New weighting factors assignment of evidence theorybased one vidence distance

Evidence theory has been widely used in the information fusion for its effectiveness of the uncertainty reasoning. However, the classical DS evidence theory involves counter-intuitive behaviors when the high conflict information exists. Based on the analysis of some modified methods, Assigning the weighting factors according to the intrinsic characteristics of the existing evidence sources is proposed, which is determined on the evidence distance theory. From the numerical examples, the proposed method provides a reasonable result with good convergence efficiency. In addition, the new rule retrieves to the Yager's formula when all the evidence sources contradict to each other completely.

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

Application of Artificial Neural Network, Kriging, and Inverse Distance Weighting Models for Estimation of Scour Depth around Bridge Pier with Bed Sill

This paper outlines the application of the multi-layer perceptron artificial neural network (ANN), ordinary kriging (OK), and inverse distance weighting (IDW) models in the estimation of local scour depth around bridge piers. As part of this study, bridge piers were installed with bed sills at the bed of an experimental flume. Experimental tests were conducted under different flow conditions and varying distances between bridge pier and bed sill. The ANN, OK and IDW models were applied to the experimental data and it was shown that the artificial neural network model predicts local scour depth more accurately than the kriging and inverse distance weighting models. It was found that the ANN with two hidden layers was the optimum model to predict local scour depth. The results from the sixth test case showed that the ANN with one hidden layer and 17 hidden nodes was the best model to predict local scour depth. Whereas the results from the fifth test case found that the ANN with three hidden layers was the best model to predict local scour depth.

Application of Weighted Average Method in Evaluation System for Rural Cadre Distance Education

We elaborate the application method,process and effect of weighted average method in the examination and evaluation system for modern distance education of rural party members and cadres.The study shows that this method reflects the evaluation results objectively and comprehensively and plays a remarkable role in establishing and improving the examination and evaluation system,thus will have an important reference value for the development of modern distance education of rural party members and cadres in the future.

Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.

Measuring Musical Rhythm Similarity: Further Experiments with the Many-to-Many Minimum-Weight Matching Distance

Musical rhythms are represented as sequences of symbols. The sequences may be composed of binary symbols denoting either silent or monophonic sounded pulses, or ternary symbols denoting silent pulses and two types of sounded pulses made up of low-pitched (dum) and high-pitched (tak) sounds. Experiments are described that compare the effectiveness of the many-to-many minimum-weight matching between two sequences to serve as a measure of similarity that correlates well with human judgements of rhythm similarity. This measure is also compared to the often used edit distance and to the one-to-one minimum-weight matching. New results are reported from experiments performed with three widely different datasets of real- world and artificially generated musical rhythms (including Afro-Cuban rhythms), and compared with results previously reported with a dataset of Middle Eastern dum-tak rhythms.

无线传感器网络动态加权DV-Distance算法

无线传感器网络DV-Distance定位算法,采用未知节点与锚节点间的累计跳段距离代替欧式距离计算节点位置,存在较大的定位误差。针对这一问题,提出一种动态加权DV-Distance改进定位算法,基于未知节点的修正模式,保证定位网络中每个未知节点具有不同的修正系数;通过动态加权修正模型,用锚节点间距离、跳数等信息计算修正系数,采用动态加权的方法将不同方向上的修正系数进行整合,修正未知节点与锚节点间累计跳段距离,提高算法的定位精度。通过仿真验证了算法具有更高的定位精度;并进一步通过实验验证了算法的有效性和可行性。

无线传感器网络DV-Distance定位算法

针对DV-Distance定位算法得到的距离值误差较大的问题,提出一种定位精度相对较高的改进型DV-Distance算法。DV-Distance定位算法通过求未知节点到参考节点之间跳段距离之和来确定未知节点坐标,改进算法在原算法的基础上,将参考节点间的真实距离与这些参考节点间的跳段距离之和的比值作为修正权值,用这个修正权值来提高定位所需距离值的精确度,并利用RSSI测距技术限定可较为精确测距的有效未知节点,从而更进一步提高定位的精度。通过计算机的仿真和实验验证,结果表明此改进算法相对于原算法,较为明显的降低了定位误差,提高了定位的精度。

Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the city.The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations.But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms.In light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter degradation.The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion.Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting.The model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

Learning distance effect on lignite quality variables at global and local scales

Determining scale and variable effects have critical importance in developing an energy resource policy.This study aims to explore the relationships in heterogeneous lignite sites using different scale models,spatial weighting as well as error-based pair-wise identification.From a statistical learning framework,the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models.In this way,the critical roles of spatial weights provided by the coordinates have been specified at a global scale.The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency.Because relationships among lignite quality variables and sampling locations are spatially non-stationary,the local structure and interdependencies among the variables were analyzed by geographically weighting regression.The local analyses including spatial patterns of bandwidths,search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning.The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.

Mapping Distribution of Precipitation, Temperature and Evaporation in Seydisuyu Basin with the Help of Distance Related Estimation Methods

In this research, distributions of precipitation, temperature and evaporation in Seydisuyu basin were analyzed with the help of inverse distance weighted (IDW) method. Because real meteorological data of the basin (precipitation, temperature and evaporation) do not have normal distribution, precipitation, temperature and evaporation distribution maps are drawn after normalization process. The number of meteorological stations, in other words the number of samples, is low, so only IDW method is used in this research. In addition to the research, reliability of the results obtained with the help of inverse distance weighting method was examined with accuracy analysis. The purpose of this study, the spatial distribution of meteorological data on a basin or areas is to demonstrate the applicability of the statistical basis.

基于逐点修匀的Ball-IDW网格变形方法

为了提高网格变形的效率,提出一种基于逐点修匀的反距离加权函数(IDW)插值网格变形方法(简称Ball-IDW方法).根据网格拓扑关系,以每个内部节点为中心构造子网格系统,将整体网格分解为多个局部子网格;通过在中心节点到子网格的边界上添加辅助插值节点,增强IDW方法的保形能力;遍历所有内部节点,利用逐点修匀的方式进行迭代,直到满足收敛条件;当网格变形幅度过大时,通过递归二分法修正中心节点的位移防止单元交叉,提高方法的鲁棒性.Ball-IDW方法的计算过程是显式的,不需要求解任何线性方程,因此具有较高的计算效率.通用实例结果表明,与径向基函数方法和IDW方法相比,Ball-IDW方法在质量更高的前提下,计算效率分别提高20%~85%和10%~80%,解决三维问题时的变形能力提高2倍.

降雨资料Kriging与IDW插值对比分析—以漓江流域为例

降水空间化信息在很多领域都具有重要意义,而进行空间插值方法形成降水空间化信息是当代较为常用的方式。面对众多的插值方法其插值精度成为我们是否采用的关键,就kirging插值法与IDW(Inverse Distance Weighting)插值进行研究以探讨其插值效果,为此类科学研究提供依据。研究以漓江流域内各气象站点降水统计资料为基础分别采用上述两种方法进行插值处理,通过与预留实测站点比较评判方法的插值效果的优劣。结果表明,多年月平均降水量作为时间步长时,降水丰沛的月份使用kriging插值法较优于IDW插值法,而枯水月份则使用IDW插值法较优于kriging插值法。

基于Gini系数和空间IDW插值法的山东省卫生资源配置公平性分析

目的从人口、地理和地域空间的角度来评价山东省卫生资源配置的公平程度。方法采用基尼系数对卫生资源配置的人口和地理公平性进行评价;利用地理信息系统的反距离权重插值法对地域空间上卫生资源配置的公平性进行描述性分析。结果山东省卫生资源配置在人口和地理上均处于相对公平状态;反距离权重插值法分析得出,经济发达地区卫生资源配置水平普遍高于经济落后地区。结论山东省卫生资源配置总体来说公平性较好,但是地域分布有一定差异,应该引起管理者的重视,提高对经济落后地区的卫生资源的投入。

基于双目视觉和改进YOLOv8n的火灾检测及测距方法

针对火灾检测出现的漏检误检、模型参数量大及定位困难的问题,基于双目视觉和改进YOLOv8n提出了一种轻量化火灾检测及测距方法.通过双目相机拍摄图片,使用改进的检测算法YOLOv8n-AEM和现有的测距算法SGBM进行检测和测距.首先,在主干网络中引入可变核卷积AKConv和EMA注意力机制,通过构建不规则卷积核有效提取火灾的特征;然后,在颈部网络中构建C2f-SCConv模块,通过特征重组降低模型参数,提高检测速度;其次,基于最小点距离改进损失函数,解决火源与光源重叠导致的漏检与误检问题;最后,增加小目标检测头,提高对小火苗的检测能力.实验结果表明,改进后的检测算法P、R、mAP分别为83.6%、76.4%、83.6%,分别提高了2.5%、3.6%、4.8%;参数量和模型大小分别为2.54 M和5.1 MB,分别降低了15.3%和15%;测距精度误差不超过2.5%,证明改进的方法能准确完成火灾的检测及测距.

基于IDW的铜陵地区土壤重金属空间分析及污染评价

为研究铜陵地区土壤重金属的空间分布特征和污染现状,通过电感耦合等离子体原子发射光谱法（ICP-AES）对土壤中的As、Hg、Pb、Cu、Zn 5种重金属元素进行了测定,应用反距离权重法（IDW）对重金属含量进行了空间分析,并采用内梅罗指数法对污染程度进行了评价。结果表明,除Hg外,土壤中As、Pb、Cu、Zn质量比均高于铜陵地区背景值,其中As、Cu和Zn质量比更高,超国家三级标准（GB15618—1995）的百分比分别为48. 11%、11. 35%、11. 08%。研究区内土壤As和Cu为重度污染,Zn为中度污染,Pb为轻度污染,Hg为无污染;土壤重金属含量高值区和污染严重区空间分布特征一致,主要集中分布在新桥硫铁矿、冬瓜山铜矿、铜官山铜矿等矿区;综合污染评价指数为8. 621,为重污染状态,污染区几乎覆盖整个区域。通过重金属污染源解析,可初步判定矿产开发活动为最主要的人为污染源,因此,必须加强矿山地质环境生态保护与修复。

海洋次表层FIDW温盐影像插值算法

最大限度地提高海洋次表层温盐影像插值速度,是解决渔况信息动态服务效率的关键。IDW可以实现海洋剖面观测点的二维插值,但通用的IDW算法效率较低,采用FIDW方法提高插值效率,在使用相同计算公式的情况下实现影像快速插值,插值结果经过实测数据验证表明误差的范围可以满足渔业分析应用的需要。

台站温度质量控制中IDW算法改进及应用

在反距离加权插值算法的基础上,结合气象上温度随高程的变化,以及各测站垂直方向上温度场与气压场的关系,对国内地面气象观测站的温度数据进行插值算法改进。然后,从搜索半径与距离权重系数的关系,以及参与插值站点数量与距离权重系数的关系两个方面进行试验数据分析,试图找出相对最佳的算法参数和最小的插值误差。最后,将改进的算法应用到温度观测数据的质量控制中。结果表明:改进后的算法可明显改善因近地面逆温造成的插值误差,在台站分布较密集的我国中东部地区,温度质量检验的灵敏度可达到1至5摄氏度。

基于ArcEngine与IDW法的围岩亚级分级研究

为减少地铁隧道施工中经常出现的围岩坍塌、透水等地质灾害,分析施工段围岩地质情况,采用一种基于权重的岩体基本质量亚级分级方法对围岩进行分级,结合C#与Arc Engine的二次开发,根据距离高次方反比法原理,利用已有地质钻孔数据,设计出对围岩进行等级估算和三维分级显示渲染功能的系统,并对该亚级分级方法的准确性进行验证。实验结果表明,基于权重的岩体基本质量亚级分级方法对隧道围岩分级更精确,具有较好的实用性。设计的系统对围岩等级估算的反算误差为8.82%,与实际国岩开挖后测得的信息对比误差为13%,三维分级渲染结果与实际情况较为接近,该方法可以为地铁隧道施工提供参考。

基于基尼系数和IDW插值法的云南省卫生资源配置公平性分析

目的分析2017年云南省卫生资源配置的公平性,为今后卫生资源的投入、规划布局提供依据。方法采用基尼系数对云南省2017年各项卫生资源按人口和地理分布的公平性进行评价,利用反距离权重插值法对云南省2017年卫生资源按地理分布情况进行描述分析。结果 2017年云南省卫生机构数、病床数、卫生技术人员和执业(助理)医师数按人口分布的基尼系数分别为0.138 6、0.124 8、0.190 4和0.235 5,按地理分布的基尼系数分别为0.331 3、0.379 0、0.379 7和0.402 8。结论2017年云南省卫生资源按人口配置的公平性优于地理配置的公平性,其中执业(助理)医师配置的公平性最差。云南省2017年卫生资源在地理空间上呈现以昆明市为中心向四周辐射递减的趋势分布,而西北部区域卫生资源配置最低。