The impact of spatial scale on local Moran's I clustering of annual fishing effort for Dosidicus gigas offshore Peru

The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

PDBSCAN: Parallel DBSCAN for Large-Scale Clustering Applications

Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.

A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine(SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

The Scale Benefit Analysis of Sugar Clusters in Resource-rich Area of Southweatern Guangxi——A Case of Chongzuo City

Based on the scope economic theory of "resource curse" and industrial clusters,the scale of sugar cluster is calculated by the output of sugarcane and sugar while the scale benefit of sugar cluster is measured by the productivity(rate of sugar production),sales output ratio,industrial output value as well as profit margin.Positive analysis of the scale merit of sugar clusters in resource-rich area of southwestern Guangxi is conducted according to related statistics of Chongzuo City.And the primary problems of sugar clusters are pointed out.The profit created by sugar for the sugar industry in Chongzuo City has already been near capacity.The sugar industry is big but not strong.With much governmental interfernce,there is no effective connections and inadequte competitive forces among subjects of the clusters.The extention of industrial chain is limited.Therefore,measures for developing sugar clusters in resource-rich area of southwestern Guangxi are proposed.Industrial structure is to be adjusted to improve the sugarcane cultivation techniques.The industrial chain should be extended to increase the economic benefits of sugar industry.Industrial support is to be strengthened and capital output for sicence and technology increased.Price regualtion fund of grain sugar is to be established with coordination with the superior region.The transformation from savings to investment should be quickened to evade "resource curse".

FUZZY CLUSTER ANALYSIS OF TURBULENT SCALES

Turbulent motion could be regarded as the superposition of fluctuations with different scales. It's of great theoretical and practical importance to determine the classification of turbulent scales quantitatively to the better description of vortex motions with different scales, and to the research of the interaction among different sclaes of vortex and the construction of better turbulent models. The mathematical method, which carries out the classification on a certain requirement, is called cluster analysis. In this paper, fuzzy cluster analysis method is used to study the classification of turbulent scales quantitatively in smooth and rough wall boundary conditions. Furthermore, the properties and interactions among all kinds of flow structures are also studied. The results are helpful to gain some insight into the properties and interactions of all kinds of turbulent scales in wall turbulent shear flow.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

基于改进YOLOv网络的外观检测研究

外观检测涉及对图像或视频中的物体进行准确和高效的识别和定位,为了解决物体表面小尺寸目标检测的问题,研究通过优化YOLOv3网络模型,引入多尺度检测和深度可分离卷积技术来提高检测精度和模型效率,以增强对小尺寸目标的识别能力,再采用深度可分离卷积技术来减少计算量,并提高模型的训练效果;实验结果表明,研究模型在物体表面小尺寸检测方面取得显著提升;与其他金属表面损伤检测算法相比,优化后的YOLOv3实现了71.52%的检测精度,超越Faster R-CNN 6.83%;尽管Faster R-CNN在准确性方面优异但速度慢,SSD速度较快但不及YOLOv2;而YOLOv2虽速度快但精度稍低;相对于原始模型,研究算法的平均精度提升了7.77个百分点,达到了79.21%;虽然网络深度的提升稍增计算量,略有检测速率下降,但引入深度可分离卷积后,检测速度达到36.2帧/秒,仅较原模型稍低2.4帧/秒;研究可以优化算法,提高小尺寸目标检测的准确性和鲁棒性,推动其在计算机视觉领域的广泛应用。

国内智慧教育领域研究热点与趋势

近年来,智慧教育的兴起引起人们的广泛关注。本文以2013—2022年CSSCI数据库中关于智慧教育的572篇文献的高频关键词为原始数据,运用Bibexcel、Ucinet和SPSS软件对高频关键词数据进行共词分析、社会网络分析、多维尺度分析和聚类分析,揭示智慧教育领域的研究热点和发展趋势。研究结果显示智慧教育研究的热点主要体现在:教育信息化、教育现代化等政策、战略;智慧教育的内涵、特征、功能等;智慧教育的体系架构与智慧环境建设等;智慧教育的技术支撑及实践。本文还基于智慧教育研究现状,提出未来发展需要关注的四个方向。

社区尺度斜入射波场下场地-建筑群耦合作用模拟方法

场地-城市相互作用(site-city interaction,SCI)效应会显著改变场地地震波场分布及建筑反应,基于SCI效应理论计算研究方法的发展现状,发挥谱元(spectral element,SE)法可快速高效求解三维地震波场传播和多自由度(multi-degree of freedom,MDOF)模型计算量小且可同时模拟大量建筑的优势,同时,结合频率波数域(frequency wave number analysis,FK)方法,以等效地震荷载的方式施加地震波场,建立了FK-SE-MDOF耦合方法,实现了SE-MDOF耦合模型中多种波型(P波、SV波和SH波)的斜入射输入,解决了当前三维SCI效应研究方法中未能同时考虑建筑非线性、频谱特性、地震波波型及入射角度影响的问题。首先对方法原理进行了介绍;然后,通过与振动台试验的对比,验证了方法的正确性;进而,采用该方法建立理想场地-城市建筑群相互作用耦合模型,主要探讨了入射角度和地震波波型对SCI效应的影响,得到了一些有益结论。该方法较为真实地反映SCI效应影响的同时,可反映建筑基础轮廓对地震波场的影响,适用于需考虑建筑轮廓信息的社区尺度SCI效应研究,可为城市规划、抗震设计、风险评估以及震后救援等工作提供定量指导。

基于网络演化博弈的信息传播行为分析

演化博弈论将博弈理论与动态演化过程相结合,在演化的过程中追求动态平衡,该方法较好地实现了对在线社交网络信息传播演化趋势的分析。为了进行在线社交网络中的信息传播演化动力学研究,同时分析到达某一均衡状态的原因和方式,首先结合在线社交网络特性构建改进聚集的无标度网络,用户间的交互采用单参数囚徒困境博弈模型,并采用费米规则更新策略。其次,通过数据仿真对影响网络中合作者比例演化的因素进行分析,得到网络聚集性、博弈收益权重会促进网络中的合作行为,而背叛诱惑与噪声因子则对其有抑制作用的结论。最后,结合新浪微博的真实舆论传播数据,对现实网络进行信息传播分析。研究表明,合作者比例是由博弈动力学和网络拓扑结构共同作用的结果,需采取适当的激励机制,以解决在线社交网络中信息传播出现的合作困境。

基于多维标度和聚类分析的“物质结构和性质”模块情境素材分类研究

不同类别情境素材具有不同的教学功能.在“物质结构与性质”模块教学中,合理选用情境素材能促进学生理解与辨析、逻辑与推理等能力的培养.为系统探察“物质结构与性质”模块情境素材的类别,以课程标准和人教版选择性必修2为源,通过文本研究法和问卷调查法筛选出35个情境素材.以中学化学一线教师以及某师范大学本科生、研究生为研究对象,通过系统聚类法确定情境素材的类别.研究发现,“物质结构和性质”模块情境素材分为社会生活、学术探究、科技前沿、模拟联想、科学史实、实验探索6类.研究可为学者和一线教师高效开展情境教学研究与实践提供参考.

大型地埋管群地源热泵三维传热-渗流耦合模拟

为确定地下水渗流对大尺度、多分支埋管集群地源热泵系统换热区温度场和换热特性的影响,基于上海天文馆880个地埋管地源热泵场地,建立考虑地下水渗流和地质分层的三维传热-渗流耦合模型,并利用现场热响应试验对模型进行验证。结果表明:渗流作用引起管群内和支管群之间沿地下水流动方向发生温度干扰,89.5~120.3 m埋深范围内的温度干扰现象相对明显;地下水渗流有利于提高管群的换热总量,缩小夏季和冬季工况的换热量差距;换热量增加百分比随水力梯度的增加而增大,两者呈对数函数关系,当水力梯度大于0.005时,换热量增加百分比趋于平稳。

基于改进Yolov5s的水稻叶病检测方法

水稻叶病防治在提高水稻产量中具有重要作用,针对水稻叶病人工检查速度慢、主观性高的问题,提出一种基于改进Yolov5s的水稻叶病目标检测方法。采用K-means聚类算法得到先验框尺寸,增强检测模型对水稻叶病的适应性;将轻量级空间注意力与通道注意力融合,对高层语义特征信息增强,增强模型对病害信息的感知度;并结合特征金字塔网络,融合多尺度感受野获取目标上下文信息,有效地增强模型对目标周围特征的提取,提高目标检测的准确度。试验结果表明:改进后的Yolov5s算法平均检测精度(IOU=0.5)提高4.3%,F1值提高5.3%,帧率FPS为58.7 f/s。有效提升Yolov5s算法对水稻叶病的检测精度,达到实时检测的需求。

保持主导振荡模态的双馈风电场动态聚合等值方法

针对风电场详细建模出现的“维数灾”问题,计及双馈风电机组各控制环节与系统振荡模态的关系,以保持等值前后主导振荡模态一致为目标,提出考虑风电机组多时间尺度级联特性的风电场站等值聚合方法。首先考虑风电机组多控制环节级联建立风电场状态空间模型;其次,利用振荡能量级判别系统主导振荡模态,并基于参与因子分析不同模态的关键影响因素;在此基础上,以主导振荡模态的关键影响因素为分群指标,结合自编码器进行数据降维处理,经改进聚合算法聚类和拓扑变换获取等值参数等环节后得到等值模型。最后,在Matlab平台分别搭建单机模型和含24台双馈风电机组的风电场模型,验证了所提等值方法的有效性。

基于三阶张量的大规模数据谱聚类集成算法

为了降低大规模数据谱聚类计算负担,进一步提高聚类的准确性和鲁棒性,提出了一种基于三阶张量的大规模数据谱聚类集成算法。首先,提出一种混合代表最近邻近似方法构造数据间的稀疏亲和子矩阵;然后将稀疏亲和子矩阵表示为二部图,通过图分割的方法得到初步聚类结果;最后,提出三阶张量集成方法,将多个聚类结果进行融合,得到最终的聚类结果。在大规模的真实数据集和合成数据集上验证,相较经典的谱聚类算法、聚类集成算法以及近年来对其改进的算法,该算法表现出更优异的性能。

基于加权锚点的多视图聚类算法

大规模多视图聚类旨在解决传统多视图聚类算法中计算速度慢、空间复杂度高,以致无法扩展到大规模数据的问题.其中,基于锚点的多视图聚类方法通过使用整体数据集合的锚点集构建后者对于前者的重构矩阵,利用重构矩阵进行聚类,有效地降低了算法的时间和空间复杂度.然而,现有的方法忽视了锚点之间的差异,均等地看待所有锚点,导致聚类结果受到低质量锚点的限制.为定位更具有判别性的锚点,加强高质量锚点对聚类的影响,提出一种基于加权锚点的大规模多视图聚类算法(Multi-view clustering with weighted anchors,MVC-WA).通过引入自适应锚点加权机制,所提方法在统一框架下确定锚点的权重,进行锚图的构建.同时,为增加锚点的多样性,根据锚点之间的相似度进一步调整锚点的权重.在9个基准数据集上与现有最先进的大规模多视图聚类算法的对比实验结果验证了所提方法的高效性与有效性.

脑卒中症状群评估量表的编制及信效度检验

目的编制脑卒中患者症状群评估量表,进行信效度检验,为精准护理提供评估工具。方法以症状管理理论为框架,通过文献回顾、德尔菲法、预调查形成量表初稿后,选取353例脑卒中患者进行调查,采用项目分析、探索性因子分析、信度分析筛选条目、检验信效度。结果编制的脑卒中症状群评估量表包括躯体活动、认知言语、感知觉、吞咽、消化排泄、情绪心理6个症状群共35个条目,累积方差贡献率为69.095%。量表总Cronbach’sα系数为0.905,折半信度为0.728。量表水平的内容效度指数为0.886,条目水平的内容效度指数为0.920~1.000。结论脑卒中患者症状群评估量表具有良好的信效度,可用于脑卒中患者症状群的评估。

基于可变尺度先验框的声呐图像目标检测

利用深度学习对声呐图像进行目标检测是近年来的研究热点,然而声呐图像存在目标尺度分布集中、数据获取难等问题,导致检测效果难以满足需求。针对该问题,提出了一种基于可变尺度先验框的目标检测方法。首先,考虑到声呐图像中目标的尺度分布具有其特殊性,基于先验统计生成可变尺度先验框。其次,为了解决声呐图像稀缺的难题,采用数据增强的方法对训练集进行扩充。最后,探索了模型的轻量化,通过删减模型的大目标检测层,在不降低模型精度的同时简化模型结构。为了评估算法的有效性,以前视声呐图像为例进行了综合试验,平均精度(mean average precision,mAP)@0.75和mAP@0.5:0.95分别达0.585和0.559,较原Yolov5网络分别提升了5.8%和3.1%,同时每秒10亿次浮点运算次数下降到14.9。结果表明,所提算法具有更高的精度和更轻量化的模型结构。

多尺度视角下广东臭氧时空分异特征及驱动因素

鉴于近地面臭氧(O_(3))浓度具有多尺度和复杂非线性的时空变化特征,本研究基于2015~2022年广东地区O_(3)监测数据和同期气象资料,利用围绕中心点分割(PAM)聚类、Kolmogorov–Zurbenko(KZ)滤波和广义加性模型(GAM)等手段,探讨了不同区域多时间尺度O_(3)浓度的变化特征及驱动因素.结果表明:广东地区近地面O_(3)存在跨行政区划的区域性特征,通过对O_(3)数据聚类分析可以划分出8个子区域;O_(3)浓度时间序列经尺度分离后,短期分量对总方差贡献最大,其次为季节分量;气象因子与O_(3)浓度变化的关系受到时间尺度和区域差异的影响;经气象调整后的O_(3)峰值浓度序列大多在2016年或者2017年后呈现下降的态势,珠三角地区是O_(3)污染的突出区域,由不利气象条件和人为排放共同引起;O_(3)浓度时间序列的长期分量大多呈现波动上升的趋势,人为排放虽然是O_(3)浓度的主要贡献,但气象条件才是驱动O_(3)浓度变化的主导因素.本研究结果进一步提示O_(3)污染防控工作需要因时因地制宜,建立基于气象状况变化的前体物减排预案对O_(3)污染防控具有重要意义.