Shape transformer nets: Generating viewpoint-invariant 3D shapes from a single image

Single-view 3D shapes generation has achieved great success in recent years. However, current methods always blind the learning of shapes and viewpoints. The generated shape only fit the observed viewpoints and would not be optimal from unknown viewpoints. In this paper, we propose a novel encoder–decoder based network which contains a disentangled transformer to generate the viewpoint-invariant 3D shapes. The differentiable and parametric Non-uniform B-spline (NURBS) surface generation and 3D-to-3D viewpoint transformation are incorporated to learn the viewpoint-invariant shape and the camera viewpoint, respectively. Our new framework allows us to learn the latent geometric parameters of shapes and viewpoints without knowing the ground truth viewpoint. That can simultaneously generate camera-viewpoint and viewpoint-invariant 3D shapes of the object. We analyze the effects of disentanglement and show both quantitative and qualitative results of shapes generated at various unknown viewpoints.     

Charge Transportation and Chirality in Liquid Crystalline Helical Network Phases of Achiral BTBT-Derived Polycatenar Molecules

First examples of multichain (polycatenar) compounds, based on the π-conjugated [1]benzothieno[3,2-b]benzothiophene unit are designed, synthesized, and their soft self-assembly and charge carrier mobility are investigated. These compounds, terminated by the new fan-shaped 2-brominated 3,4,5-trialkoxybenzoate moiety, form bicontinuous cubic liquid crystalline (LC) phases with helical network structure over extremely wide temperature ranges (>200 K), including ambient temperature. Compounds with short chains show an achiral cubic phase with the double network, which upon increasing the chain length, is at first replaced by a tetragonal 3D phase and then by a mirror symmetry is broken triple network cubic phase. In the networks, the capability of bypassing defects provides enhanced charge carrier mobility compared to imperfectly aligned columnar phases, and the charge transportation is non-dispersive, as only rarely observed for LC materials. At the transition to a semicrystalline helical network phase, the conductivity is further enhanced by almost one order of magnitude. In addition, a mirror symmetry broken isotropic liquid phase is formed beside the 3D phases, which upon chain elongation is removed and replaced by a hexagonal columnar LC phase.     

Co/Al金属在聚晶立方氮化硼中的作用及加工适用性

通过正交试验和切削试验相结合的方法,检测聚晶立方氮化硼(PCBN)烧结体的硬度和强度,分析并观察烧结体组元相互熔渗状况以及微观形貌,探索了Co/Al金属对PCBN烧结体的影响以及不同结合剂含量PCBN的加工适应性。试验结果表明:CBN含量和Co含量对PCBN的抗弯强度和硬度影响显著;在刀具材料的选择上,高PCBN浓度的刀具适合加工灰铸铁,低PCBN浓度的刀具适合加工淬硬钢;Co/Al金属能够通过高温高压在PCBN层和硬质合金基体之间进行相互熔渗,导致PCBN层在距硬质合金10~20μm处Co含量达到最大值;PCBN的失效为穿晶断裂方式。     

Nonlinear convection in an elasticoviscous fluid‐saturated anisotropic porous layer using a local thermal nonequilibrium model

By adopting a perturbation method and a local thermal nonequilibrium model, nonlinear thermal convection in an anisotropic porous layer saturated by an elasticoviscous fluid is investigated. An elasticoviscous fluid is modeled by a modified Darcy‐Oldroyd‐B model, and the fluid and solid phase temperatures are represented using a two‐field model for the heat transport equation. Anisotropy in permeability and fluid and solid thermal conductivities are considered. A cubic Landau equation is derived separately to study the stability of bifurcating solution of both stationary and oscillatory convection, and the results of linear instability theory are delineated. The boundary between stationary and oscillatory convection is demarcated by identifying codimension‐two points in the viscoelastic parameters plane. It is found that the subcritical instability is not possible, and the linear instability analysis itself completely captures the behavior of the onset of convection. Heat transfer is obtained in terms of Nusselt number, and the effect of governing parameters on the same is discussed. The results of the Maxwell fluid are obtained as a particular case from the present study.     

Effect of wear behaviour of single mono- and poly-crystalline cBN grains on the grinding performance of Inconel 718

Polycrystalline cubic boron nitride (PcBN) grains were fabricated by combining the monocrystalline cBN (McBN) nanoparticles and inter-abrasive ceramic materials via high temperature and pressure techniques. Grinding performance of Inconel 718 with single McBN and PcBN grains, including grinding force, force ratio, ground surface quality was investigated. Characterization of the wear morphology evolution of worn grains and scratches of PcBN grains were discussed. In addition, the fracture behaviour of PcBN grains was evaluated as the varying of the undeformed chip thickness. Results show that PcBN grains have the smaller grinding force and force ratio, more stable grain wear rate in comparison to McBN grains. Additionally, the better wear-resistance and grinding performance owing to its multi-cutting edges structure in terms of the grain wear morphology evolution were achieved for PcBN grains regardless of the undeformed chip thickness.     

pH-sensitive self-assembling property of poly(ethyleneimine)/cinnamic acid mixture and its effect on pH-dependent release of monoolein cubic phase

Poly(ethyleneimine) (PEI)/cinnamic acid (CA) mixture was self-assembled into microsphere in aqueous phase. As the pH value increased, the self-assembly became hardly formed. As the molar ratio of the amino group of PEI to the carboxyl group of CA increased, the pH window for the formation of self-assembly became broader. The phase transition temperature of cubic phase was 58.5–67.5°C, depending on the PEI/CA content. The release of dye loaded in cubic phase containing PEI/CA increased in a first-order fashion. The release degree was higher at a lower pH value.     

Methanol steam reforming microreactor with novel 3D-Printed porous stainless steel support as catalyst support

In order to study the methanol steam reforming performance of the 3D-printed porous support for hydrogen production, three dimensional (3D) printing technology was proposed to fabricate porous stainless steel supports with body-centered cubic structure (BCCS) and face-centered cubic structure (FCCS). Catalyst loading strength of the 3D-printed porous stainless steel supports was studied. Moreover, methanol steam reforming performance of different 3D-printed porous supports for hydrogen production was experimentally investigated by changing reaction parameters. The results show that the 3D-printed porous stainless steel supports with BCCS and FCCS exhibit better catalyst loading strength, and can be used in the microreactor for methanol steam reforming for hydrogen production. Compared with 90 pores per inch (PPI) Fe-based foam support, 3D-printed porous stainless steel supports with FCCS and BCCS show the similar methanol steam reforming performance for hydrogen production in the condition of 6500 mL/(g·h) gas hourly space velocity (GHSV) with 360 °C reaction temperature. This work provides a new idea for the structural design and fabrication of the porous support for methanol steam reforming microreactor for hydrogen production.     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

The formate electrooxidation on Pt/C and PtSnO2/C nanoparticles in alkaline media: The effect of morphology and SnO2 on the platinum catalytic activity

Pt/C and PtSnO₂/C electrocatalysts with and without cubic preferential morphology were used for formate electrooxidation reaction (FER) in alkaline medium. The synthesis of catalysts was carried out by alcohol reduction method using KBr as a shape directing agent (Bromide Anion Exchange method - BAE). The X-ray diffraction (XRD) showed characteristic peaks of the Pt face-centered cubic (FCC) structure, as well as cassiterite SnO₂. The Transmission Electron Microscopy (TEM) and Scanning Transmission Electron Microscopy (STEM) micrographs show SnO₂ dispersed onto carbon support and adjacent to the Pt nanoparticles (NPs), as well as cubic Pt NPs. The cyclic voltammetry (CV) measurements show that the current density peak for FER on Pt/C (100) is 2.40 times higher than on Pt/C polycrystalline (poly). The current density at end of chronoamperometry (CA) analysis on PtSnO₂/C poly was 1.33 and 5.29 times higher than on Pt/C (100) and Pt/C poly, respectively. The presence of SnO₂ and the (100) facets of platinum cubic morphology might prevent platinum surface deactivation caused by intermediates formed during the FER process.     

Comparison of modeling methods for wind power prediction: a critical study

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.