Analysis of the COVID-19, Outbreak in Brazil Using Topological Weighted Centroid: An Intelligent Geographic Information System Approach

This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.

Reach Centroid Localization Algorithm

As much as accurate or precise position estimation is always desirable, coarse accuracy due to sensor node localization is often sufficient. For such level of accuracy, Range-free localization techniques are being explored as low cost alternatives to range based localization techniques. To manage cost, few location aware nodes, called anchors are deployed in the wireless sensor environment. It is from these anchors that all other free nodes are expected to estimate their own positions. This paper therefore, takes a look at some of the foremost Range-free localization algorithms, detailing their limitations, with a view to proposing a modified form of Centroid Localization Algorithm called Reach Centroid Localization Algorithm. The algorithm employs a form of anchor nodes position validation mechanism by looking at the consistency in the quality of Received Signal Strength. Each anchor within the vicinity of a free node seeks to validate the actual position or proximity of other anchors within its vicinity using received signal strength. This process mitigates multipath effects of radio waves, particularly in an enclosed environment, and consequently limits localization estimation errors and uncertainties. Centroid Localization Algorithm is then used to estimate the location of a node using the anchors selected through the validation mechanism. Our approach to localization becomes more significant, particularly in indoor environments, where radio signal signatures are inconsistent or outrightly unreliable. Simulated results show a significant improvement in localization accuracy when compared with the original Centroid Localization Algorithm, Approximate Point in Triangulation and DV-Hop.

基于反吸引速度更新机制的改进蜉蝣算法

针对蜉蝣算法(MA)前期收敛速度较慢、后期寻优精度不高等问题,提出一种基于反吸引速度更新机制的改进蜉蝣算法(MMOA)。采用改进型Tent混沌序列初始化蜉蝣种群,使蜉蝣分布更加均匀,提升了种群的多样性;结合MA的特点,引入反吸引速度更新机制指导蜉蝣速度更新,平衡算法的全局搜索和局部寻优能力,进而提升算法的收敛性能;对全局最优蜉蝣进行逐维的重心反向学习变异,降低各维度间的干扰,帮助算法跳出局部最优并加速收敛。基于12个标准测试函数和部分CEC2017测试函数进行对比仿真实验,结果表明:MMOA较灰狼优化(GWO)算法、MA等算法在收敛速度、寻优精度和稳定性等方面都具有明显优势。

基于聚类—重心法的连锁便利店配送中心选址研究

随着人民生活节奏的不断加快,便利店因其便利的属性而深受欢迎,其门店遍布各大小区和繁华地段。大量的门店给商家带来极大的仓储运输压力,因此建立一套新的物流配送系统,并据此系统建立选址模型是必要的。模型首先使用聚类算法得出中转中心地址,再由聚类出的中转中心地址通过重心法得到总配送中心地址。该模型简单便捷易操作,能有效降低运输成本,提升运输效率。

嘉陵江流域骤发干旱时空演变特征分析

为探究嘉陵江流域骤发干旱可识别特性与时空演变特征,基于标准化蒸发胁迫比研究了1980—2020年嘉陵江流域骤发干旱的时空分布特征,通过密度聚类算法提取骤发干旱斑块分析了斑块质心的轨迹变化,并探讨了骤发干旱暴发初期气象要素的异常情况。结果表明:流域西北部骤发干旱强度更高且发展速度更快,流域南部骤发干旱持续时间更长;流域骤发干旱事件主要发生在4—11月,且骤发干旱事件的质心主要沿东北和西北方向迁移;嘉陵江流域的骤发干旱主要由高温和降水共同主导。

基于冗余天线阵列和加权质心算法的光伏系统直流电弧故障定位方法

针对光伏直流电弧故障定位问题,本文通过研究故障电弧的电磁辐射特性,提出一种基于冗余天线阵列和加权质心算法的定位方法。先计算电弧燃烧时天线采集到的电磁信号的方均根值,与辐照度一起输入BP神经网络预测天线与电弧的距离;再构造冗余天线阵列研究不同天线数量和布局方式,选出接收信号最强的天线,将天线坐标和距离输入加权质心算法,获得定位结果;最后结合K均值聚类算法提高定位精度。实验结果表明,所提方法具有良好的定位能力。

基于YOLOv5算法的圆心定位方法

准确地获取图像中圆形目标的圆心是目标识别和定位中的关键问题。目前圆心定位主要采用最小二乘拟合圆以及HOUGH变换方法,但这些方法在不同程度上存在着鲁棒性不强、对环境光线要求高、需提前调试参数确定阈值、复杂背景下效果急剧变差等局限性。针对该问题,提出一种综合运用YOLOv5算法、Grabcut算法和灰度质心法进行图像分类、图像分割和灰度重心算法求取圆心坐标。该方法使用YOLOv5对图像中的圆形目标进行粗定位,再通过图像分割方法分割出圆形目标,最后使用加权型的灰度重心算法准确定位出圆心坐标,实现对圆形标志的可靠定位。用多个实验比较了该算法与现有算法的精度和稳定性,实验表明,该算法与现有算法相比,在干扰及形变的情况下圆心定位平均相对误差保持在0.5pixel以内,而在干扰、强形变及目标残缺的情况下仍能保持在7 pixel以内,该算法不仅提高了圆形目标圆心检测的鲁棒性和准确性,而且具有良好的抗干扰性。

Analysis of Five Typical Localization Algorithms for Wireless Sensor Networks

In this paper, the self-localization problem is studied. It is one of the key technologies in wireless sensor networks (WSNs). And five localization algorithms: Centroid algorithm, Amorphous algorithm, DV-hop algorithm, APIT algorithm and Bounding Box algorithm are discussed. Simulation of those five localization algorithms is done by MATLAB. The simulation results show that the positioning error of Amorphous algorithm is the minimum. Considering economy and localization accuracy, the Amorphous algorithm can achieve the best localization performance under certain conditions.

Research on WSN Double-Radius Localization Algorithm Based on Partition Judgment Mechanism

Localization technology is an important support technology for WSN(Wireless Sensor Networks). The centroid algorithm is a typical range-free localization algorithm, which possesses the advantages such as simple localization principle and easy realization. However, susceptible to be influenced by the density of anchor node and uniformity of deployment, its localization accuracy is not high. We study localization principal and error source of the centroid algorithm. Meanwhile, aim to resolve the problem of low localization accuracy, we proposes a new double-radius localization algorithm, which makes WSN node launch periodically two rounded communications area with different radius to enable localization region to achieve the second partition, thus there are some small overlapping regions which can narrow effectively localization range of unknown node. Besides, partition judgment mechanism is proposed to ascertain the area of unknown node, and then the localization of small regions is realized by the centroid algorithm. Simulation results show that the algorithm without adding additional hardware and anchor nodes but increases effectively localization accuracy and reduces the dependence on anchor node.

Fast Multilevel CVT-Based Adaptive Data Visualization Algorithm

Efficient data visualization techniques are critical for many scientific applications. Centroidal Voronoi tessellation(CVT) based algorithms offer a convenient vehicle for performing image analysis,segmentation and compression while allowing to optimize retained image quality with respect to a given metric.In experimental science with data counts following Poisson distributions,several CVT-based data tessellation algorithms have been recently developed.Although they surpass their predecessors in robustness and quality of reconstructed data,time consumption remains to be an issue due to heavy utilization of the slowly converging Lloyd iteration.This paper discusses one possible approach to accelerating data visualization algorithms.It relies on a multidimensional generalization of the optimization based multilevel algorithm for the numerical computation of the CVTs introduced in[1],where a rigorous proof of its uniform convergence has been presented in 1-dimensional setting.The multidimensional implementation employs barycentric coordinate based interpolation and maximal independent set coarsening procedures.It is shown that when coupled with bin accretion algorithm accounting for the discrete nature of the data,the algorithm outperforms Lloyd-based schemes and preserves uniform convergence with respect to the problem size.Although numerical demonstrations provided are limited to spectroscopy data analysis,the method has a context-independent setup and can potentially deliver significant speedup to other scientific and engineering applications.

融入重心反向学习和单纯形搜索的粒子群优化算法

针对粒子群优化PSO算法后期种群多样性差和易陷入局部最优解等问题,提出具备重心反向学习和单纯形搜索行为的粒子群优化COLS-PSO算法。初始时,基于混沌策略构造出搜索空间。进化过程中,基于Spearman系数选择需要进行重心反向学习的粒子,以帮助算法逃离局部极值区域。进一步引入局部搜索能力较强的单纯形搜索方法增强对最优粒子邻近区域的开发,以提高搜索精度。实验先在若干标准测试函数上进行,之后将COLS-PSO算法应用于软件测试数据生成问题。实验结果表明,COLS-PSO算法在求解精度、收敛速度和有效性方面表现较好,能够有效平衡种群多样性和算法收敛性的矛盾。

自适应变异蝴蝶优化算法

针对基本蝴蝶优化算法存在的收敛速度慢、求解精度低和易陷入局部最优等问题,提出一种自适应变异蝴蝶优化算法。首先,利用改进帐篷映射结合重心反向学习初始化种群,获得更好的初始解;其次,在位置更新处引入非线性惯性权重,平衡算法的全局搜索与局部搜索能力;最后,在算法运行过程中,根据群体适应度方差以及当前最优解大小来决定是否对当前最优解和最差解进行高斯变异二次寻优,增强算法跳出局部最优的能力。对12个基准测试函数的多种维度仿真实验结果表明,该算法在收敛速度、求解精度和寻优稳定性方面明显优于其他对比算法。

基于毫米波雷达与情感神经网络的室内人员跌倒检测算法

为了实现室内人员的跌倒检测,提出一种基于毫米波雷达的跌倒检测(Human Fall Detection,HFD)算法。首先,利用环境杂波滤除算法去除雷达数据的杂波信号;其次,在三维笛卡尔坐标系中利用参考质心聚类算法和躯干特征算法得到人物质心状态向量;最后,利用连续径向基情感神经网络(Continuous Radial Basis Emotional Neural Network,CRBENN)推断算法判断人物是否跌倒。实验结果表明,该算法能够在多种动作中实现人物跌倒判断及其定位,其检测准确率为99.28%,数据处理时间不超过35.8 ms,体现出了较好的准确性和实时性。

基于权重系数自调整的加权质心定位算法

针对目前采用的经典加权质心定位算法,其定位节点的位置的精确度较低。文章通过RSSI测距结合加权定位提出一种基于权重系数自调整的加权质心定位算法。该算法进行多个节点位置的估算时采用经典质心算法,之后对多个估计位置利用优化权值的方法进行求值,最后明确节点位置所在。该算法是融合了RSSI测距的技术和经典质心算法的加权算法,操作更为容易。经过进行仿真实验,可以看出与经典的定位算法相比,该算法定位的精确度有比较明显的提升。

融合学习机制的多混沌麻雀搜索算法

针对麻雀搜索算法(SSA)易受初始解的影响陷入局部极值、迭代后期收敛速度慢等缺陷,提出了一种融合学习机制的多混沌麻雀搜索算法(MMCSSA)。首先,引入重心反向学习策略(COBL)生成精英种群增强对多源优质搜索区域的勘探能力,提升算法的局部极值逃逸能力和收敛性能。其次,提出一种动态调整的黄金正弦领导策略并嵌入SSA中以改善发现者的搜索方式,增强算法的全局搜索能力。然后,提出一种基于学习机制的多混沌映射策略,该机制利用多混沌多扰动模式的特性,通过动态调用不同混沌映射赋予算法不同类别的扰动特征。混沌扰动失败时,引入高斯变异策略对当前解进行深度开发,两种策略协同作用,相互促进,极大增强了算法逃逸局部最优的能力。最后,将所提算法应用于12个不同特征的基准函数进行实验,结果表明与其他算法相比,MMCSSA在收敛精度、寻优速度和鲁棒性方面有更好的表现。

基于最小三角形算法的室内可见光三维定位方法

为进一步提高室内可见光三维定位的精度,提出了一种基于最小三角形算法的室内可见光三维定位方法。该方法采用视距链路模型,由定位终端接收携带发光二极管位置信息的光强信号,利用最小三角形算法和接收信号强度指示方法来计算接收机在室内的三维位置信息,再引入加权质心算法降低光路受遮挡所造成的影响。仿真结果表明:在室内5 m×5 m×3 m的定位区域内,提出的定位方法平均定位误差约为4.35 cm,平均高度误差约为1.65 cm,定位精度优于传统的室内可见光三维定位方法。

基于WiFi-蓝牙混合定位技术的震后压埋人员手机定位方法

我国地震灾害频发,快速确定压埋人员的准确位置成为震后救援工作的一大难点。文中提出的基于WiFi-蓝牙混合定位技术的震后压埋人员手机定位方法,通过在模拟的震后压埋环境周边分别部署WiFi探针和蓝牙网关,采集压埋人员携带的智能设备发射的信号接收强度(received signal strength indication, RSSI)数据,利用高斯-卡尔曼混合滤波对RSSI数据进行预处理,建立基于震后压埋环境的无线信号距离损耗模型,采用改进的加权质心定位算法解算压埋智能设备的坐标,通过评估WiFi和蓝牙定位结果可信度确定最终的定位结果。实验结果表明:单一的WiFi定位技术和蓝牙定位技术的平均定位精度分别为0.917、0.867 m,而WiFi-蓝牙混合定位技术的定位精度为0.541 m,相对于单一的WiFi和蓝牙定位技术,定位精度分别提高了41%和37.6%。

一种线结构光亚像素中心坐标提取方法

为了提高光条中心点提取的精度和速度,提出了一种新的线结构光条纹中心亚像素提取方法。首先对图像进行中值滤波,采用轮廓跟踪算法避免扫描光条纹区域外的像素,以此提高计算速度,结合灰度重心法对光条纹中心进行初提取;通过均方灰度梯度法计算光条纹的法线方向并以初提取点为中心进行双线性插值;以提取到的初始点和插值点采用加权灰度重心法计算光条纹中心的亚像素位置。实验结果表明,所提方法的标准误差在0.1400 pixel左右,运算时间约为0.0670 s。

基于邻域重心反向学习的混合樽海鞘群蝴蝶优化算法

针对蝴蝶优化算法(BOA)收敛速度较慢和过早收敛到局部解的问题,提出一种基于邻域重心反向学习的混合樽海鞘群蝴蝶优化算法(HSSBOA)。首先,将樽海鞘群算法(SSA)引入BOA中,使算法快速处理局部搜索阶段,并更新种群位置,从而更有效地完成寻优过程,避免算法陷入局部最优;然后,引入邻域重心反向学习以便更好地帮助算法在邻域内进行小范围精确搜索,从而提高算法的精度;最后,引入动态切换概率以改善搜索中全局与局部的比重,从而加快算法的搜索速度。选取10个标准检测函数进行测试,将HSSBOA与几个先进的优化算法从收敛精度、高维度数据、收敛速度、Wilcoxon秩和检验和平均绝对误差(MAE)五个方面进行对比分析。研究结果表明,相较于其他算法,HSSBOA取得了更优的结果。消融实验进一步验证了各项改进均为正向作用。实例问题上的表现表明相较于其他方法,在求解有约束的复杂问题时,HSSBOA能够更有效地搜索出最优解。可见HSSBOA在寻优精度、稳定性和收敛效率等方面取得了一定的优势,并且能够求解复杂的现实问题。

基于YOLOv5s和改进质心跟踪的人员跌倒检测

针对目标检测器检测跌倒时过于依赖卷积网络分类效果、无法利用运动信息的问题,本文设计了一种基于YOLOv5s和改进质心跟踪的跌倒检测模型。为解决耗费资源问题,用MobileNetV3网络和Slim Neck模块对YOLOv5s进行轻量化,同时将MobileNetV3网络中的SE模块替换为更高效的ECA模块,降低网络复杂度的同时保持较高的精度。引入哈希感知算法改进质心跟踪,增加目标关联的依据,提高跌倒检测的准确性。实验结果显示改进YOLOv5s模型大小下降52.2%,计算量下降51.8%,精度高达90.3%。改进质心跟踪的跌倒检测模型准确率提高了4.3%。结果表明了本文提出模型的有效性和优越性。