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.

Relative ordering of square-norm distance correlations in open quantum systems

We investigate the square-norm distance correlation dynamics of the Bell-diagonal states under different local deco- herence channels, including phase flip, bit flip, and bit-phase flip channels by employing the geometric discord （GD） and its modified geometric discord （MGD）, as the measures of the square-norm distance correlations. Moreover, an explicit comparison between them is made in detail. The results show that there is no distinct dominant relative ordering between them. Furthermore, we obtain that the GD just gradually deceases to zero, while MGD initially has a large freezing interval, and then suddenly changes in evolution. The longer the freezing interval, the less the MGD is. Interestingly, it is shown that the dynamic behaviors of the two geometric discords under the three noisy environments for the Werner-type initial states are the same.

Correlation between laser-wire distance and Nd：YAG laser ＋ P-GMA hybrid welding heat input parameters

In laser ＋ P-GMA hybrid welding, laser-wire distance is an important parameter to describe the distance from laser spot to the center of the pulsed gas metal arc. The experiments results show that the optimal laser-wire distance with the deepest weld penetration increases with welding current and laser power being increased and decreases with welding speed being increased. Welding current, laser power and welding speed determine the hybrid welding heat input in laser ＋ arc hybrid welding process, so there is a correlation between the optimal laser-wire distanee and the hybrid heat input welding parameters for the deepest weld penetration： the optimal laser-wire distance increases with the heat input being increased. The positive correlation between the optimal laser-wire distance and the hybrid welding heat input is induced by the characteristics of the limited influence of P-GMA welding process on laser transmission and the dependence of weld penetration of hybrid welding on laser power.

Dynamic spatiotemporal correlation coefficient based on adaptive weight

Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic entity attributes and are interrelated in space and time.Such correlations have therefore drawn increasing attention in financial risk management.However,classical correlation measurements are typically based on either time series correlations or spatial dependence;they cannot be directly applied to financial data with spatiotemporal correlations.The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can(1)address the absolute quantity,dynamic quantity,and dynamic development of financial data and(2)be used for risk grading,financial risk evaluation,and portfolio management.To verify the validity and superiority of this model,cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation,respectively.Empirical findings show that the proposed coefficient is highly effective and convenient compared to others.Overall,our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.

随机空间几何构型下物联网络攻击节点定位

物联网各节点形成的空间几何构型随机性,决定了攻击节点的空间分辨率出现较大波动,继而影响攻击节点的定位精度。针对物联网下各节点形成的空间几何构型随机性,利用节点脉冲和功率的关联属性不受空间几何构型影响的特征,设计一种攻击节点定位算法。定位算法检测节点脉冲信号,设计最优功率关联算法,关联功率与脉冲的属性。通过关联属性构建节点距离测算定位方法。实验结果表明:文中的物联网攻击节点定位方法,在低、中、高阶三种攻击特征形式下,可以实时准确定位攻击性节点位置,具备较强的实用性。

vbICA方法用于GNSS坐标序列共模误差提取研究

全球导航卫星系统(global navigation satellite system,GNSS)坐标序列精度主要受共模误差(common mode error,CME)影响。为提高GNSS坐标序列精度,采用变分贝叶斯独立分量分析方法(variational Bayesian independent component analysis,vbICA)提取实验区20个GNSS测站坐标序列的CME,并对比分析vbICA与主成分分析(principal component analysis,PCA)和独立分量分析(independent component analysis,ICA)的滤波性能。结果表明,vbICA滤波效果优于PCA和ICA;经vbICA滤波后,E、N、U方向坐标残差序列均方根(RMS)平均降低36.57%、31.63%、10.97%,距离相关系数平均降低60.53%、56.84%、25.80%;扣除CME后,GNSS速度场估计更加可靠和精确,可有效提高GNSS坐标序列精度,为地球动力学研究提供可靠的数据支撑。

基于相关性局域化迭代集合平滑反演渗透系数场

在地下水流和溶质运移问题中,有较多研究基于物理距离局域化集合同化方法反演水文地质参数。当反演参数与观测信息之间不存在物理距离时,这种方法不适用。为了克服这个局限,通过渗透系数与水头信息之间的相关性计算局域化方法的阻滞因子,构建基于相关性的局域化迭代集合平滑方法。为了方便比较,将该方法和一种基于物理距离的局域化迭代集合平滑一同用于同化水头信息反演二维孔隙承压含水层的渗透系数场。算例中考虑了不同集合大小、观测误差及观测数量等因子的组合,便于分析其对渗透系数反演精度的影响。研究结果显示:(1)在所有算例中新方法得到的渗透系数均方根误差范围为[0.8307,0.9590],都小于基于物理距离方法的均方根误差,范围为[0.8394,1.0000];(2)基于物理距离方法得到的渗透系数场空间上存在不连续性,而新方法的结果不存在此现象。文章提出了一种新的基于相关性局域化迭代平滑方法,该方法不需要依赖参数与观测信息之间的物理距离且参数反演精度高于基于物理距离的方法,可作为参数反演的科学工具。

关系嵌入和类协方差度量的小样本图像分类

在小样本图像分类的问题情景下,图像之间具有结构性、辨别性的特征关系还未被重视,图像的特征关系匹配不足,导致特征嵌入不完善,度量效果不明显.为了关注图像特征之间的匹配关系,通过关注单个图像的空间信息及匹配图像间的特征,实现度量嵌入特征的关系,提出基于关系嵌入和类协方差度量的网络结构,通过自相关注意表示和交互相关注意来学习图像内和图像间的特征关系.其中,将单个图像的基本特征表示映射成自相关张量,关注其中的通道空间信息,学习图像张量内结构信息.而对于图像对,计算2个图像表示的互关联信息,并生成共同注意,从而得到图像间具有判别性的关系信息.最后用马氏距离度量特征关系.经过大量实验验证之后,在3个小样本基准数据集中评估,并与大量现有方法进行对比,结果显示网络结构对学习特征间关系具有一定的有效性.

基于一种距离相关的超高维生存数据Model-Free特征筛选

随着大数据时代的来临,数据维度爆炸式增长,超高维数据的降维问题逐渐成为众多研究领域的热点话题。由于响应变量通常存在右删失,处理超高维完全数据的降维方法在右删失数据中将不再适用。本研究提出一种新的基于距离相关能有效处理超高维右删失数据的特征筛选方法。首先利用距离相关系数计算每个协变量对响应变量的边际效应,建立与该系数有关的筛选指标,然后再根据事先确立的筛选准则进行特征筛选。提出的特征筛选方法不依赖任何模型结构假定,因此可以有效避免模型指定错误带来的不良后果。此外,该方法采用的距离协方差估计量是总体距离协方差的一个无偏估计,统计准确性和计算精度高。模拟和实证研究表明,提出的方法能在保留所有重要变量的前提下快速剔除与响应变量相关程度较弱的协变量,从而达到降低参数维数的目的。

用户用电负荷变化的异常检测与识别

在智能电网时代,大部分用电异常行为都会伴随用电负荷的变化,研究用户用电行为对于电力系统的运行和管理都至关重要。为此,提出一种直接利用负荷数据进行计算,通过计算特征用电负荷曲线与日用电负荷曲线之间的相关度来判断用户是否存在异常用电行为的方法。在相关度计算过程中,将欧氏距离与皮尔逊相关系数相结合,以更准确地判断用户的用电负荷是否发生重大变化。此外,为提高判断的准确性和灵活性,采用自适应阈值策略对500组数据进行实验研究。相关度计算的结果表明,其中122组被判断为负荷变化过大,99组数据存在负荷异常事件,该方法的判断准确率达到了81.1%。

我国粮食安全水平测度及影响因素研究——基于2012—2022年31个省级面板数据

基于中国2012—2022年31个省区市面板数据,综合采用全局熵值法、TOPSIS距离函数法和灰色关联分析法,实证研究了中国粮食安全水平及影响因素。研究结果显示,2012—2022年我国大部分省区市粮食安全水平呈先下降后上升的趋势。从分维度指标综合指数的时间演化趋势来看,大部分省份的粮食供给安全、粮食生产稳定性安全、粮食生产持续性安全维度综合指数呈上升趋势,粮食生产持续性安全维度综合指数的变动较大。灰色关联分析结果显示,单位面积产量、耕地面积比例、农业机械总动力、人均粮食占有量、粮食生产波动系数等因素与粮食安全水平关联度较大。未来,应增强耕地生产能力,确保耕地面积的基本稳定;加强农业基础设施建设,持续提升粮食供给保障能力;促进农业产业化发展,优化粮食生产结构和布局;加强农业风险管理,推动农业科技创新和应用。

常规直流逆变站交流送出线路距离保护适应性分析与对策

常规直流逆变站交流送出线路故障通常会引发换相失败,存在故障电压和电流波形畸变等问题,可能导致工频相量距离保护性能劣化。首先,分析了工程中广泛应用的正序电压极化距离保护在常规直流逆变站交流送出线路中的适应性,分析结果表明,当逆变站运行于单回交流出线工况时,传统正序电压极化距离保护不再适用;然后,基于交流线路的RL模型,提出了基于波形相关系数的时域距离元件,可以不受逆变站非线性输出特性的影响,并针对时域距离元件存在的出口近区故障方向判别问题,利用电流畸变程度判断故障方向;最后,通过仿真验证了时域距离保护方案的有效性。

基于XGBoost算法的医保信息系统入侵安全风险监测方法

目的针对常规的医保信息系统对入侵行为的判定过程较为繁杂、监测反应时间较长的问题,提出基于XGBoost算法的医保信息系统入侵安全风险监测方法。方法按照安全性影响因素的关系,对安全因素关联度修正强度进行计算与修正,并对修正后的安全因素关联进行划分,得到划分的系统入侵安全信息关联度;根据划分关联度参数,基于XGBoost算法对入侵行为进行分级判定;按照结果,使用树编辑距离算法,对入侵行为信息进行匹配,并建立神经网络监测结构;根据匹配结果建立对应报警机制,实现对医保信息系统入侵安全风险的实时监测。结果实验结果表明,本文方法对医保信息系统入侵安全风险的监测平均反应时间小于3 s,反应时间较短,安全性较高。结论本文提出的基于XGBoost算法的医保信息系统入侵安全风险监测方法,能够有效监测医保信息系统的入侵安全风险,具有较高的应用价值。

Relationship Between Heterosis and Parental Genetic Distance Based on Molecular Markers for Functional Genes Related to Yield Traits in Rice

The genetic distances among 18 cytoplasmic male sterile lines and 11 restorer lines were analyzed with molecular markers derived from yield-related functional genes. The correlation between parental genetic distance and heterosis was investigated by analyzing the performance of 47 combinations. The results showed that the genetic distance was significantly correlated with yield heterosis （r=0.29^＊）, but not significantly correlated with heterosis for other traits, such as number of effective panicles per plant, seed setting rate, 1000-grain weight, number of grains per panicle and theoretical yield. However, the correlation coefficient was so small that the parental genetic distance could not to be used to predict heterosis.

Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor

Anti-detection is becoming as an emerging challenge for anti-phishing.This paper solves the threats of anti-detection from the threshold setting condition.Enough webpages are considered to complicate threshold setting condition when the threshold is settled.According to the common visual behavior which is easily attracted by the salient region of webpages,image retrieval methods based on texton correlation descriptor(TCD)are improved to obtain enough webpages which have similarity in the salient region for the images of webpages.There are two steps for improving TCD which has advantage of recognizing the salient region of images:(1)This paper proposed Weighted Euclidean Distance based on neighborhood location(NLW-Euclidean distance)and double cross windows,and combine them to solve the problems in TCD;(2)Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting conditions.Experimental results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable,and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain.

Exact Statistical Distribution and Correlation of Human Height and Weight: Analysis and Experimental Confirmation

The statistical relationship between human height and weight is of especial importance to clinical medicine, epidemiology, and the biology of human development. Yet, after more than a century of anthropometric measurements and analyses, there has been no consensus on this relationship. The purpose of this article is to provide a definitive statistical distribution function from which all desired statistics (probabilities, moments, and correlation functions) can be determined. The statistical analysis reported in this article provides strong evidence that height and weight in a diverse population of healthy adults constitute correlated bivariate lognormal random variables. This conclusion is supported by a battery of independent tests comparing empirical values of 1) probability density patterns, 2) linear and higher order correlation coefficients, 3) statistical and hyperstatistics moments up to 6th order, and 4) distance correlation (dCor) values to corresponding theoretical quantities: 1) predicted by the lognormal distribution and 2) simulated by use of appropriate random number generators. Furthermore, calculation of the conditional expectation of weight, given height, yields a theoretical power law that specifies conditions under which body mass index (BMI) can be a valid proxy of obesity. The consistency of the empirical data from a large, diverse anthropometric survey partitioned by gender with the predictions of a correlated bivariate lognormal distribution was found to be so extensive and close as to suggest that this outcome is not coincidental or approximate, but may be a consequence of some underlying biophysical mechanism.

Large scale characteristic distance between strong earthquake

The global seismicity shows a characteristic distance of about 23°. The middle deep and deep earthquakes in the European and Asian blocks concentrate on several zones which separate about 23° away from earch other. The shallow strong earthquakes ( M S≥7) form belts of over 20°, the ends of these belts are near the concentration points of middle deep and deep earthuakes. The Circular Pacific island arcs, volcanoes, shallow earthquakes, middle deep and deep earthquakes also show the characteristic distance of about 23°.

The Influence of the Exchange-Correlation Functional on the Non-Interacting Kinetic Energy and Its Implications for Orbital-Free Density Functional Approximations

In this work it is shown that the kinetic energy and the exchange-correlation energy are mutual dependent on each other.This aspect is first derived in an orbital-free context.It is shown that the total Fermi potential depends on the density only,the individual parts,the Pauli kinetic energy and the exchange-correlation energy,however,are orbital dependent and as such mutually influence each other.The numerical investigation is performed for the orbital-based non-interacting Kohn-Sham system in order to avoid additional effects due to further approximations of the kinetic energy.The numerical influence of the exchange-correlation functional on the non-interacting kinetic energy is shown to be of the orderof a few Hartrees.For chemical purposes,however,the energetic performance as a function of the nuclear coordinates is much more important than total energies.Therefore,the effect on the bond dissociation curve was studied exemplarily for the carbon monoxide.The data reveals that,the mutual influence between the exchange-correlation functional and the kinetic energy has a significant influence on bond dissociation energies and bond distances.Therefore,the effect of the exchange-correlation treatment must be considered in the design of orbital-free density functional approximations for the kinetic energy.

高维线性模型的影响点诊断

从单影响点到多影响点2个角度回顾了影响点诊断领域的进展;重点介绍了近年发展起来的一些高维影响点检测新方法,该方法适用于自变量个数远超样本量的情形,可被看作是经典Cook距离在高维数据的推广.Cook距离量化了个体观测对最小二乘系数估计的影响,而新方法则捕获了个体观测对边际相关的影响,进而对变量选择和其他下游分析任务产生重要影响.数值模拟结果验证了新方法的可行性和有效性.

基于空间广义有序Probit模型的高速公路事故严重程度分析

为深入了解影响高速公路交通事故严重程度的显著因素,以广深沿江高速东莞段2014—2019年事故数据为研究对象,将事故严重程度分为3类(即无伤亡事故、轻伤事故、重伤亡事故),并以事故严重程度为因变量、13个潜在影响因素为自变量,通过条件自回归先验解析相邻事故间的空间相关性,建立不同关联距离阈值的空间广义有序Probit模型。结果表明:事故数据间存在显著的空间关联;空间广义有序Probit模型优于广义有序Probit模型和多项Logit模型,基于250 m关联距离阈值的空间广义有序Probit模型表现最优。该模型参数估计显示:车辆类型和归属地、事故发生时间、事故地点曲率、桥梁路段和事故类型对高速公路事故严重程度均有显著影响。边际效应结果表明:相对于小汽车间的交通事故,涉及客车、货车和其他类型车辆的交通事故导致人员重伤亡的概率分别增加3.27%、1.53%和4.11%;外省车使得重伤亡事故的发生概率增加1.02%;相对于周末、春季和桥梁路段,工作日、夏季和非桥梁路段发生的交通事故导致人员重伤亡的概率分别提高0.87%、2.38%和0.08%;单车事故导致重伤亡的概率比多车事故低1.64%;事故地点曲率每增加1 km-1,重伤亡事故的概率将降低1.54%。