Adaptive kernel-type estimator for square-integrable distribution density

1987 ◽  
Vol 26 (4) ◽  
pp. 318-324 ◽  
Author(s):  
A. Kazbaras
Keyword(s):  
Distribution Density ◽  
Adaptive Kernel ◽  
Integrable Distribution ◽  
Kernel Type

scholarly journals The Root-Soil Water Relationship Is Spatially Anisotropic in Shrub-Encroached Grassland in North China: Evidence from GPR Investigation

2021 ◽  
Vol 13 (6) ◽  
pp. 1137
Author(s):  
Xihong Cui ◽  
Zheng Zhang ◽  
Li Guo ◽  
Xinbo Liu ◽  
Zhenxian Quan ◽  
...  
Keyword(s):  
Soil Water ◽  
Root Distribution ◽  
Preferential Flow ◽  
Distribution Density ◽  
Vertical Direction ◽  
Additive Models ◽  
Depth Interval ◽  
Soil Core ◽  
Semi Arid ◽  
Free Area

To analyze the root-soil water relationship at the stand level, we integrated ground-penetrating radar (GPR), which characterized the distribution of lateral coarse roots (>2 mm in diameter) of shrubs (Caragana microphylla Lam.), with soil core sampling, which mapped soil water content (SWC) distribution. GPR surveys and soil sampling were carried out in two plots (Plot 1 in 2017 and Plot 2 in 2018) with the same size (30 × 30 m2) in the sandy soil of the semi-arid shrubland in northern China. First, the survey area was divided into five depth intervals, i.e., 0–20, 20–40, 40–60, 60–80, and 80–100 cm. Each depth interval was then divided into three zones in the horizontal direction, including root-rich canopy-covered area, root-rich canopy-free area, and root-poor area, to indicate different surface distances to the canopy. The generalized additive models (GAMs) were used to analyze the correlation between root distribution density and SWC after the spatial autocorrelation of each variable was eliminated. Results showed that the root-soil water relationship varies between the vertical and horizontal directions. Vertically, more roots are distributed in soil with high SWC and fewer roots in soil with low SWC. Namely, root distribution density is positively correlated with SWC in the vertical direction. Horizontally, the root-soil water relationship is, however, more complex. In the canopy-free area of Plot 1, the root-soil water relationship was significant (p < 0.05) and negatively correlated in the middle two depth intervals (20–40 cm and 40–60 cm). In the same two depth intervals in the canopy-free area of Plot 2, the root-soil water relationship was also significant (p < 0.01) but non-monotonic correlated, that is, with the root distribution density increasing, the mean SWC decreased first and then increased. Moreover, we discussed possible mechanisms, e.g., root water uptake, 3D root distribution, preferential flow along roots, and different growing stages, which might lead to the spatially anisotropic relationship between root distribution and SWC at the stand level. This study demonstrates the advantages of GPR in ecohydrology studies at the field scale that is challenging for traditional methods. Results reported here complement existing knowledge about the root-soil water relationship in semi-arid environments and shed new insights on modeling the complex ecohydrological processes in the root zone.

Effect of shape/size and distribution of microgeometries of textures on tribo-performance of crankpin bearing

2021 ◽  
pp. 135065012110105
Author(s):  
Nguyen Van Liem ◽  
Wu Zhenpeng ◽  
Jiao Renqiang
Keyword(s):  
Friction Coefficient ◽  
Friction Force ◽  
Contact Force ◽  
Distribution Density ◽  
Hydrodynamic Lubrication ◽  
Operating Conditions ◽  
Asperity Contact ◽  
Combined Model ◽  
Obvious Effect ◽  
Area Density

The effect of the shape/size and distribution of microgeometries of textures on improving the tribo-performance of crankpin bearing is proposed. Based on a combined model of the slider-crank mechanism dynamic and hydrodynamic lubrication, the distribution density, area density, and shape of spherical textures, square-cylindrical textures, wedge-shaped textures, and a hybrid between spherical texture and square-cylindrical texture on the crankpin bearing's tribo-performance are investigated under different operating conditions of the engine. The tribological characteristic of the crankpin bearing is then evaluated via the indexes of the oil film pressure p, asperity contact force, friction force, and friction coefficient of the crankpin bearing. The research results show that the distribution density with n = 12 and m = 6, and area density with α = 30% of various microtextures have an obvious effect on ameliorating the crankpin bearings tribo-performance. Concurrently, at the mixed lubrication region, the shape of the square-cylindrical texture on improving the tribo-performance is better than the other shapes of the spherical texture, wedge-shaped texture, and spherical and square-cylindrical texture. Particularly, all the average values of the asperity contact force, friction force, and friction coefficient with a square-cylindrical texture are significantly reduced by 14.6%, 19.5%, and 34.5%, respectively, in comparison without microtextures. Therefore, the microtextures of the spherical texture applied on the bearing surface can contribute to enhance the durability and decrease the friction power loss of the engine.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

A distribution density-based methodology for driving data cluster analysis: A case study for an extended-range electric city bus

2018 ◽  
Vol 73 ◽  
pp. 131-143 ◽  
Author(s):  
Haiming Xie ◽  
Guangyu Tian ◽  
Hongxu Chen ◽  
Jing Wang ◽  
Yong Huang
Keyword(s):  
Cluster Analysis ◽  
Distribution Density ◽  
City Bus ◽  
Extended Range
Sign in / Sign up

Export Citation Format

Share Document