arXiv subjects

Xin Xie

Publications and source records attributed to Xin Xie.

96 records · Page 6Linked to original sources

arXiv2017

A Discrete Packing Model of Granular Material Confined in A Vertical Column

The transmission rules of interparticle forces between granular particles were analyzed, and a discrete packing model was proposed to calculate the static pressure at the bottom of granular material confined in a vertical column. The mechanical analysis and numerical simulation results indicate that the silo effect is caused by the frictional contacts between border particles and inner walls, the static pressure at the bottom depends on the external load initially, and then tends to a saturation pressure (Pn) in an exponential form. The saturation pressure (Pn) is positive linear related to the container radius (R) with the same granular matter and stacking manner. The saturation pressure (Pn) is directly proportional to the particle size (ra), and the increasing or decreasing characteristic depends on the frictional property of inner walls, the friction and stacking angle of grains. At last, the predictions of the aforementioned model are compared with the experimental results from the literature, and good agreement is achieved.

cond-mat.soft↗

arXiv2017

A Review of Smart Materials in Tactile Actuators for Information Delivery

As the largest organ in the human body, the skin provides the important sensory channel for humans to receive external stimulations based on touch. By the information perceived through touch, people can feel and guess the properties of objects, like weight, temperature, textures, and motion, etc. In fact, those properties are nerve stimuli to our brain received by different kinds of receptors in the skin. Mechanical, electrical, and thermal stimuli can stimulate these receptors and cause different information to be conveyed through the nerves. Technologies for actuators to provide mechanical, electrical or thermal stimuli have been developed. These include static or vibrational actuation, electrostatic stimulation, focused ultrasound, and more. Smart materials, such as piezoelectric materials, carbon nanotubes, and shape memory alloys, play important roles in providing actuation for tactile sensation. This paper aims to review the background biological knowledge of human tactile sensing, to give an understanding of how we sense and interact with the world through the sense of touch, as well as the conventional and state-of-the-art technologies of tactile actuators for tactile feedback delivery.

physics.app-ph↗

arXiv2017

Slope Stability Analysis with Geometric Semantic Genetic Programming

Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classification and regression analysis of a sample dataset. Furthermore, a model for slope stability analysis is established on the basis of geometric semantics. According to the results of the study based on GSGP, the method can analyze slope stability objectively and is highly precise in predicting slope stability and safety factors. Hence, the predicted results can be used as a reference for slope safety design.

cs.NE↗

arXiv2017

Smart materials and structures for energy harvesters

Vibrational energy harvesters capture mechanical energy from ambient vibrations and convert the mechanical energy into electrical energy to power wireless electronic systems. Challenges exist in the process of capturing mechanical energy from ambient vibrations. For example, resonant harvesters may be used to improve power output near their resonance, but their narrow bandwidth makes them less suitable for applications with varying vibrational frequencies. Higher operating frequencies can increase harvesters power output, but many vibrational sources are characterized by lower frequencies, such as human motions. This paper provides a thorough review of state of the art energy harvesters based on various energy sources such as solar, thermal, electromagnetic and mechanical energy, as well as smart materials including piezoelectric materials and carbon nanotubes. The paper will then focus on vibrational energy harvesters to review harvesters using typical transduction mechanisms and various techniques to address the challenges in capturing mechanical energy and delivering it to the transducers.

physics.app-ph↗

arXiv2017

Observation of Efimov molecules created from a resonantly interacting Bose gas

We convert a strongly interacting ultracold Bose gas into a mixture of atoms and molecules by sweeping the interactions from resonant to weak. By analyzing the decay dynamics of the molecular gas, we show that in addition to Feshbach dimers it contains Efimov trimers. Typically around 8\% of the total atomic population is bound into trimers, identified by their density-independent lifetime of about 100~$μ$s. The lifetime of the Feshbach dimers shows a density dependence due to inelastic atom-dimer collisions, in agreement with theoretical calculations. We also vary the density of the gas across a factor of 250 and investigate the corresponding atom loss rate at the interaction resonance.

cond-mat.quant-gas↗

arXiv2010

Towards automated high-throughput screening of C. elegans on agar

High-throughput screening (HTS) using model organisms is a promising method to identify a small number of genes or drugs potentially relevant to human biology or disease. In HTS experiments, robots and computers do a significant portion of the experimental work. However, one remaining major bottleneck is the manual analysis of experimental results, which is commonly in the form of microscopy images. This manual inspection is labor intensive, slow and subjective. Here we report our progress towards applying computer vision and machine learning methods to analyze HTS experiments that use Caenorhabditis elegans (C. elegans) worms grown on agar. Our main contribution is a robust segmentation algorithm for separating the worms from the background using brightfield images. We also show that by combining the output of this segmentation algorithm with an algorithm to detect the fluorescent dye, Nile Red, we can reliably distinguish different fluorescence-based phenotypes even though the visual differences are subtle. The accuracy of our method is similar to that of expert human analysts. This new capability is a significant step towards fully automated HTS experiments using C. elegans.

cs.CV↗