96,068 research outputs found
Properties of pedestrians walking in line - Fundamental diagrams
We present experimental results obtained for a one-dimensional flow using
high precision motion capture. The full pedestrians' trajectories are obtained.
In this paper, we focus on the fundamental diagram, and on the relation between
the instantaneous velocity and spatial headway (distance to the predecessor).
While the latter was found to be linear in previous experiments, we show that
it is rather a piecewise linear behavior which is found if larger density
ranges are covered. Indeed, our data clearly exhibits three distinct regimes in
the behavior of pedestrians that follow each other. The transitions between
these regimes occur at spatial headways of about 1.1 and 3 m, respectively.
This finding could be useful for future modeling.Comment: 9 figures, 3 table
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Kinetic Solvers with Adaptive Mesh in Phase Space
An Adaptive Mesh in Phase Space (AMPS) methodology has been developed for
solving multi-dimensional kinetic equations by the discrete velocity method. A
Cartesian mesh for both configuration (r) and velocity (v) spaces is produced
using a tree of trees data structure. The mesh in r-space is automatically
generated around embedded boundaries and dynamically adapted to local solution
properties. The mesh in v-space is created on-the-fly for each cell in r-space.
Mappings between neighboring v-space trees implemented for the advection
operator in configuration space. We have developed new algorithms for solving
the full Boltzmann and linear Boltzmann equations with AMPS. Several recent
innovations were used to calculate the discrete Boltzmann collision integral
with dynamically adaptive mesh in velocity space: importance sampling,
multi-point projection method, and the variance reduction method. We have
developed an efficient algorithm for calculating the linear Boltzmann collision
integral for elastic and inelastic collisions in a Lorentz gas. New AMPS
technique has been demonstrated for simulations of hypersonic rarefied gas
flows, ion and electron kinetics in weakly ionized plasma, radiation and light
particle transport through thin films, and electron streaming in
semiconductors. We have shown that AMPS allows minimizing the number of cells
in phase space to reduce computational cost and memory usage for solving
challenging kinetic problems
A Comparison of Two Shallow Water Models with Non-Conforming Adaptive Grids: classical tests
In an effort to study the applicability of adaptive mesh refinement (AMR)
techniques to atmospheric models an interpolation-based spectral element
shallow water model on a cubed-sphere grid is compared to a block-structured
finite volume method in latitude-longitude geometry. Both models utilize a
non-conforming adaptation approach which doubles the resolution at fine-coarse
mesh interfaces. The underlying AMR libraries are quad-tree based and ensure
that neighboring regions can only differ by one refinement level.
The models are compared via selected test cases from a standard test suite
for the shallow water equations. They include the advection of a cosine bell, a
steady-state geostrophic flow, a flow over an idealized mountain and a
Rossby-Haurwitz wave. Both static and dynamics adaptations are evaluated which
reveal the strengths and weaknesses of the AMR techniques. Overall, the AMR
simulations show that both models successfully place static and dynamic
adaptations in local regions without requiring a fine grid in the global
domain. The adaptive grids reliably track features of interests without visible
distortions or noise at mesh interfaces. Simple threshold adaptation criteria
for the geopotential height and the relative vorticity are assessed.Comment: 25 pages, 11 figures, preprin
Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process
This letter presents a physical human-robot interaction scenario in which a
robot guides and performs the role of a teacher within a defined dance training
framework. A combined cognitive and physical feedback of performance is
proposed for assisting the skill learning process. Direct contact cooperation
has been designed through an adaptive impedance-based controller that adjusts
according to the partner's performance in the task. In measuring performance, a
scoring system has been designed using the concept of progressive teaching
(PT). The system adjusts the difficulty based on the user's number of practices
and performance history. Using the proposed method and a baseline constant
controller, comparative experiments have shown that the PT presents better
performance in the initial stage of skill learning. An analysis of the
subjects' perception of comfort, peace of mind, and robot performance have
shown a significant difference at the p < .01 level, favoring the PT algorithm.Comment: Presented at IEEE International Conference on Robotics and Automation
ICRA-201
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
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