46,093 research outputs found
Pion condensation in a soft-wall AdS/QCD model
Finite isospin chemical potential and temperature have been
introduced in the framework of soft-wall AdS/QCD model. By self-consistently
solve the equation of motion, we obtain the phase boundary of pion condensation
phase, across which the system undergoes a phase transition between pion
condensation phase and normal phase. Comparing the free energy of solutions
with and without pion condensation, we find that the phase transition is of
first order type both at large and small . Qualitatively, the
behavior at large is in agreement with the lattice simulation in
Phys.Rev.D66(2002)034505, while the behavior at small is different from
lattice simulations and previous studies in hard wall AdS/QCD model. This
indicates that a full back-reaction model including the interaction of
gluo-dynamics and chiral dynamics might be necessary to describe the small
pion condensation phase. This study could provide certain clues to
build a more realistic holographic model.Comment: Regular article, 21 page
Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers
open access articleAutonomous robots that operate in the field can enhance their security and efficiency by
accurate terrain classification, which can be realized by means of robot-terrain interaction-generated
vibration signals. In this paper, we explore the vibration-based terrain classification (VTC),
in particular for a wheeled robot with shock absorbers. Because the vibration sensors are
usually mounted on the main body of the robot, the vibration signals are dampened significantly,
which results in the vibration signals collected on different terrains being more difficult to
discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade.
The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of
the existing feature-engineering and feature-learning classification methods; and (2) According to
the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM
(1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened
vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods,
which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project;
meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method
outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method
(LSTM) by 8.23%
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