Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

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。

考虑综合相似性度量的光伏典型出力场景聚类方法

场景聚类是描述不确定性光伏典型出力特性的有效途径之一,如何度量波动繁杂的光伏发电曲线相似性以及生成具有代表性的光伏出力场景是目前亟需解决的问题。为此,提出一种考虑综合相似性度量的光伏典型出力场景聚类方法,其基本思路是首先考虑光伏发电的电量大小、形态趋势、波动位置相似性,得到适用于光伏发电曲线的综合相似性度量距离;其次将形态质心作为优化问题求解,再用同倍比放大法得到兼顾电量和形态的实际质心,针对传统聚类算法在初始中心确定等方面的不足,以二十四节气为区间提出基于改进K-means算法的光伏典型场景集生成模型;最后构建光伏发电场景集指标评价体系,以熵权Topsis法对典型出力场景集进行综合评价。云南某地装机50MW的光伏电站2018—2020年算例结果表明:该文算法能准确划分和提取典型光伏出力场景,且以节气为区间生成的典型场景集在波动和电量指标上都有较好的表现,证明算法的有效性。

基于RSSI的修正补偿三边定位算法设计与仿真

在无线传感器网络节点定位过程中,三边定位算法常结合测距算法获取未知节点的位置信息。针对现有三边定位算法定位精度低、适用范围小和需要多次发送定位数据包导致节点能耗增大等问题,提出一种修正补偿测量距离的三边定位算法。相比常见发送多次定位数据包然后进行滤波计算降低定位误差的方法,采用对数常态-模型测量节点间距离,发送较少定位数据包,通过判断三锚节点圆的相对位置,对由于测距误差较大导致节点圆不相交的3种情况进行修正补偿,采用基于质心算法的三边定位算法计算未知节点坐标。通过搭建Matlab仿真试验,验证算法可行性及定位精度,试验结果表明,提出的修正补偿三边定位算法综合考虑了定位精度、算法复杂度和节点功耗3者之间的关系,适用范围更广,定位误差<15%。

基于语义分割和融合算法的草坪杂草定位

【目的】探究基于多算法融合的草坪杂草精准定位算法,为草坪杂草的自动识别和清除机器人的除草作业提供技术支撑与理论依据。【方法】提出了一种基于语义分割的多算法融合的草坪定位算法。首先,通过PSPNet网络分割草坪和非草坪轮廓。其次,针对分割出来的非草坪轮廓提取感兴趣的区域,去除非杂草轮廓,保留杂草轮廓。然后,利用Zhang-Suen细化算法提取杂草轮廓骨架线,并获取骨架交叉点数量和坐标位置。最后,利用融合算法依据交叉点数量选择不同的定位策略,实现杂草根部的精准定位。【结果】融合算法定位的杂草坐标与真实杂草根部中心坐标的均方根误差为83.17像素,相比平均质心法减少了14%,相比最小外接圆减少了22%。换算到实际场景之下,融合算法定位的杂草坐标与真实杂草根部中心坐标的均方根误差为12.48 mm,误差在可接受的误差范围内。融合算法提高了杂草根部中心的定位精度,降低了杂草定位的误差。【结论】基于语义分割和融合算法的草坪杂草定位方法提高了杂草根部中心的定位精度,降低了单一方法的定位误差,可以为草坪杂草自动识别和除草作业提供技术支持。

基于信号强度差值的改进质心定位算法

质心算法用于室内定位时具有实现简单及综合开销小的特点,但定位精度及稳定性较差。为解决该问题,文章利用信号强度差值(RSSI)对质心算法进行改进。将参与定位的参考节点部署为形如等腰直角三角形的定位区域,并采用缩小定位区域的思想将定位区域按照三角形顶点垂直平分线均匀划分为4个定位子区域。在定位过程中,获取信号强度值最大3个参考节点并判断是否构成等腰直角三角形,若满足条件则通过计算参考节点三角形各顶点间信号强度差值以判断目标节点所处子区域,最后以所聚焦子区域的质心坐标作为最终定位结果。仿真结果表明,改进算法相比传统的质心定位算法及三边质心算法具有更高的定位精度和更好的稳定性,且因改进算法避免了使用路径损耗函数,减少了前期参考节点部署工作量。该改进算法是一种较为简单、实用,并且精度和稳定性较高的室内定位算法,适用于各种室内无线定位系统。

k-Means++算法下的无线局域传感网络凸包质心定位

为了提高无线局域传感网络凸包质心定位的准确性,利用k-Means++聚类算法计算传感器节点的接收信号强度和路径损耗,筛选出具有代表性和稳定性的节点作为凸包质心,再使用加权算法计算凸包质心定位权值,选择出更准确地质心。在此基础上,通过分析无线传感器信号的间隔时间以及比较周边凸包质心和未知节点的接收信号实际条件,确定凸包质心之间的连通性关系。根据设定的凸包质心定位判断阈值,进一步确定凸包质心的定位结果,提高定位的准确性和可靠性。仿真结果表明,所提方法最大定位误差为0.167 m,在不同凸包质心数量下通信半径为40 m时的定位误差小于0.35 m。证明了所提方法能有效地实现无线局域传感网络中的质心定位,定位误差小。

基于加权质心算法的无线传感网络丢包节点定位技术

针对无线传感网络中丢包节点的定位问题,提出一种基于加权质心算法的定位技术.分析无线传感网络节点分布和拓扑结构后,采用RSSI的测距模型改进加权质心算法,经测距模型将RSSI值转换成丢包节点与周围节点的距离,采用加权质心定位算法求解WSN丢包节点定位误差,以3个锚节点作为圆心,锚节点距离作为半径构建圆,计算3个圆交点的坐标后,求出由3个交点构建三角形的质心位置平均值,该值为WSN丢包节点定位坐标,实现无线传感网络丢包节点定位.实验结果表明:该方法应用效果较好,其平均转发率为98.18%,平均检测率为98.36%,能有效定位出WSN丢包节点,提高了WSN网络的稳定性,使得WSN数据传输更加可靠,保障了WSN数据传输质量.

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。

高维数据聚类数量可视化确定模式

为了解决经典K-均值聚类算法要求用户事先知道待处理数据的聚类数量及聚类结果对算法的初始化很敏感的问题,提出一种对K-均值聚类算法的改进措施并可视化地确定聚类数量的综合方案。首先,对数据进行标准化,使其服从正态分布,利用主分量分析(princi‐palcomponentanalysis,PCA)抽取数据中最重要的特征以实现高维数据的降维;然后,采用最远质心选择和最小-最大距离规则对K-均值聚类算法的初始化进行修正,避免出现空聚类并确保数据的可分离性;在此基础上,采用统计经验法则估计聚类数量的可能范围,通过搜索在此范围内平方误差和(sum-of-squared-error,SSE)曲线的肘部估计最佳的聚类数量;最后,通过计算比较各个聚类的轮廓系数以评价算法的聚类质量,从而最终确定数据集固有的聚类数量。仿真结果表明,该方案不仅能可视化地确定数据集潜在的聚类数量,而且为大数据时代的高维数据分析提供了一种有效的方法。

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.

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.

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.

Detection of fabric defects based on frequency-tuned salient algorithm

The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results.

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。