275 research outputs found
Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks
This paper considers the distributed sampled-data control problem of a group
of mobile robots connected via distance-induced proximity networks. A dwell
time is assumed in order to avoid chattering in the neighbor relations that may
be caused by abrupt changes of positions when updating information from
neighbors. Distributed sampled-data control laws are designed based on nearest
neighbour rules, which in conjunction with continuous-time dynamics results in
hybrid closed-loop systems. For uniformly and independently initial states, a
sufficient condition is provided to guarantee synchronization for the system
without leaders. In order to steer all robots to move with the desired
orientation and speed, we then introduce a number of leaders into the system,
and quantitatively establish the proportion of leaders needed to track either
constant or time-varying signals. All these conditions depend only on the
neighborhood radius, the maximum initial moving speed and the dwell time,
without assuming a prior properties of the neighbor graphs as are used in most
of the existing literature.Comment: 15 pages, 3 figure
Analytical coupled-wave model for photonic crystal quantum cascade lasers
A coupled-wave model is developed for photonic-crystal quantum cascade
lasers. The analytical model provides an efficient analysis of full
three-dimensional large-area device structure, and the validity is confirmed
via simulations and previous experimental results.Comment: 21 pages and 8 figure
Flame Extinction and Air Vitiation Effects In FDS In Poorly Ventilated Compartment Fires
Compartment fires with different ventilation conditions exhibit different dynamical behaviors, ranging from steady fuel-limited fires to unsteady air-limited fires. Numerical simulations are here performed to study compartment fires in a configuration corresponding to a scaled-down model developed at University of Maryland, in which experimental data are available. The simulations use Fire Dynamics Simulator (FDS) developed by National Institute of Science and Technology (NIST). Four different cases are studied that are representative of different fire conditions: steady over-ventilated fires; steady under-ventilated fires; and unsteady fires with partial flame quenching; unsteady fires leading to total flame quenching. To account for air vitiation and flame extinction effects, a new flame extinction model is developed and integrated into FDS. It is found that the new model improves the numerical predictions and offers the potential of a better representation of the flame dynamics and upper-layer gas composition
Numerical Simulation of Low-Pressure Explosive Combustion in Compartment Fires
A filtered progress variable approach is adopted for large eddy simulations (LES) of turbulent deflagrations. The deflagration model is coupled with a non-premixed combustion model, either an equilibrium-chemistry, mixture-fraction based model, or an eddy dissipation model. The coupling interface uses a LES-resolved flame index formulation and provides partially-premixed combustion (PPC) modeling capability. The PPC sub-model is implemented into the Fire Dynamic Simulator (FDS) developed by the National Institute of Standards and Technology, which is then applied to the study of explosive combustion in confined fuel vapor clouds. Current limitations of the PPC model are identified first in two separate series of simulations: 1) a series of simulation corresponding to laminar flame propagation across homogeneous mixtures in open or closed tunnel-like configurations; and 2) a grid refinement study corresponding to laminar flame propagation across a vertically-stratified layer.
An experimental database previously developed by FM Global Research, featuring controlled ignition followed by explosive combustion in an enclosure filled with vertically-stratified mixtures of propane in air, is used as a test configuration for model validation. Sealed and vented configurations are both considered, with and without obstacles in the chamber. These pressurized combustion cases present a particular challenge to the bulk pressure algorithm in FDS, which has robustness, accuracy and stability issues, in particular in vented configurations. Two modified bulk pressure models are proposed and evaluated by comparison between measured and simulated pressure data in the Factory Mutual Global (FMG) test configuration. The first model is based on a modified bulk pressure algorithm and uses a simplified expression for pressure valid in a vented compartment under quasi-steady conditions. The second model is based on solving an ordinary differential equation for bulk pressure (including a relaxation term proposed to stabilize possible Helmholtz oscillations) and modified vent flow velocity boundary conditions that are made bulk-pressure-sensitive. Comparisons with experiments are encouraging and demonstrate the potential of the new modeling capability for simulations of low pressure explosions in stratified fuel vapor clouds
Steady state behavior of the free recall dynamics of working memory
This paper studies a dynamical system that models the free recall dynamics of
working memory. This model is a modular neural network with n modules, named
hypercolumns, and each module consists of m minicolumns. Under mild conditions
on the connection weights between minicolumns, we investigate the long-term
evolution behavior of the model, namely the existence and stability of
equilibriums and limit cycles. We also give a critical value in which Hopf
bifurcation happens. Finally, we give a sufficient condition under which this
model has a globally asymptotically stable equilibrium with synchronized
minicolumn states in each hypercolumn, which implies that in this case
recalling is impossible. Numerical simulations are provided to illustrate our
theoretical results. A numerical example we give suggests that patterns can be
stored in not only equilibriums and limit cycles, but also strange attractors
(or chaos)
Controllability of networked multiagent systems based on linearized Turing's model
Turing's model has been widely used to explain how simple, uniform structures
can give rise to complex, patterned structures during the development of
organisms. However, it is very hard to establish rigorous theoretical results
for the dynamic evolution behavior of Turing's model since it is described by
nonlinear partial differential equations. We focus on controllability of
Turing's model by linearization and spatial discretization. This linearized
model is a networked system whose agents are second order linear systems and
these agents interact with each other by Laplacian dynamics on a graph. A
control signal can be added to agents of choice. Under mild conditions on the
parameters of the linearized Turing's model, we prove the equivalence between
controllability of the linearized Turing's model and controllability of a
Laplace dynamic system with agents of first order dynamics. When the graph is a
grid graph or a cylinder grid graph, we then give precisely the minimal number
of control nodes and a corresponding control node set such that the Laplace
dynamic systems on these graphs with agents of first order dynamics are
controllable.Comment: 13 pages, 4 figures, submitted to automatic
A Plug-and-Play Image Registration Network
Deformable image registration (DIR) is an active research topic in biomedical
imaging. There is a growing interest in developing DIR methods based on deep
learning (DL). A traditional DL approach to DIR is based on training a
convolutional neural network (CNN) to estimate the registration field between
two input images. While conceptually simple, this approach comes with a
limitation that it exclusively relies on a pre-trained CNN without explicitly
enforcing fidelity between the registered image and the reference. We present
plug-and-play image registration network (PIRATE) as a new DIR method that
addresses this issue by integrating an explicit data-fidelity penalty and a CNN
prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs"
it into an iterative method as a regularizer. We additionally present PIRATE+
that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ).
PIRATE+ interprets the fixed-point iteration of PIRATE as a network with
effectively infinite layers and then trains the resulting network end-to-end,
enabling it to learn more task-specific information and boosting its
performance. Our numerical results on OASIS and CANDI datasets show that our
methods achieve state-of-the-art performance on DIR
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