We focus on the quantitative and local topological properties of range images. We consider the spaces Mm of m × m high-contrast patches of range images for m=?3, 5, 7, 9, 11. Using computational topological tools to analyze range image patches, we detect that M3 (M9, M11) has core subsets with the topology of a circle, M3, M5, M7, M9 and that M11 have some subspaces with the topology of a Klein bottle. We also discover that the largest subspace with the Klein bottle’s topology decreases as the measurements of patches increase, which generalizes the results in the paper of H. Adams and G. Carlsson, and demonstrates properties among optical images and range image patches, which are more similar than those established by Lee et al.
Chemical vapor deposition growth of amorphous ruthenium-phosphorus films on SiO2 containing ∼ 15% phosphorus is reported. cis-Ruthenium(II)dihydridotetrakis-(trimethylphosphine), cis-RuH2(PMe3)4 (Me = CH3) was used at growth temperatures ranging from 525 to 575 K. Both Ru and P are zero-valent. The films are metastable, becoming increasingly more polycrystalline upon annealing to 775 and 975 K. Surface studies illustrate that demethylation is quite efficient near 560 K. Precursor adsorption at 135 K or 210 K and heating reveal the precursor undergoes a complex decomposition process in which the hydride and trimethylphosphine ligands are lost at temperatures as low at 280 K. Phosphorus and its manner of incorporation appear responsible for the amorphous-like character. Molecular dynamics simulations are presented to suggest the local structure in the films and the causes for phosphorus stabilizing the amorphous phase. 相似文献
Surrogate model-assisted multi-objective genetic algorithms (MOGA) show great potential in solving engineering design problems since they can save computational cost by reducing the calls of expensive simulations. In this paper, a two-stage adaptive multi-fidelity surrogate (MFS) model-assisted MOGA (AMFS-MOGA) is developed to further relieve their computational burden. In the warm-up stage, a preliminary Pareto frontier is obtained relying only on the data from the low-fidelity (LF) model. In the second stage, an initial MFS model is constructed based on the data from both LF and high-fidelity (HF) models at the samples, which are selected from the preliminary Pareto set according to the crowding distance in the objective space. Then the fitness values of individuals are evaluated using the MFS model, which is adaptively updated according to two developed strategies, an individual-based updating strategy and a generation-based updating strategy. The former considers the prediction uncertainty from the MFS model, while the latter takes the discrete degree of the population into consideration. The effectiveness and merits of the proposed AMFS-MOGA approach are illustrated using three benchmark tests and the design optimization of a stiffened cylindrical shell. The comparisons between the proposed AMFS-MOGA approach and some existing approaches considering the quality of the obtained Pareto frontiers and computational efficiency are made. The results show that the proposed AMFS-MOGA method can obtain Pareto frontiers comparable to that obtained by the MOGA with HF model, while significantly reducing the number of evaluations of the expensive HF model.
In an effort to develop highly functionalized flame retardant materials, hybrid nanocoatings are prepared by alternately depositing a positively charged polyaniline (PANi) and negatively charged montmorillonite (MMT) using the layer-by-layer (LbL) assembly technique. Carbon nanotubes (CNTs) are employed in polymer nanocomposites as effective reinforcement, where nanotubes are stabilized in MMT aqueous solution. The 3D structure and high density of CNTs deposited in the PANi/CNTs-MMT multilayers produce thicker and heavier coatings in comparison to the LbL assemblies without CNTs. Vertical and horizontal flame testing show that the incorporation of CNTs improves fire resistance. Additionally, cone calorimetry reveals that stacking two nanomaterials (MMT and CNTs) in a single coating shows a significant reduction in peak heat release rate (up to 51%), total smoke release (up to 47%), and total heat release (up to 37%) for the polyurethane foam. The enhancement of flame retardancy is attributed to a synergistic effect; MMT serves as a physical barrier that retards the diffusion of heat and gas. The addition of CNTs strengthens the thermal stability and high char yield. These results, coupled with the simplicity with which the LbL deposition is applied, present a viable alternative to halogen-free flame retardant nanocoatings to natural and synthetic fibers. 相似文献