基于CiteSpace可视化分析的高校智慧教育空间研究进展与启示

存量时代下,高校教育空间随数字化教学模式的改变而逐步迭代升级,随之而来的校园教育空间智慧化建设也为教育设施的效能提升、情境预测提供了新的方法与工具。本文运用CiteSpace可视化工具,呈现近十年来国内外高校智慧教育空间的研究热点,总结其研究现状与发展趋势,归纳出高校智慧校园建设的四个阶段及智慧教育空间所呈现的低碳化、人本化、多元化、复合化的阶段特征,以期为后续校园教育空间的更新与建设提供参考。

Metamodel-based Global Optimization Using Fuzzy Clustering for Design Space Reduction

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.

Detection of Residual Yarn in Bobbin Based on Odd Partial Gabor Filter and Multi-Color Space Hierarchical Clustering

In an automatic bobbin management system that simultaneously detects bobbin color and residual yarn,a composite texture segmentation and recognition operation based on an odd partial Gabor filter and multi-color space hierarchical clustering are proposed.Firstly,the parameter-optimized odd partial Gabor filter is used to distinguish bobbin and yarn texture,to explore Garbor parameters for yarn bobbins,and to accurately discriminate frequency characteristics of yarns and texture.Secondly,multi-color clustering segmentation using color spaces such as red,green,blue(RGB)and CIELUV(LUV)solves the problems of over-segmentation and segmentation errors,which are caused by the difficulty of accurately representing the complex and variable color information of yarns in a single-color space and the low contrast between the target and background.Finally,the segmented bobbin is combined with the odd partial Gabor’s edge recognition operator to further distinguish bobbin texture from yarn texture and locate the position and size of the residual yarn.Experimental results show that the method is robust in identifying complex texture,damaged and dyed bobbins,and multi-color yarns.Residual yarn identification can distinguish texture features and residual yarns well and it can be transferred to the detection and differentiation of complex texture,which is significantly better than traditional methods.

SLC Method and Earthquakes' Clustering Features in Time-Space in the Top Area of the Kunlun-Altun-Arc

By using the SLC(Single-Link Cluster)method,this study worked in three respects:(a)set up three-dimensional(3-D)SLC software that can deal with a large catalogue of earthquakes and analyze the characteristics of earthquakes’ clustering and scattering in time-space:(b)defined several parameters to describe the distinguishing feature for the SLC frame and developed a technique to calculate the 3-D SLC frames and these parameters with gradual time-sliding,and inspected their variations with time,especially before large events; and(c)by using these means,treated the earthquake catalogue in the top area of the Kunlun-Altun-Arc as well as some valuable results that had been obtained.

基于Cite Space Ⅴ的煤矿安全管理可视化分析

为总结全球范围内煤矿安全管理的研究现状并预测其未来发展趋势,借助Cite Space V软件,对1992--2017年期间Web of Science(wos)收录的“煤矿”“安全管理”领域的相关文献进行可视化知识图谱分析。通过对该领域主要研究机构及国家、作者等方面进行可视化分析,描述该领域的主要研究力量及研究人员的分布;通过对关键词、研究聚类等进行知识图谱可视化分析,描述该领域的研究热点及未来发展趋势。结果表明:该领域的研究主要集中于中国、美国,且主要研究机构大多为各国高校,作者间合作以小范围团队合作为主;研究主要集中于安全教育、人因失误、数据挖掘以及职业病防治等方面;数据挖掘是该领域未来研究发展趋势之一。

基于CiteSpace的我国民族地区聚落研究现状分析与展望(1991—2020年)

民族地区聚落是反映各民族文化及其特征的重要物质载体,受城镇化的影响及国家和地方政策的推动,相关研究与实践面临着新的机遇和挑战。为更好地开展后续工作,需理清前期研究的成果及发展方向。选取近三十年间中国知网数据库中相关文献859篇,综合运用CiteSpace及Python进行数据处理及分析,从而判断当时研究的发展态势、热点及分布特征,并将相关研究归为空间特征、景观环境、保护发展三个方向分别展开详细论述。由此发现,既往研究存在系统性较弱、主观性较强等问题,未来可尝试通过建立聚落数据库、融入新技术等方式展开多学科视野下的多尺度融合研究。

Cluster Analysis on the Nucleotide Sequences of Six Genes in Rice

[ Objective] This study aimed to construct four-dimensional graphics of nucleotide sequences of six genes in rice （ GluB-6, GtuB-7, PDIL2, OsMPK1, OsCATC, OsCATA） and to conduct phase-space clustering, thus demonstrating the relationship between the structure and function of rice genes. [ Method ] Base sequences were represented by four-dimensional graphics and clustered in the phase space. The relationship between clustering results and biological characteristics of these genes were analyzed. [ Result] Genes with similar four-dimensional graphics exhibit similar biological characteristics. [ Conclusion] Four-dimensional graphics of genes with different functions and base lengths present phase-space relationship with their biological functions, which provided an effective way for the prediction of gene function.

Control of the interparticle spacing in superparamagnetic iron oxide nanoparticle clusters by surface ligand engineering

Polymer-mediated self-assembly of superparamagnetic iron oxide（SPIO） nanoparticles allows modulation of the structure of SPIO nanocrystal cluster and their magnetic properties. In this study, dopamine-functionalized polyesters（DApolyester） were used to directly control the magnetic nanoparticle spacing and its effect on magnetic resonance relaxation properties of these clusters was investigated. Monodisperse SPIO nanocrystals with different surface coating materials（poly（ε-caprolactone）, poly（lactic acid）） of different molecular weights containing dopamine（DA） structure（DA-PCL2k,DA-PCL1k, DA-PLA1k）） were prepared via ligand exchange reaction, and these nanocrystals were encapsulated inside amphiphilic polymer micelles to modulate the SPIO nanocrystal interparticle spacing. Small-angle x-ray scattering（SAXS）was applied to quantify the interparticle spacing of SPIO clusters. The results demonstrated that the tailored magnetic nanoparticle clusters featured controllable interparticle spacing providing directly by the different surface coating of SPIO nanocrystals. Systematic modulation of SPIO nanocrystal interparticle spacing can regulate the saturation magnetization（Ms） and T2 relaxation of the aggregation, and lead to increased magnetic resonance（MR） relaxation properties with decreased interparticle spacing.

Determination of the Proper Motions and Membership of the Globular Cluster M3 and of its Orbit in the Galaxy

We examine 14 plates of the globular cluster M3 (NGC 5272) taken with the 40 cm refractor at the Sheshan station of Shanghai Astronomical Observatory. The plates span over a period of about 77 years. The positions and absolute proper motions of eight stars in the Hipparcos Catalogue and of 49 stars in the Tycho-2 Catalogue are used as the reference frame. The astrometric reduction is made with the central overlapping principle. The absolute proper motions of 534 stars in a region of about 100' × 100' around the cluster are measured. With the new proper motion data the membership probabilities of the stars are determined. The average absolute proper motion obtained for the cluster is -0.06±0.30 mas yr-1 in R.A. and -2.6±0.30 mas yr-1 in Decl. By combining this result with the known distance and radial velocity of the cluster, we also obtained the Galactic orbit of M3 for a chosen three-component Galactic potential.

我国产业集群创新网络研究热点及前沿——基于CiteSpace的可视化分析

近年来,产业集群已发展成为我国区域经济发展的重要产业组织模式,为研究产业集群创新网络领域的研究现状、热点以及趋势,文中基于2000-2021年中国知网上以“产业集群创新网络”或“集群创新网络”为主题的1268篇文献,使用Cite Space绘制知识图谱对发文情况及文献数据进行可视化分析,梳理了当前的研究现状、热点主题及研究前沿趋势。研究结果表明,国内产业集群创新网络研究发文量呈波动上升趋势,且已形成网络演化典型模式、网络结构特征、网络内部作用机制、创新网络治理4大研究主题。未来研究前沿趋势包括协同创新网络的搭建、运营、治理与双重网络嵌入研究。

Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors

Earthquakes exhibit clear clustering on the earth. It is important to explore the spatial-temporal characteristics of seismicity clusters and their spatial heterogeneity. We analyze effects of plate space, tectonic style, and their interaction on characteristic of cluster.Based on data of earthquakes not less than moment magnitude(M_w) 5.6 from 1960 to 2014, this study used the spatial-temporal scan method to identify earthquake clusters. The results indicate that seismic spatial-temporal clusters can be classified into two types based on duration: persistent clusters and burst clusters. Finally, we analysed the spatial heterogeneity of the two types. The main conclusions are as follows: 1) Ninety percent of the persistent clusters last for 22-38 yr and show a high clustering likelihood;ninety percent of the burst clusters last for 1-1.78 yr and show a high relative risk. 2) The persistent clusters are mainly distributed in interplate zones, especially along the western margin of the Pacific Ocean. The burst clusters are distributed in both intraplate and interplate zones, slightly concentrated in the India-Eurasia interaction zone. 3) For the persistent type, plate interaction plays an important role in the distribution of the clusters’ likelihood and relative risk. In addition, the tectonic style further enhances the spatial heterogeneity. 4) For the burst type,neither plate activity nor tectonic style has an obvious effect on the distribution of the clusters’ likelihood and relative risk. Nevertheless,interaction between these two spatial factors enhances the spatial heterogeneity, especially in terms of relative risk.

The Uniform Convergence Topology On Multi-valued Mapping Space

Some convergences of topology are discussed. The definitions of almost uniform convergence topology and compatible topology are given. It is shown that the quasiuniform convergence and generalized uniform convergence have no compatible topology,but the almost uniform convergence has compatible topology. Moreover, the description of all uniform convergence limits and their mutual relation are investigated[1].

基于软聚类的深度图增强方法

针对现有的深度获取方式存在数据缺失、分辨率低等问题,提出一种基于软聚类的深度图增强方法,称为软聚类求解器.该方法利用软聚类的强边缘保持特性提高深度图增强的精度.将软聚类仿射矩阵和加权最小二乘模型有机结合,构建了软聚类求解器中的置信加权最小二乘模型,提出了基于迭代的求解方法.为评估所提出的方法,在多项深度图增强任务上进行试验,包括深度图补洞、深度图超分辨率和深度图纠正,评价指标包含了峰值信噪比(PSNR)、结构相似度(SSIM)、均方根差(RMSE)和运行效率.结果表明:文中方法在深度图补洞任务中的平均PSNR达到了42.28,平均SSIM达到了98.83%;在深度图超分辨率、深度图纠正任务中的平均RMSE达到了8.96、 2.36.文中方法处理1张分辨率为2 048×1 024像素的图像仅需5.03 s.

结合聚类边界采样的主动学习

主动学习是一种机器学习方法,需要选择最有价值的样本进行标注。目前,主动学习在应用时面临着一些挑战,其依赖分类器的先验假设,这容易导致分类器性能意外下降,同时需要一定规模的样本作为启动条件。聚类可以降低问题规模,是主动学习的一种有效手段。为此,结合密度聚类边界采样,开展主动学习方法的研究。针对容易产生分类错误的聚类边界区域,通过计算样本密度,提出一种密度峰值聚类边界点采样方法;在此基础上,给出密度熵的定义,并利用密度熵对聚类边界区域进行启发式搜索,提出一种基于聚类边界采样的主动学习方法。试验结果表明,与文献中的5种主动学习算法相比,该算法能够以更少标记量获得同等甚至更高的分类性能,是一种有效的主动学习算法;在标记不足,无标签样本总量20%的情况下,算法在Accuracy、F-score等指标上取得较好的结果。

交通规划导向的科技赋能交通小区划分方法

文章以手机信令、路网大数据为基础,提出了针对交通规划的科技赋能交通小区划分方法,以路网数据划分聚类单元,利用手机信令数据准确识别聚类单元内居民出行时空分布特征,基于改进耦合空间聚类算法进行聚类,借助现状路网对聚类结果进行修正得到交通小区划分结果。宁波市实例研究表明,科技赋能交通小区划分方法能兼顾居民出行时空特征和城市路网匹配,并能较好保证交通小区的整体连续性,该方法适用性强,可为交通规划的科学制定提供借鉴。

Bi元素的添加对Al-Cu-Si三元共晶合金双峰结构转变的影响

为了揭示Bi元素对共晶合金中凝固组织的影响和作用机理,使用重力凝固法制备了Al-Cu-Si-xBi(x=0%、0.01%、0.03%、0.05%、0.07%)三元共晶合金,分析了不同添加量Bi对Al-Cu-Si三元共晶合金凝固的组织特征、相的生长形态特征及相组成等的影响。结果表明:Al-Cu-Si三元共晶合金具有α-Al/θ-Al_(2)Cu二元共晶和α-Al/θ-Al_(2)Cu/β-Si三元共晶的混合双峰共晶结构。未添加Bi时,α-Al/θ-Al_(2)Cu二元共晶呈现典型的辐射状菊花集群形态,α-Al/θ-Al_(2)Cu/β-Si三元共晶中的Si相呈现长针状形态,双峰共晶结构较不均匀。随着Bi的添加,α-Al/θ-Al_(2)Cu二元共晶呈现均匀细小的短片层结构,Si相变为点针状。双峰共晶结构的层片间距的差值也逐渐缩小,当Bi的添加量为0.05%时,变质效果最好,双峰共晶结构最为均匀细小,合金硬度亦最好。

PC Cluster系统无法运行JSS作业的原因分析及解决方法

本文在简要介绍PC Cluster系统组成的基础上,着重分析了系统不能运行JSS作业的原因——inode被占满了而引起的,并介绍了通过删除mailman软件包排除故障的方法。

基于K-means聚类和特征空间增强的噪声标签深度学习算法

深度学习中神经网络的性能依赖于高质量的样本,然而噪声标签会降低网络的分类准确率。为降低噪声标签对网络性能的影响,噪声标签学习算法被提出。该算法首先将训练样本集划分成干净样本集和噪声样本集,然后使用半监督学习算法对噪声样本集赋予伪标签。然而,错误的伪标签以及训练样本数量不足的问题仍然限制着噪声标签学习算法性能的提升。为解决上述问题,提出基于K-means聚类和特征空间增强的噪声标签深度学习算法。首先,该算法利用K-means聚类算法对干净样本集进行标签聚类,并根据噪声样本集与聚类中心的距离大小筛选出难以分类的噪声样本,以提高训练样本的质量;其次,使用mixup算法扩充干净样本集和噪声样本集,以增加训练样本的数量;最后,采用特征空间增强算法抑制mixup算法新生成的噪声样本,从而提高网络的分类准确率。并在CIFAR10、CIFAR100、MNIST和ANIMAL-10共4个数据集上试验验证了该算法的有效性。

基于色彩聚类的老年病房色彩设计研究

目的基于感性工学理论,以K-Means色彩聚类为基础获取老年病房患者的色彩感性意象评价。方法首先搜集并筛选现代医院老年病房设计案例,通过K-Means聚类方法进行色彩提取以建立老年病房色彩样本库;其次运用语义差异法量化老年病房色彩的感性意象认知,再将所得评价结果运用SPSS软件进行因子分析;最后,提取影响色彩意象评价的主因子,并探讨各感性意象与色彩样本之间的对应关系。结果通过量化老年病房患者的色彩感性意象认知,可知老年患者更倾向于选择中高明度、暖色调的色彩作为病房空间的主要色彩。结论该流程能获取科学、客观的色彩搭配方案,并有助于缓解老年患者的疾病压力,促进康复过程,同时也为医疗环境设计领域提供了新的思路和方法。

基于改进松弛嵌入空间的多视图聚类

针对传统聚类方法缺乏统一特征表示,存在保守性的缺陷,提出一种基于改进松弛嵌入空间的多视图聚类方法。在统一的框架下联合学习一个综合的潜在嵌入表示矩阵、全局相似矩阵和一个精确指标矩阵。进一步放松全局相似矩阵的约束,并在此基础上提出一种改进的松弛多视图聚类嵌入空间,使得该方法具有更低的计算复杂度和更多的数据点对之间的相关性。实验结果表明,该方法能够获得鲁棒性更强、准确度更高的聚类结果。