An Intelligent Human-Tracking Robot Based-on Kinect Sensor

This thesis provides an indoor human-tracking robot, which is also able to control other electrical devices for the user. The overall experimental setup consists of a skid-steered mobile robot, Kinect sensor, laptop, wide-angle camera and two lamps. The Kinect sensor is mounted on the mobile robot to collect position and skeleton data of the user in real time and sends it to the laptop. The laptop processes these data and then sends commands to the robot and the lamps. The wide-angle camera is mounted on the ceiling to verify the tracking performance of the Kinect sensor. A C++ program runs the camera, and a java program is used to process the data from the C++ program and the Kinect sensor and then sends the commands to the robot and the lamps. The human-tracking capability is realized by two decoupled feedback controllers for linear and rotational motions. Experimental results show that although there are small delays (0.5 s for linear motion and 1.5 s for rotational motion) and steady-state errors (0.1 m for linear motion and 1.5° for rotational motion), tests show that they are acceptable since the delays and errors do not cause the tracking distance or angle out of the desirable range (±0.05m and ± 10° of the reference input) and the tracking algorithm is robust. There are four gestures designed for the user to control the robot, two switch-mode gestures, lamp crate gesture, and lamp-selection and color change gesture. Success rates of gestures recognition are more than 90% within the detectable range of the Kinect sensor

Computational Study on the Microscopic Adsorption Characteristics of Linear Alkylbenzene Sulfonates with Different Chain Lengths on Anthracite Surface

In order to explore the influence of different lengths of hydrophobic carbon chains on the diffusion characteristics of surfactants on the surface of anthracite, six linear alkyl benzene sulfonates with different hydrophobic carbon chain lengths were selected (mC, m = 8, 10, 12, 14, 16, 18; m represents the numbers of carbon atoms in the hydrophobic carbon chain), and molecular dynamics (MD) simulations were adopted. Models of surfactant-anthracite, surfactant-graphite layer, and water-surfactant-anthracite were constructed. After analyzing a series of properties such as adsorption energy, diffusion coefficient, radial distribution function (RDF), and hydrophobic tail order parameters, it was found that 12C had the highest adsorption strength on the surface of anthracite; the reason was that 12C had the highest degree of aggregation near the oxygen-containing functional groups on the surface of anthracite. Further studies had found that the hydrophobic tail chain of 12C had the strongest isotropy. The study fills the gap in the systematic study of the diffusion characteristics of linear alkylbenzene sulfonates (LAS) with different chain lengths on the surface of anthracite, enriches and develops the basic theory of coal wettability, and also provides technical ideas for the design of new surfactants and new dust suppression agents

Parametric study on the water impacting of a free-falling symmetric wedge based on the extended von Karman's momentum theory

The present study is concerned with the peak acceleration azmax occurring during the water impact of a symmetric wedge. This aspect can be important for design considerations of safe marine vehicles. The water-entry problem is firstly studied numerically using the finite-volume discretization of the incompressible Navier-Stokes equations and the volume-of-fluid method to capture the air-water interface. The choice of the mesh size and time-step is validated by comparison with experimental data of a free fall water-entry of a wedge. The key original contribution of the article concerns the derivation of a relationship for azmax (as well as the correlated parameters when azmax occurs), the initial velocity, the deadrise angle and the mass of the wedge based on the transformation of von Karman momentum theory which is extended with the inclusion of the pile-up effect. The pile-up coefficient, which has been proven dependent on the deadrise angle in the case of water-entry with a constant velocity, is then investigated for the free fall motion and the dependence law derived from Dobrovol'skaya is still valid for varying deadrise angle. Reasonable good theoretical estimates of the kinematic parameters are provided for a relatively wide range of initial velocity, deadrise angle and mass using the extended von Karman momentum theory which is the combination of the original von Karman method and Dobrovol'skaya's solution and this theoretical approach can be extended to predict the kinematic parameters during the whole impacting phase.Comment: arXiv admin note: text overlap with arXiv:2207.1041

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202

Structural Color 3D Printing By Shrinking Photonic Crystals

The rings, spots and stripes found on some butterflies, Pachyrhynchus weevils, and many chameleons are notable examples of natural organisms employing photonic crystals to produce colorful patterns. Despite advances in nanotechnology, we still lack the ability to print arbitrary colors and shapes in all three dimensions at this microscopic length scale. Commercial nanoscale 3D printers based on two-photon polymerization are incapable of patterning photonic crystal structures with the requisite ~300 nm lattice constant to achieve photonic stopbands/ bandgaps in the visible spectrum and generate colors. Here, we introduce a means to produce 3D-printed photonic crystals with a 5x reduction in lattice constants (periodicity as small as 280 nm), achieving sub-100-nm features with a full range of colors. The reliability of this process enables us to engineer the bandstructures of woodpile photonic crystals that match experiments, showing that observed colors can be attributed to either slow light modes or stopbands. With these lattice structures as 3D color volumetric elements (voxels), we printed 3D microscopic scale objects, including the first multi-color microscopic model of the Eiffel Tower measuring only 39-microns tall with a color pixel size of 1.45 microns. The technology to print 3D structures in color at the microscopic scale promises the direct patterning and integration of spectrally selective devices, such as photonic crystal-based color filters, onto free-form optical elements and curved surfaces

A Data-driven dE/dx Simulation with Normalizing Flow

In high-energy physics, precise measurements rely on highly reliable detector simulations. Traditionally, these simulations involve incorporating experiment data to model detector responses and fine-tuning them. However, due to the complexity of the experiment data, tuning the simulation can be challenging. One crucial aspect for charged particle identification is the measurement of energy deposition per unit length (referred to as dE/dx). This paper proposes a data-driven dE/dx simulation method using the Normalizing Flow technique, which can learn the dE/dx distribution directly from experiment data. By employing this method, not only can the need for manual tuning of the dE/dx simulation be eliminated, but also high-precision simulation can be achieved

Experimental investigation and thermodynamic calculation of Ni–Al–La ternary system in nickel-rich region: A new intermetallic compound Ni2AlLa

The phase equilibrium of the Ni–Al–La ternary system in a nickel-rich region was observed at 800 °C and 1000 °C using scanning electron microscopy backscattered electron imaging, energy dispersive X-ray spectrometry and X-ray diffractometry. The solubility of Al in the Ni5La phase was remeasured at 800 °C and 1000 °C. Herein, we report a new ternary phase, termed Ni2AlLa, confirmed at 800 °C. Its X-ray diffraction (XRD) pattern was indexed and space group determined using Total Pattern Solution (TOPAS), and the suitable lattice parameters were fitted using the Pawley method and selected-area electron diffraction. Ni2AlLa crystallizes in the trigonal system with a space group R3 (no. 146), a = 4.1985 Å and c = 13.6626 Å. A self-consistent set of thermodynamic parameters for the Al–La and Ni–La binary systems and the Ni–Al–La ternary system includes a Ni2AlLa ternary phase, which was optimized using the CALPHAD method. The calculated thermodynamic and phase-equilibria data for the binary and ternary systems are consistent with the literature and measured data