147,798 research outputs found
Non-Gaussian statistical models of surface wave fields for remote sensing applications
Based on the complete Stokes wave model with the bias term and using a simple mapping approach and an iteration solution method, we established a formula for the joint probability density function of the surface slope elevation of a nonlinear random wave field. The formula requires three parameters to define the whole density function: the rms surface elevation and slope values and the significant slope. This model represents the dynamics of the wave in a more direct way than the Gram-Charlier approximation. Based on this new statistical model and laboratory experiments, formula and numerical values of EM bias and dynamics bias are derived. The results indicate that various biases should be considered seriously if accuracy of the altimeter measurement is required in centimeter range
Microscale ocean dynamics
The detailed dynamics of the micro-scale ocean surface phenomena are studied along with the relationships among the surface signatures with the underlying dynamical processes. The approach to advance the understanding in this area is as follows: (1) Conduct rigorous theoretical studies of the ocean surface wave dynamics and statistical properties; (2) Conduct process-oriented laboratory experiments to verify the theoretical results, and to provide guidance for further studies; and (3) Prepare testable hypotheses for field verifications and comparisons during the ONR/NASA sponsored Surface Wave Dynamics Experiment (SWADE). An analytic model was established for wave breaking probability to study the influence of wave breaking on the spectrum shape in both deep and finite-depth waters. The spectrum was processed along with the structures of the water surface under the influence of wind and existing waves
Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with
renewable energy source, where the transmission power of a node comes from both
the conventional utility power (AC power) and the renewable energy source. We
assume the transmission power of each node is a function of the local channel
state, local data queue state and local energy queue state only. In turn, we
consider two delay optimization formulations, namely the decentralized
partially observable Markov decision process (DEC-POMDP) and Non-cooperative
partially observable stochastic game (POSG). In DEC-POMDP formulation, we
derive a decentralized online learning algorithm to determine the control
actions and Lagrangian multipliers (LMs) simultaneously, based on the policy
gradient approach. Under some mild technical conditions, the proposed
decentralized policy gradient algorithm converges almost surely to a local
optimal solution. On the other hand, in the non-cooperative POSG formulation,
the transmitter nodes are non-cooperative. We extend the decentralized policy
gradient solution and establish the technical proof for almost-sure convergence
of the learning algorithms. In both cases, the solutions are very robust to
model variations. Finally, the delay performance of the proposed solutions are
compared with conventional baseline schemes for interference networks and it is
illustrated that substantial delay performance gain and energy savings can be
achieved
Delay-Optimal User Scheduling and Inter-Cell Interference Management in Cellular Network via Distributive Stochastic Learning
In this paper, we propose a distributive queueaware intra-cell user
scheduling and inter-cell interference (ICI) management control design for a
delay-optimal celluar downlink system with M base stations (BSs), and K users
in each cell. Each BS has K downlink queues for K users respectively with
heterogeneous arrivals and delay requirements. The ICI management control is
adaptive to joint queue state information (QSI) over a slow time scale, while
the user scheduling control is adaptive to both the joint QSI and the joint
channel state information (CSI) over a faster time scale. We show that the
problem can be modeled as an infinite horizon average cost Partially Observed
Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting the
special structure of the problem, we shall derive an equivalent Bellman
equation to solve the POMDP problem. To address the distributive requirement
and the issue of dimensionality and computation complexity, we derive a
distributive online stochastic learning algorithm, which only requires local
QSI and local CSI at each of the M BSs. We show that the proposed learning
algorithm converges almost surely (with probability 1) and has significant gain
compared with various baselines. The proposed solution only has linear
complexity order O(MK)
Inferring bulk self-assembly properties from simulations of small systems with multiple constituent species and small systems in the grand canonical ensemble
In this paper we generalize a methodology [T. E. Ouldridge, A. A. Louis, and
J. P. K. Doye, J. Phys.: Condens. Matter {\bf 22}, 104102 (2010)] for dealing
with the inference of bulk properties from small simulations of self-assembling
systems of characteristic finite size. In particular, schemes for extrapolating
the results of simulations of a single self-assembling object to the bulk limit
are established in three cases: for assembly involving multiple particle
species, for systems with one species localized in space and for simulations in
the grand canonical ensemble. Furthermore, methodologies are introduced for
evaluating the accuracy of these extrapolations. Example systems demonstrate
that differences in cluster concentrations between simulations of a single
self-assembling structure and bulk studies of the same model under identical
conditions can be large, and that convergence on bulk results as system size is
increased can be slow and non-trivial.Comment: Accepted by J. Chem. Phy
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