178,024 research outputs found
Update on the Development of a Flutter Analysis Capability for Unconventional Aircraft Concepts Using HCDstruct
Following years of development, the Higher-fidelity Conceptual Design and structural optimization (HCDstruct) tool is being extended to support dynamic aeroservoelastic analysis and structural optimization for advanced aircraft concepts. These required enhancements include: the development of an aerodynamic matching routine for correcting Nastrans doublet-lattice method aerodynamics; the implementation of control surface structural models; and the implementation of support for Nastrans flutter solution sequence (SOL 145). This paper presents an update on the implementation of generalized control surface structural models and support for Nastran SOL 145
Parameterized Streaming Algorithms for Vertex Cover
As graphs continue to grow in size, we seek ways to effectively process such
data at scale. The model of streaming graph processing, in which a compact
summary is maintained as each edge insertion/deletion is observed, is an
attractive one. However, few results are known for optimization problems over
such dynamic graph streams.
In this paper, we introduce a new approach to handling graph streams, by
instead seeking solutions for the parameterized versions of these problems
where we are given a parameter and the objective is to decide whether there
is a solution bounded by . By combining kernelization techniques with
randomized sketch structures, we obtain the first streaming algorithms for the
parameterized versions of the Vertex Cover problem. We consider the following
three models for a graph stream on nodes:
1. The insertion-only model where the edges can only be added.
2. The dynamic model where edges can be both inserted and deleted.
3. The \emph{promised} dynamic model where we are guaranteed that at each
timestamp there is a solution of size at most .
In each of these three models we are able to design parameterized streaming
algorithms for the Vertex Cover problem. We are also able to show matching
lower bound for the space complexity of our algorithms.
(Due to the arXiv limit of 1920 characters for abstract field, please see the
abstract in the paper for detailed description of our results)Comment: Fixed some typo
How to Model Condensate Banking in a Simulation Model to Get Reliable Forecasts? Case Story of Elgin/Franklin
Imperial Users onl
Computationally Efficient Data-Driven MPC for Agile Quadrotor Flight
This paper develops computationally efficient data-driven model predictive
control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed
flights can experience high levels of aerodynamic effects. Modeling these
turbulent aerodynamic effects is a cumbersome task and the resulting model may
be overly complex and computationally infeasible. Combining Gaussian Process
(GP) regression models with a simple dynamic model of the system has
demonstrated significant improvements in control performance. However, direct
integration of the GP models to the MPC pipeline poses a significant
computational burden to the optimization process. Therefore, we present an
approach to separate the GP models to the MPC pipeline by computing the model
corrections using reference trajectory and the current state measurements prior
to the online MPC optimization. This method has been validated in the Gazebo
simulation environment and has demonstrated of up to reduction in
trajectory tracking error, matching the performance of the direct GP
integration method with improved computational efficiency.Comment: 6 pages, accepted in ACC 2023 (American Control Conference, 2023
Matching Theory for Future Wireless Networks: Fundamentals and Applications
The emergence of novel wireless networking paradigms such as small cell and
cognitive radio networks has forever transformed the way in which wireless
systems are operated. In particular, the need for self-organizing solutions to
manage the scarce spectral resources has become a prevalent theme in many
emerging wireless systems. In this paper, the first comprehensive tutorial on
the use of matching theory, a Nobelprize winning framework, for resource
management in wireless networks is developed. To cater for the unique features
of emerging wireless networks, a novel, wireless-oriented classification of
matching theory is proposed. Then, the key solution concepts and algorithmic
implementations of this framework are exposed. Then, the developed concepts are
applied in three important wireless networking areas in order to demonstrate
the usefulness of this analytical tool. Results show how matching theory can
effectively improve the performance of resource allocation in all three
applications discussed
Velocity estimation via registration-guided least-squares inversion
This paper introduces an iterative scheme for acoustic model inversion where
the notion of proximity of two traces is not the usual least-squares distance,
but instead involves registration as in image processing. Observed data are
matched to predicted waveforms via piecewise-polynomial warpings, obtained by
solving a nonconvex optimization problem in a multiscale fashion from low to
high frequencies. This multiscale process requires defining low-frequency
augmented signals in order to seed the frequency sweep at zero frequency.
Custom adjoint sources are then defined from the warped waveforms. The proposed
velocity updates are obtained as the migration of these adjoint sources, and
cannot be interpreted as the negative gradient of any given objective function.
The new method, referred to as RGLS, is successfully applied to a few scenarios
of model velocity estimation in the transmission setting. We show that the new
method can converge to the correct model in situations where conventional
least-squares inversion suffers from cycle-skipping and converges to a spurious
model.Comment: 20 pages, 13 figures, 1 tabl
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