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61.
62.
Motivated by the continuing interest in the tree data model, we study the expressive power of downward navigational query languages on trees and chains. Basic navigational queries are built from the identity relation and edge relations using composition and union. We study the effects on relative expressiveness when we add transitive closure, projections, coprojections, intersection, and difference; this for Boolean queries and path queries on labeled and unlabeled structures. In all cases, we present the complete Hasse diagram. In particular, we establish, for each query language fragment that we study on trees, whether it is closed under difference and intersection. 相似文献
63.
Zhijiang Li Yingping Zheng Liqin Cao Lei Jiao Yanfei Zhong Caiyi Zhang 《Color research and application》2020,45(4):656-670
Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results. 相似文献
64.
针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题,提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色(RTCP)算法灰度化图像,然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点,最后采用局部强度顺序模式建立特征点描述符,根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上,所提算法与尺度不变特征变换(SIFT)算法、加速鲁棒特征(SURF)算法、"风"(KAZE)算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明:随着图像亮度差异的增加,SIFT算法、SURF算法、"风"(KAZE)算法和ORB算法匹配正确率下降迅速,所提算法下降缓慢并且正确率均高于80%;所提算法特征点检测较慢和描述符维数较高,平均耗时为23.47 s,匹配速度不及另外四种算法,但匹配质量却远超过它们。对实时性要求不高的系统中,所提算法可以克服光照变化对图像匹配造成的影响。 相似文献
65.
《Ceramics International》2020,46(13):20751-20768
“Weibull statistics” for strength distribution analysis refers to either the ordinary Weibull distribution function or the Weibull statistical fracture theory. The ordinary Weibull distribution function is an empirical distribution function on an equal footing with other type of classical empirical distributions such as normal and log-normal distributions for fitting the statistical data of various random variables nonexclusive to materials strength. It has no explicit physical meaning and cannot be used for size scaling and prediction of strength. The Weibull statistical fracture theory is a weakest-link statistical fracture model for a solid with the strength distribution of an elemental volume being described by the ordinary Weibull distribution function. It has the capability of size scaling and prediction of strength for specimens with different geometries and different loading modes. The three-parameter Weibull statistical fracture theory in uniaxial flexure of prismatic beams is reformulated and validated by both numerical and real strength experiments of different ceramics. 相似文献
66.
Md Arifuzzaman Uneb Gazder Muhammad Saiful Islam 《Journal of Adhesion Science and Technology》2020,34(10):1100-1114
AbstractThe expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene–Butadiene modified asphalt is more consistent compared to the Styrene–Butadiene–Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt. 相似文献
67.
Xianda XIE Shuting WANG Ming YE Zhaohui XIA Wei ZHAO Ning JIANG Manman XU 《Frontiers of Mechanical Engineering》2020,15(1):100
We present an energy penalization method for isogeometric topology optimization using moving morphable components (ITO–MMC), propose an ITO–MMC with an additional bilateral or periodic symmetric constraint for symmetric structures, and then extend the proposed energy penalization method to an ITO–MMC with a symmetric constraint. The energy penalization method can solve the problems of numerical instability and convergence for the ITO–MMC and the ITO–MMC subjected to the structural symmetric constraint with asymmetric loads. Topology optimization problems of asymmetric, bilateral symmetric, and periodic symmetric structures are discussed to validate the effectiveness of the proposed energy penalization approach. Compared with the conventional ITO–MMC, the energy penalization method for the ITO–MMC can improve the convergence rate from 18.6% to 44.5% for the optimization of the asymmetric structure. For the ITO–MMC under a bilateral symmetric constraint, the proposed method can reduce the objective value by 5.6% and obtain a final optimized topology that has a clear boundary with decreased iterations. For the ITO–MMC under a periodic symmetric constraint, the proposed energy penalization method can dramatically reduce the number of iterations and obtain a speedup of more than 2. 相似文献
68.
《International Journal of Hydrogen Energy》2020,45(37):18602-18611
The hydrogen due to its high mass energy density is a new renewable, economically viable and clean resource. The most eco-friendly and economical approaches for the generation of hydrogen through hydrogen evolution is electrochemical water splitting. The two-dimensional (2D) nanomaterials have been recently found as potential candidates as non-noble metal catalyst for hydrogen evolution. In this work, we have systematically studied the structural and electronic properties of the newly predicted hexagonal-aluminium carbide monolayer (h-AlC ML) under the framework of dispersion-corrected density functional theory (DFT) calculations. The calculated electronic total density of states (TDOS) of h-AlC ML predict its metallic nature in contrast to other polar honeycomb 2D materials which are either semiconducting or semimetallic. The metallic behavior of h-AlC monolayer which motivates us to investigate its HER activity results due to the presence of delocalized charge density near Fermi level. Thus, we have investigated the HER activity of h-AlC ML by calculating hydrogen (H) adsorption energy (ΔEH) and Gibbs free energy (ΔGH) at three different sites of the 3 × 3 and 4 × 4 supercells of h-AlC ML; top of carbon atom (EH-C), top of aluminium atom (EH-Al) and hollow site (EH-Hollow). Our results show that the hollow site is most catalytically active site in both supercells of h-AlC ML. We believe that our results will inspire experimentalists to fabricate this new 2D material for achieving the desired range of HER activity. 相似文献
69.
Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively. 相似文献
70.