Atomically thin mononitrides SiN and GeN: new two-dimensional semiconducting materials

Low-dimensional Si-based semiconductors are unique materials that can both match well with the Si-based electronics and satisfy the demand of miniaturization in modern industry. Owing to the lack of such materials, many researchers put their efforts into this field. In this work, employing a swarm structure search method and density functional theory, we theoretically predict two-dimensional atomically thin mononitrides SiN and GeN, both of which present semiconducting nature. Furthermore study shows that SiN and GeN behave as indirect band gap semiconductors with the gap of 1.75 and 1.20 eV, respectively. The ab initio molecular dynamics calculation tells that both two mononitrides can exist stably even at extremely high temperature of 2000 K. Notably, electron mobilities are evaluated as 0.888x$10^3$ $cm^2V^{-1}s^{-1}$ and 0.413x$10^3$ $cm^2V^{-1}s^{-1}$ for SiN and GeN, respectively. The present work expands the family of low-dimensional Si-based semiconductors.Comment: arXiv admin note: text overlap with arXiv:1703.0389

Microdynamic analysis of ellipsoidal particle flow in a shear cell

This paper studies rheological properties of ellipsoidal particles in a model annular shear cell and compares them with the relevant parameters obtained for spherical particles under similar conditions using the discrete element method (DEM). Some important microdynamic variables such as velocity, coordination number, volume fraction and stress were considered. It was found that there are some differences between the spherical and ellipsoidal particles in terms of these properties. The feature was explained by the microscopic structures at particle scale such as those related to particle alignment and interparticle force

Look globally, age locally: Face aging with an attention mechanism

Face aging is of great importance for cross-age recognition and entertainment-related applications. Recently, conditional generative adversarial networks (cGANs) have achieved impressive results for face aging. Existing cGANs-based methods usually require a pixel-wise loss to keep the identity and background consistent. However, minimizing the pixel-wise loss between the input and synthesized images likely resulting in a ghosted or blurry face. To address this deficiency, this paper introduces an Attention Conditional GANs (AcGANs) approach for face aging, which utilizes attention mechanism to only alert the regions relevant to face aging. In doing so, the synthesized face can well preserve the background information and personal identity without using the pixel-wise loss, and the ghost artifacts and blurriness can be significantly reduced. Based on the benchmarked dataset Morph, both qualitative and quantitative experiment results demonstrate superior performance over existing algorithms in terms of image quality, personal identity, and age accuracy.Comment: arXiv admin note: text overlap with arXiv:1807.09251 by other author

Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

Numerical analysis of effects of specularity coefficient and restitution coefficient on the hydrodynamics of particles in a rotating drum

Various simulations have been conducted to understand the macroscopic behavior of particles in the solid-gas flow in rotating drums in the past. In these studies, the no-slip wall boundary condition and fixed restitution coefficient between particles were usually adopted. The paper presents a numerical study of the gas-solid flow in a rotating drum to understand the effect of the specularity coefficient and restitution coefficient on the hydrodynamic behavior of particles in the segregation process. The volume fraction, granular pressure, granular temperature and their relationships are examined in detail. The boundary conditions of the no-slip and specularity coefficient of 1 are compared. In the simulations, two different sizes of particles with the same density are considered and the Eulerian–Eulerian multiphase model and the kinetic theory of granular flow (KTGF) are used. The results reveal that the hydrodynamical behavior of the particles in the rotating drum is affected by the boundary condition and restitution coefficient. In particular, the increase of specularity coefficient can increase the active region depth, angle repose, granular pressure for both small and large particles and granular temperature for large particles. With increasing restitution coefficient, the angle of repose decreases and granular pressure and temperature increase at the same volume fraction for both small and large particles