基于邻域曲率的低特征辨识度点云配准方法

在对特征辨识度低的点云进行配准的过程中,传统的基于局部特征提取和匹配的方法通常精度不高,而基于全局特征匹配的方法精度和效率也难以保证。针对这一问题,提出一种改进的局部特征配准方法。在初步配准阶段,设计了一种基于法向量投影协方差分析的关键点提取方法,结合快速特征直方图(FPFH)对关键点进行特征描述,定义多重匹配条件对特征点进行筛选,最后将对应点的最近距离之和作为优化目标进行粗匹配;在精配准阶段,采用以点到平面的最小距离作为迭代优化对象的改进迭代最近点(ICP)算法进行精确配准。实验结果表明,在配准特征辨识度低的点云时,相较于其他三种配准方法,该方法能保持高配准精度的同时降低配准时间。

基于可辨识矩阵的完全自适应2D特征选择算法

针对基于信息增益与皮尔森相关系数的特征选择算法FSIP(feature selection based on information gain and Pearson correlation coefficient)存在的特征子集选取需要人工参与的问题,提出基于可辨识矩阵的完全自适应2D特征选择算法DFSIP(discernibility based FSIP).DFSIP算法完全自适应地发现特征子集,每次选择当前特征中最重要的一个特征,并以此特征约简可辨识矩阵,剔除冗余特征,最终自适应地获得最优特征子集.依据最优特征子集构建K-ELM分类器来评价最优特征子集的类别辨识能力.在基因数据集的实验测试以及与FSIP,mRMR,LLE Score,DRJMIM,AVC,AMID算法的实验比较和统计重要性检测表明:DFSIP算法能够自动选择出辨识能力更强的特征子集,基于此特征子集的分类器具有很好的分类性能.

基于知识图谱的电力信息安全威胁可视化技术

传统电力信息安全威胁分析方法无法获取威胁数据的关联特征量,导致电力信息安全威胁的可视化效果较差。为此,提出基于知识图谱的电力信息安全威胁可视化技术。采用二值化图谱特征挖掘方法提取威胁数据关联特征量,通过模糊度检测和信息融合识别分析安全威胁高阶频谱特征量;利用稀疏散点图分析法加载三维可视化知识图谱,结合小波域特征分解方法实现电力信息安全威胁可视化。仿真结果表明,所提技术的图谱量化分析能力较高,电力威胁信息特征辨识度水平较好。

Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

基于SDN技术的校园网易被攻击区域识别

为了提高校园网的安全性,提出基于SDN技术的校园网易被攻击区域识别方法。首先构建校园网的网格区域分布模型,采用特征盲分离技术,实现对校园网入侵信号特征的监测。采用无线节点分布式融合的方法,构建校园网易被攻击区域的网格分块融合结构模型,通过模糊参数辨识和SDN技术,实现对攻击区域的有效识别。仿真结果表明,采用该方法识别结果的准确性较高,且特征辨识度较高,有效提高了校园网的抗攻击能力和安全性。