arXiv subjects

Yao Jiang

Publications and source records attributed to Yao Jiang.

24 records · Page 2Linked to original sources

arXiv2022

The mechanism of Li deposition on the Cu substrates in the anode-free Li metal batteries

Due to the rapid growth in the demand for high-energy-density Li batteries and insufficient global Li reserves, the anode-free Li metal batteries are receiving increasing attention. Various strategies, such as surface modification and structural design of Cu current collectors, have been proposed to stabilize the anode-free Li metal batteries. Unfortunately, the mechanism of Li deposition on the Cu surfaces with the different Miller indices is poorly understood, especially on the atomic scale. Here, a large-scale molecular dynamics simulation of Li deposition on the Cu substrates was performed in the anode-free Li metal batteries. The results show that the Li layers on the Cu (100), Cu (110), and Cu (111) surfaces are closer to the structures of Li (110), Li (100), and Li (110) surfaces, respectively. The mechanism was studied through the surface similarity analysis, potential energy surfaces, and lattice features. Finally, a proposal to reduce the fraction of the (110) facet in commercial Cu foils was made to improve the reversibility and stability of Li plating/stripping in the anode-free Li metal batteries.

cond-mat.mtrl-sci↗

arXiv2022

Tac3D: A Novel Vision-based Tactile Sensor for Measuring Forces Distribution and Estimating Friction Coefficient Distribution

The importance of force perception in interacting with the environment was proven years ago. However, it is still a challenge to measure the contact force distribution accurately in real-time. In order to break through this predicament, we propose a new vision-based tactile sensor, the Tac3D sensor, for measuring the three-dimensional contact surface shape and contact force distribution. In this work, virtual binocular vision is first applied to the tactile sensor, which allows the Tac3D sensor to measure the three-dimensional tactile information in a simple and efficient way and has the advantages of simple structure, low computational costs, and inexpensive. Then, we used contact surface shape and force distribution to estimate the friction coefficient distribution in contact region. Further, combined with the global position of the tactile sensor, the 3D model of the object with friction coefficient distribution is reconstructed. These reconstruction experiments not only demonstrate the excellent performance of the Tac3D sensor but also imply the possibility to optimize the action planning in grasping based on the friction coefficient distribution of the object.

cs.RO↗

arXiv2021

Self-healing mechanism of lithium in lithium metal batteries

Li metal is an ideal anode material for use in state-of-the-art secondary batteries. However, Li-dendrite growth is a safety concern and results in low coulombic efficiency, which significantly restricts the commercial application of Li secondary batteries. Unfortunately, the Li deposition (growth) mechanism is poorly understood on the atomic scale. Here, we used machine learning to construct a Li potential model with quantum-mechanical computational accuracy. Molecular dynamics simulations in this study with this model revealed two self-healing mechanisms in a large Li-metal system, viz. surface self-healing and bulk self-healing, and identified three Li-dendrite morphologies under different conditions, viz. "needle", "mushroom", and "hemisphere". Finally, we introduce the concepts of local current density and variance in local current density to supplement the critical current density when evaluating the probability of self-healing.

cond-mat.mtrl-sci↗

arXiv2021

Light Field Salient Object Detection: A Review and Benchmark

Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce preliminary knowledge on light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD are also summarized with detailed information and statistical analyses. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved. Besides, due to the inconsistency of datasets in their current forms, we further generate complete data and supplement focal stacks, depth maps and multi-view images for the inconsistent datasets, making them consistent and unified. Our supplemental data makes a universal benchmark possible. Lastly, because light field SOD is quite a special problem attributed to its diverse data representations and high dependency on acquisition hardware, making it differ greatly from other saliency detection tasks, we provide nine hints into the challenges and future directions, and outline several open issues. We hope our review and benchmarking could help advance research in this field. All the materials including collected models, datasets, benchmarking results, and supplemented light field datasets will be publicly available on our project site https://github.com/kerenfu/LFSOD-Survey.

cs.CV↗

arXiv2021

BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection

Depth information has been proved beneficial in RGB-D salient object detection (SOD). However, depth maps obtained often suffer from low quality and inaccuracy. Most existing RGB-D SOD models have no cross-modal interactions or only have unidirectional interactions from depth to RGB in their encoder stages, which may lead to inaccurate encoder features when facing low quality depth. To address this limitation, we propose to conduct progressive bi-directional interactions as early in the encoder stage, yielding a novel bi-directional transfer-and-selection network named BTS-Net, which adopts a set of bi-directional transfer-and-selection (BTS) modules to purify features during encoding. Based on the resulting robust encoder features, we also design an effective light-weight group decoder to achieve accurate final saliency prediction. Comprehensive experiments on six widely used datasets demonstrate that BTS-Net surpasses 16 latest state-of-the-art approaches in terms of four key metrics.

cs.CV↗

arXiv2020

Numerical Dissipation Based Error Estimators and Grid Adaptation for Large Eddy Simulation

Grid adaptation for implicit Large Eddy Simulation (LES) is a non-trivial challenge due to the inherent coupling of the modeling and numerical errors. An attempt to address the challenge first requires a comprehensive assessment and then the development of error estimators to highlight regions that require refinement. Following the work of Schranner et al., a novel approach to estimate the numerical dissipation of the turbulent kinetic energy (TKE) equations is proposed. The presented approach allows the computation of the local numerical dissipation for arbitrary curvilinear grids through a post-processing procedure. This method, as well as empirical and kinetic-energy-based approaches, are employed to estimate the inherent numerical TKE. We incorporate the numerical TKE to evaluate an effective eddy viscosity, an effective Kolmogorov length scale, and an effective TKE to build a family of Index Quality (IQ) based error estimators. The proposed IQ based estimators are then assessed and utilized to show their effectiveness through an application of grid adaptation for the periodic hill test case and transitional flow over the SD 7003 airfoil. Numerical results are validated through a comparison against reference LES and experimental data. Flow over the adapted grids appear better abled to capture pertinent flow features and integrated functions, such as the lift and drag coefficients.

math.NA↗