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Hao-Ning Wang

Publications and source records attributed to Hao-Ning Wang.

2 recordsLinked to original sources

Thermodynamic and hydrodynamic characteristics of interacting system formed in relativistic heavy ion collisions

To study the energy-dependent characteristics of thermodynamic and hydrodynamic parameters, based on the framework of a multi-source thermal model, we analyze the soft transverse momentum ($p_{T}$) spectra of the charged particles ($π^{-}$, $π^{+}$, $K^{-}$, $K^{+}$, $\bar{p}$, and $p$) produced in gold-gold (Au-Au) collisions at the center-of-mass energies $\sqrt{s_{NN}}=7.7$, 11.5, 14.5, 19.6, 27, 39, 62.4, and 200 GeV from the STAR Collaboration and in lead-lead (Pb-Pb) collisions at $\sqrt{s_{NN}}=2.76$ and 5.02 TeV from the ALICE Collaboration. In the rest framework of emission source, the probability density function obeyed by meson momenta satisfies the Bose-Einstein distribution, and that obeyed by baryon momenta satisfies the Fermi-Dirac distribution. To simulate the $p_{T}$ of the charged particles, the kinetic freeze-out temperature $T$ and transverse expansion velocity $β_{T}$ of emission source are introduced into the relativistic ideal gas model. Our results, based on the Monte Carlo method for numerical calculation, show a good agreement with the experimental data. The excitation functions of thermodynamic parameter $T$ and hydrodynamic parameter $β_{T}$ are then obtained from the analyses, which shows an increase tendency from 7.7 GeV to 5.02 TeV in collisions with different centralities.

hep-ph↗

AFR: An Efficient Buffering Algorithm for Cloud Robotic Systems

Communication between robots and the server is a major problem for cloud robotic systems. In this paper, we address the problem caused by data loss during such communications, and propose an efficient buffering algorithm, called AFR, to solve the problem. We model the problem into an optimization problem to maximize the received Quantity of Information (QoI). Our AFR algorithm is formally proved to achieve near-optimal QoI, which has a lower bound that is a constant multiple of the unrealizable optimal QoI. We implement our AFR algorithm in ROS without changing the interface or API for the applications. Our experiments on two cloud robot applications show that our AFR algorithm can efficiently and effectively reduce the impact of data loss. For the remote mapping application, the RMSE caused by data loss can be reduced by about 20%. For the remote tracking application, the probability of tracking failure caused by data loss can be reduced from about 40%-60% to under 10%. Meanwhile, our AFR algorithm introduces time overhead of under 10 microseconds.

cs.RO↗