Applicability of Bevis Formula at Different Height Levels and Global Weighted Mean Temperature Model Based on Near-earth Atmospheric Temperature

Weighted mean temperature(T m)is a critical parameter in Global Navigation Satellite System(GNSS)technology to retrieve precipitable water vapor(PWV).It is convenient to obtain high-precision T m estimates near surface utilizing Bevis formula and surface temperature.However,some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions.We investigate the applicability of the Bevis formula at different height levels and find that the Bevis formula has relatively high precision when the altitude is low,while with altitude increasing,the precision decreases gradually.To solve the problem,we analyze the relationship between T m and atmospheric temperature within the near-earth space range(the height range between 0~10 km)and find that they have a high correlation on a global scale.Accordingly,we build a global weighted mean temperature model based on near-earth atmospheric temperature.Validation results of the model show that this model can provide high-precision T m estimation at any height level in the near-earth space range.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

Global Attractor for High-dimensional Spacially Discrete FitzHugh-Nagumo System in Weighted Space

In this paper,We study the global attractor and its properties on in nite lattice dynamical system FitzHugh-Nagumo in a weighted space lσ^2×lσ^2.We prove the existence and uniqueness of the solution to the lattice dynamical system FitzHugh-Nagumo in lσ^2×lσ^2.Then we get a bounded absorbing set,which suggests the existence of global attractors.Finally,we study the uniform boundedness and the upper semicontinuity of the global attractor.

Multi-sensor optimal weighted fusion incremental Kalman smoother

In practical applications, the system observation error is widespread. If the observation equation of the system has not been verified or corrected under certain environmental conditions,the unknown system errors and filtering errors will come into being.The incremental observation equation is derived, which can eliminate the unknown observation errors effectively. Furthermore, an incremental Kalman smoother is presented. Moreover, a weighted measurement fusion incremental Kalman smoother applying the globally optimal weighted measurement fusion algorithm is given.The simulation results show their effectiveness and feasibility.

Soil Quality Assessment Using Weighted Fuzzy Association Rules

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.

WEIGHTED PSEUDO ALMOST-PERIODIC SOLUTIONS OF SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH MIXED DELAYS

In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.

GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression

Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region.

Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps

The traditional K-means clustering algorithm is difficult to determine the cluster number,which is sensitive to the initialization of the clustering center and easy to fall into local optimum.This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO(Self-Organization Map and Weight Particle Swarm Optimization).Firstly,the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center.Then,the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm.The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight,and the cluster center is the“food”of the particle group.Each particle moves toward the nearest cluster center.Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration.After a lot of experimental analysis on the commonly used UCI data set,this paper not only solves the shortcomings of K-means clustering algorithm,the problem of dependence of the initial clustering center,and improves the accuracy of clustering,but also avoids falling into the local optimum.The algorithm has good global convergence.

Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Asymptotic Analysis to a Diffusion Equation with a Weighted Nonlocal Source

In this paper,we deal with the blow-up property of the solution to the diffusion equation u_t = △u + a(x)f(u) ∫_Ωh(u)dx,x∈Ω,t>0 subject to the null Dirichlet boundary condition.We will show that under certain conditions,the solution blows up in finite time and prove that the set of all blow-up points is the whole region.Especially,in case of f(s) = s^p,h(s) = s^q,0 ≤ p≤1,p + q >1,we obtain the asymptotic behavior of the blow up solution.

Direct and Reverse Carleson Conditions on Generalized Weighted Bergman-Orlicz Spaces

Let D be the open unit disk in the complex plane C. For a〉 -1, let dAa（z）=（1 ＋a） （1 -｜z}^2） ^a da（z）be the weighted Lebesgue measure on ]D. For a positive function ω ∈ L^1（D,dAa）, the generalized weighted Bergman-Orlicz spaceA^ψω（D,dAa）is｜｜f｜｜ω^ψ=∫Dψ｜F（z）｜ω（z）dA^（z） 〈 ∞,where q; is a strictly convex Orlicz function that satisfies other technical hypotheses. Let G be a measurable subset of D, we say G satisfies the reverse Carleson condition for A^ψω （D, dAa） if there exists a positive constant C such that ∫Gψ（f（z））ω（z）dAa（z）≥C∫Dψ（｜f（z）dAa（z）.for all f ∈ .A^ψω （D,dAa）. Let μ be a positive Borel measure, we say μ satisfies the direct Carleson condition if there exists a positive constant M such that for all f∈Aψ^ω （D,dAa）,∫Dψ（｜f（z）｜）dμ（z）≤M∫Dψ（｜f（z）｜）ω（z）dAa（a）.In this paper, we study the direct and reverse Carleson condition on the generalized weighted Bergman-Orlicz space Aω^ψ（D,dAa）.We present conditions on the set G such that＇the reverse Carleson condition＇holds. ＂Moreover, we give a sufficient condition for the finite positive Borel measure μ to satisfy the direct carleson condition on the generalized weighted Bergman-Orlicz spaces.

Effect of Weight Function in Nonlinear Part on Global Solvability of Cauchy Problem for Semi-Linear Hyperbolic Equations

In this paper, we investigate the effect of weight function in the nonlinear part on global solvability of the Cauchy problem for a class of semi-linear hyperbolic equations with damping.

Electromagnetic Radiation Causes Weight Loss and Weight Destabilization of Objects with Presumed Elevated Levels of KELEA (Kinetic Energy Limiting Electrostatic Attraction), Relevance to Human Health and to Global Warming

A natural force has been proposed, which is required to prevent the fusion and disappearance of the discrete electrical charges that are present on electrostatically attached opposing electrical charges. This force may also explain the repulsion between objects with either matching positive or negative electrical charges. The energy of this force is referred to as KELEA (kinetic energy limiting electrostatic attraction). KELEA is especially attracted to dipolar compounds and to other materials with spatially separated opposite electrical charges. These compounds can be used to increase the level of KELEA in water. KELEA activated water can become an added source of KELEA for objects that are placed in close proximity to the water. It is generally held that the weight of an object is solely determined by its mass in relation to that of the earth. Yet, it was previously reported that the measured weight of certain KELEA attracting objects can undergo considerable variability over time. This observation is consistent with the concept that KELEA can contribute to the measured weight of certain objects. The present study strengthens this concept by demonstrating that the weight of cellulose containing materials, including paper, cotton fabrics, and wood, is increased if the materials are placed close to containers of KELEA activated water. It is further shown that electromagnetic radiation can significantly reduce the added weight of the KELEA exposed cellulose containing materials. Moreover, the previously added weight of the materials can be regained by replacing the materials back into the KELEA enhanced environment. It is proposed that the electrical charges that accompany electromagnetic radiation are able to competitively withdraw some of the KELEA from certain KELEA-enhanced objects. This effect can be reliably demonstrated using single sheets of writing paper, which are primarily composed of mechanically-bonded, branched cellulose fibers. There can be considerable fluctuations of the weight of the materials exposed to electromagnetic radiation after having been placed nearby to KELEA activated water. The weight instability is interpreted as being due to the electromagnetic radiation also triggering a dynamic process of rapid additions and removals of significant quantities of KELEA to and from objects. These observations are relevant to the further understanding of KELEA and to the potential health and climate consequences of manmade electromagnetic radiation causing a reduction in the environmental levels of KELEA.

区间函数型数据构权方法研究

针对由数据量过大而引起的函数型综合评价信息损失或是计算复杂度过大等问题,文中提出了一种基于区间函数型数据的综合评价方法,并针对区间函数型数据表支持的综合评价问题的特殊性,提出了一种新的确定权重系数的"全局"拉开档次法.相较于函数型综合评价,区间函数型综合评价在呈现连续的函数特征同时,增加了区间化的步骤,能够更好地挖掘数据信息,提升综合评价的效率.最后通过以义乌小商品景气指数为例,分别使用函数型数据形式和区间函数型数据形式下的"全局"拉开档次法对其进行赋权,进而进行评价研究.结果表明该文提出的区间函数型综合评价方法更具优势.

基于改进蝴蝶搜索算法的DGPS整周模糊度快速解算

为了快速准确地解算差分全球定位系统(DGPS)整周模糊度,提出了一种改进蝴蝶搜索算法(IBOA)求解整周模糊度。首先在蝴蝶优化算法(BOA)的香味系数中加入一个自适应权重,弥补BOA算法觅食行为中较弱的搜索能力;其次使用动态切换概率权衡BOA算法中全局搜索与局部搜索的比例;最后在全局搜索和局部搜索阶段引入新的迭代位置更新策略,提升了算法全局搜索能力和跳出局部最优能力。与最小二乘模糊度降相关平差算法(LAMBDA)算法进行1000个历元数据的解算对比实验,结果表明所提算法的平均搜索成功率比LAMBDA算法提高了5.07%。

基于改进粒子群算法优化的染色木材颜色检测算法研究

为提高染色木材颜色的检测精度和速度,对樟子松木材单板进行染色,选取染色单板的光谱反射率作为输入,以极限学习机模型为基础构建预测模型,对染色单板的色度参数L^(*)、a^(*)、b^(*)进行预测,运用粒子群算法对ELM权值和阈值进行寻优,并引入非线性惯性权重和新的位置与速度更新策略改进粒子群算法,以消除其易陷入局部最优的缺点。此外,以L^(*)、a^(*)、b^(*)平均绝对误差为评价指标,与基础ELM模型及其他模型作对比,发现优化后的模型平均绝对误差为0.16,测色效果相较于基础ELM的0.68、麻雀算法优化的ELM的0.37等具有明显优势,这对于提高木材染色生产效率具有重要意义。

全局减方差方法在大空间γ辐射场计算中的应用

为了提高大空间γ辐射场全局计算效率,开展了全局减方差(Global Variance Reduction,GVR)方法在大空间γ辐射场计算中的应用研究。针对计数栅元/网格体积差异造成的过度分裂问题,引入体积修正因子修改全空间权窗参数。体积修正后的基于通量的GVR方法计算的全局品质因子(FOMG)比直接模拟提高约39倍。针对非计数区计算耗时问题,提出了非计数区修正方法,使得FOMG因子进一步提高40%。在引入体积和非计数区修正的基础上,在大空间γ辐射场计算中与基于粒子误差、权重、径迹、数目、能量、碰撞和通量的7种GVR方法进行对比。结果表明:7种GVR方法计算的FOMG因子比直接模拟提高2~3个量级,基于误差的标准差σ降低2~3个量级;而基于权重的GVR方法计算的FOMG因子比直接模拟提高2304倍,在所有GVR方法中减方差效果最好。在基于通量的GVR方法中引入光滑因子SI后,模拟计算的权窗下限随SI增加而减小,FOMG因子随SI的增加先增加后减小。当SI=0.8时,该方法计算的FOMG因子最大,比直接模拟提高3246倍。

2001–2020年中国广西及东盟区域1 km分辨率降尺度月度降水数据集

中国广西及东盟区域位于亚洲东南部,同属亚热带季风气候,降水充沛,洪涝灾害频发,直接影响社会经济活动,高分辨率、高精度降水资料可有效支撑区域水资源、农业、灾害及生态等管理和研究。本研究以2001–2020年全球降水观测计划降水数据(GPM IMERG)为因变量,结合中分辨率成像光谱仪(MODIS)增强植被指数(EVI)、地表蒸散发(ET)、地表温度(LST)、先进星载热发射和反射辐射仪(ASTER)海拔(ELV)等解释变量,引入考虑变量随地理环境影响变化的地理加权回归(GWR)模型,构建空间分辨率为10 km的年尺度模型,经过测试选取验证精度良好的指数核函数。通过回代,基于空间分辨率为1 km的解释变量,构建了2001–2020年中国广西及东盟区域1 km年度降水数据集,并进一步采用比例指数法获得研究区2001–2020年1 km月度降水数据集。采用2001–2020年2679个地面观测站点数据对降尺度数据集进行验证,相关系数、均方根误差和偏差分别为0.792、74.610mm、-0.122%。本数据集能够有效反映1 km分辨率下的降水时空分布及其差异性,可广泛应用于水资源、农业、生态环境、灾害模拟等研究领域。

结合源偏倚和权窗的蒙特卡罗全局减方差方法

全局计数问题在反应堆pin-by-pin模型蒙特卡罗模拟和多物理耦合计算中动态粒子输运蒙特卡罗模拟等重大研究领域中都有广泛的应用场景.大量的全局减方差算法研究立足于全局计数误差分布的展平,由此提高全局计数的整体效率.本工作针对两种高效全局减方差算法,即均匀计数密度算法(属于源偏倚算法的一种)和权窗算法的结合展开研究,提出利用均匀计数密度算法的偏倚因子调整权窗下限,由此实现两种算法的有机结合.基于Hoogenboom-Martin压水堆全堆基准题中开展了一系列对比测试,验证了混合全局减方差算法更优于单一权窗算法或均匀计数密度算法,尤其是在降低最大误差方面.同时,基于新的指标,验证了均匀计数密度算法较经典的均匀裂变源算法具有更好的表现.研究结果表明,本文提出的混合全局减方差算法能高效求解全局计数问题,进一步促进了相关领域的研究.