous aerial vehicles such as
reusable launch vehicles in order to achieve flexibility in real-time flying operations. An aerial vehicle is modeled as a
nonlinear multi-input-multi-output (MIMO) system. The inputs include the control parameters and current system states
that include velocity and position coordinates of the vehicle. The outputs are the new system states. An ideal trajectory
control design system generates a series of control commands to achieve a desired trajectory under various disturbances
and vehicle model uncertainties including aerodynamic perturbations caused by geometric damage to the vehicle.
Conventional approaches suffer from the nonlinearity of the MIMO system, and the high-dimensionality of the system
state space. In this paper, we apply a Neural Dynamic Optimization (NDO) based approach to overcome these
difficulties. The core of an NDO model is a multilayer perceptron (MLP) neural network, which generates the control
parameters online. The inputs of the MLP are the time-variant states of the MIMO systems. The outputs of the MLP and
the control parameters will be used by the MIMO to generate new system states. By such a formulation, an NDO model
approximates the time-varying optimal feedback solution.
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