1,664 research outputs found
Fast and Accurate Computation of Time-Domain Acoustic Scattering Problems with Exact Nonreflecting Boundary Conditions
This paper is concerned with fast and accurate computation of exterior wave
equations truncated via exact circular or spherical nonreflecting boundary
conditions (NRBCs, which are known to be nonlocal in both time and space). We
first derive analytic expressions for the underlying convolution kernels, which
allow for a rapid and accurate evaluation of the convolution with
operations over successive time steps. To handle the onlocality in space,
we introduce the notion of boundary perturbation, which enables us to handle
general bounded scatters by solving a sequence of wave equations in a regular
domain. We propose an efficient spectral-Galerkin solver with Newmark's time
integration for the truncated wave equation in the regular domain. We also
provide ample numerical results to show high-order accuracy of NRBCs and
efficiency of the proposed scheme.Comment: 22 pages with 9 figure
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
Dynamically generated cyclic dominance in spatial prisoner's dilemma games
We have studied the impact of time-dependent learning capacities of players
in the framework of spatial prisoner's dilemma game. In our model, this
capacity of players may decrease or increase in time after strategy adoption
according to a step-like function. We investigated both possibilities
separately and observed significantly different mechanisms that form the
stationary pattern of the system. The time decreasing learning activity helps
cooperator domains to recover the possible intrude of defectors hence supports
cooperation. In the other case the temporary restrained learning activity
generates a cyclic dominance between defector and cooperator strategies, which
helps to maintain the diversity of strategies via propagating waves. The
results are robust and remain valid by changing payoff values, interaction
graphs or functions characterizing time-dependence of learning activity. Our
observations suggest that dynamically generated mechanisms may offer
alternative ways to keep cooperators alive even at very larger temptation to
defect.Comment: 7 pages, 6 figures, accepted for publication in Physical Review
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