Triangular Bézier sub-surfaces on a triangular Bézier surface

This paper considers the problem of computing the Bézier representation for a triangular sub-patch on a triangular Bézier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular Bézier patch. Based on de Casteljau recursions and shifting operators, previous methods express the control points of the triangular sub-patch as linear combinations of the construction points that are constructed from the control points of the triangular Bézier surface. The construction points contain too many redundancies. This paper derives a simple explicit formula that computes the composite triangular sub-patch in terms of the blossoming points that correspond to distinct construction points and then an efficient algorithm is presented to calculate the control points of the sub-patch

N′-(Diphenyl­methyl­ene)-2-hydroxy­benzo­hydrazide

In the title compound, C20H16N2O2, intra­molecular N—H⋯O and inter­molecular O—H⋯O hydrogen bonds are found. The inter­molecular hydrogen bonds link the mol­ecules into an infinite chain along the c axis. The dihedral angles between the aromatic rings are 16.9 (3), 80.8 (3) and 64.6 (3)

Transport dynamics analysis in ferromagnetic heterojunction using Raman spectroscopy and magnetic force microscopy

AbstractThe ZnO/La0.7Sr0.3MnO3 thin film was epitaxially fabricated on LaAlO3 (100) by pulse laser deposition. The Raman scattering on the single layer LaSrMnO and junction ZnO/La0.7Sr0.3MnO3 was investigated in a giant softening by 490cm−1 John-Teller, 620 and 703cm−1 optical phonon modes. The Raman spectra LaSrMnO and ZnO/La0.7Sr0.3MnO3 were observed with distinct features, i.e., the thickness was in dependent of frequency and intensity. The dynamics results showed that the spin–orbital coupling was caused by anomalies tilt of MnO6 octahedron. The LSMO/ZnO junction exhibited excellent junction positive magneto-resistance behavior in the temperature range of 77–300K. The kinetic energy gain was achieved by orbital competition, strong crystal field and charge order of energy band splitting. The transport orbits were in the environment of the ferromagnetic-orbital ordering. The structures of barriers could be adjusted by junction interface and domain boundary condition in terms of the presence of spin–orbital fluctuating

Thermal fluid dynamics of the effect of filler wire on deposition rate and bead formation intending plasma arc-based DED

The influence of filler wire configuration, such as size and geometry, on the deposition rate (DR) and bead formation, has been studied in wire arc-based directed energy deposition (WADED), but the fundamental physics underlying its effect on wire melting and melt pool dynamics remains unclear. In this paper, a series of plasma arc-based DED (plasma-DED) experiments were conducted to investigate the impact of five different filler wire configurations on DR and bead dimensions. The coupling behaviours of wire melting, metal transfer and melt pool dynamics under the five filler wire configurations were also simulated numerically using the authors' recently developed wire-feeding model. The calculated wire melting and bead cross-sections are consistent with the experimental images and measurements. The results demonstrate that the filler wire significantly affects the highest DR by altering wire melting and metal transfer behaviours through changes in arc energy absorption. The filler wire with a rhombus geometry which is closer to a Gaussian-like arc distribution than the flat wire was shown to get higher DR and more stable metal transfer. Furthermore, different filler wire configurations lead to distinct melt pool behaviours, including temperature distribution and flow velocity, due to various metal transfer behaviours and arc shading effects. This study sheds light on the fundamental physics underlying the impact of filler wire on wire melting and bead formation for the first time. The methods and findings can guide improving DR and controlling bead shape in the plasma-DED process

An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection

Aimed at the vehicle/pedestrian visual sensing task under low-light conditions and the problems of small, dense objects and line-of-sight occlusion, a nighttime vehicle/pedestrian detection method was proposed. First, a vehicle/pedestrian detection algorithm was designed based on You Only Look Once X (YOLOX). The model structure was re-parameterized and lightened, and a coordinate-based attention mechanism was introduced into the backbone network to enhance the feature extraction efficiency of vehicle/pedestrian targets. A feature-scale fusion detection branch was added to the feature pyramid, while a loss function was designed, which combines Complete Intersection Over Union (CIoU) for target localization and Varifocal Loss for confidence prediction to improve the feature extraction ability for small, dense, and low-illumination targets. In addition, in order to further improve the detection accuracy of the algorithm under low-light conditions, a training strategy based on data domain transfer was proposed, which fuses the larger-scale daylight dataset with the smaller-scale nighttime dataset after low-illumination degrading. After low-light enhancement, training and testing were performed accordingly. The experimental results show that, compared with the original YOLOX model, the improved algorithm trained by the proposed data domain transfer strategy achieved better performance, and the mean Average Precision (mAP) increased by 5.9% to 82.4%. This research provided effective technical support for autonomous driving safety at night

Simulation and Field Studies on an Innovative Downhole Machine Designed for Ultrashort-Radius Horizontal Well Drilling Engineering

Ultrashort-Radius Horizontal Well (URHW) drilling engineering plays an important role in increasing the recovery factor of old oilfields. By sidetracking old wellbores at a very high build-up rate, the URHW can effectively exploit the residual oil near old wellbores. Currently, the main problem faced in URHW drilling engineering is the reduced torque received by drill bits owing to the increased friction between the flexible drilling assembly and wellbore as the horizontal section extends, which greatly limits oil production from a single trip. To tackle this problem, we proposed an innovative machine design, a Dynamic Flexible Drill Rod (DFDR), to provide extra torque near the drill bit to extend the horizontal section of the URHW. The interior structure and working principle of the DFDR were illustrated. The mechanical properties of the DFDRs critical load-bearing part were examined via simulation. The torque and pressure loss characteristics were analyzed using computational fluid dynamics. Corresponding modifications were made to optimize the design, with model machines produced accordingly. Field trials were carried out based on old wellbores in Chunliang District, Shengli Oilfield. The DFDR-based technique extended the URHWs horizontal section in this area by 13.38% on average

Growth behavior of iron grains during deep reduction of copper slag

The change in granularity of iron grains in copper slag during coal-based deep reduction was identified using optical microscopy and the Image J analysis software. The growth behavior of iron grains was investigated based on the Hillert dynamic model. The results indicate that the granularity and sphericity of iron grains are strongly affected by the reduction time and temperature during the deep reduction process. It is found that in isothermal condition, the growth rate of iron granularity increases with time exhibiting an S-shape characteristic. Meanwhile, in non-isothermal condition, the growth rate of iron granularity increases exponentially with temperature. When the reduction temperature is in the range of ~1423–1573 K and the reduction time was in the range of ~30–180 min, the grain growth kinetic parameters are calculated as follows: growth index n = 1.424 ± 0.07855, apparent activation energy Q = 116.17 kJ∙mol, and pre-exponential factor as 20,839.38

Analysis of magnetic particle agglomeration structure and interaction forces between magnetic particles

Chain-like and diamond-shaped magnetic particle agglomeration (MPA) commonly forming in a weak magnetic field are simulated based on the finite element method (FEM), and the effects of particle diameter, magnetic field strength, particle relative magnetic permeability, and particle number in magnetic particle chains (MPCs) and diamond-shaped MPA on the strength of MPA are analysed in detail. The results show that magnetic forces on the centre contact points (CCPs) of MPA are positively correlated with the particle diameter, magnetic field strength, particle relative magnetic permeability, and particle number. In addition, the forces on the CCPs of the MPCs (N=2) have a square relationship with the particle diameter and magnetic field strength and have a power relationship of 1.25 with the particle relative magnetic permeability. The forces on each contact point decrease slowly from the centre to both ends in the MPCs and then rapidly decrease to one value (approximately 0.779 times the forces on the CCPs). As for the diamond-shaped MPA, with the increase in the angle α between the magnetic field and axis of diamond-shaped MPA, the force magnitude of the particle entrained parallelly in the diamond-shaped MPA shows a trend of a “cosine curve” shape and the minimum value is 2109 times that of the entrained particle’s gravity. The angle θ between the direction of the force and the negative X-axis shows a trend of a “sine curve” shape. When α = 25º and 155º, the angle θ of the force on the entrained particle reaches an extreme value, that is, θ = 21.87º. Only if the angle θ reaches 30º can the particle entrained parallelly escape from the diamond-shaped MPA. Thus, the diamond-shaped MPA remains in a stable state and it is difficult to disperse MPA by changing the direction of the magnetic field

A Domestic Trash Detection Model Based on Improved YOLOX

Domestic trash detection is an essential technology toward achieving a smart city. Due to the complexity and variability of urban trash scenarios, the existing trash detection algorithms suffer from low detection rates and high false positives, as well as the general problem of slow speed in industrial applications. This paper proposes an i-YOLOX model for domestic trash detection based on deep learning algorithms. First, a large number of real-life trash images are collected into a new trash image dataset. Second, the lightweight operator involution is incorporated into the feature extraction structure of the algorithm, which allows the feature extraction layer to establish long-distance feature relationships and adaptively extract channel features. In addition, the ability of the model to distinguish similar trash features is strengthened by adding the convolutional block attention module (CBAM) to the enhanced feature extraction network. Finally, the design of the involution residual head structure in the detection head reduces the gradient disappearance and accelerates the convergence of the model loss values allowing the model to perform better classification and regression of the acquired feature layers. In this study, YOLOX-S is chosen as the baseline for each enhancement experiment. The experimental results show that compared with the baseline algorithm, the mean average precision (mAP) of i-YOLOX is improved by 1.47%, the number of parameters is reduced by 23.3%, and the FPS is improved by 40.4%. In practical applications, this improved model achieves accurate recognition of trash in natural scenes, which further validates the generalization performance of i-YOLOX and provides a reference for future domestic trash detection research