Geometry and Convergence Analysis of Algorithms for Registration of 3D Shapes

The computation of a rigid body transformation which optimally aligns a set of measurement points with a surface and related registration problems are studied from the viewpoint of geometry and optimization. We provide a convergence analysis for widely used registration algorithms such as ICP, using either closest points (Besl and McKay, 1992) or tangent planes at closest points (Chen and Medioni, 1991) and for a recently developed approach based on quadratic approximants of the squared distance function (Pottmann et al., 2004). ICP based on closest points exhibits local linear convergence only. Its counterpart which minimizes squared distances to the tangent planes at closest points is a Gauss–Newton iteration; it achieves local quadratic convergence for a zero residual problem and—if enhanced by regularization and step size control—comes close to quadratic convergence in many realistic scenarios. Quadratically convergent algorithms are based on the approach in (Pottmann et al., 2004). The theoretical results are supported by a number of experiments; there, we also compare the algorithms with respect to global convergence behavior, stability and running time.     

Distributed consensus tracking for multi‐agent systems under two types of attacks

This paper studies a distributed coordinated control problem for a class of linear multi‐agent systems subject to two types of attacks. The problem boils down to how to achieve secure consensus tracking for multi‐agent systems with connected and disconnected (paralyzed) directed switching topologies caused by two types of attacks. The attacks on the edges instead of nodes lead to the loss of security performance. Two cases are studied in this paper. First, under only a class of connectivity‐maintained attacks, sufficient conditions are derived to achieve secure consensus tracking in mean‐square. Second, when the multi‐agent systems are further subject to a class of connectivity‐broken attacks, novel sufficient conditions are further obtained to ensure secure consensus tracking with a specified convergence rate by virtue of the idea of average dwell time switching between some stable and unstable subsystems. Three numerical simulations are finally given to illustrate the theoretical analysis. Copyright © 2015 John Wiley & Sons, Ltd.     

基于椭圆曲线的多重盲签名方案

提出了两个基于椭圆曲线的多重盲签名方案,该方案可以同时完成盲签名和多重签名的任务而且签名尺寸不会随签名人数增加而增加。还对方案的正确性、盲性、不可伪造性作了证明。     

糖尿病活体胰腺体积测量的临床应用

应用ＮＡＩ－２００医用数字图象处理系统对２４例国人糖尿病的胰腺ＣＴ影象进行了前瞻性研究，首次得出了精确的活体胰腺体积为１２．７９±２．２８ｃｍ３／ｍ２．正常人为１６．４４±４．１４ｃｍ３／ｍ２．经ｔ检验后具有明显的差异性Ｐ＜０．０１．提示国人糖尿病对叶伴有明显的胰腺体积缩小。     

神经网络在机电零件制造成本预估系统的应用研究

由于激烈的市场竞争,企业必须建立完善的设计决策支持系统,针对这种需求,本文提出了一种解决机械零件在概念设计阶段的制造成本预估问题的系统方法。在数据库的支持下,首先建立合适的神经网络模型,用已有数据对其进行训练直至其收敛,从而可以进行预估计算。     

基于485总线的自动分诊系统

本文介绍一种以单片机为核心，基于485总线的自动分诊系统：它可为病人就医提供清晰的指引：并提供统计功能，能准确地对医师的工作量进行客观的统计；还提供医师与分诊员之间的联络(对讲功能)。其价格便宜，操作简便，易于上手。     

校园网DDoS攻击防御平台建设方案研究

分布式拒绝服务攻击(DDoS)已经成为互联网最大的威胁之一。分析了校园网现状,建立了业务模型,提出了流量清洗设备的功能要求,设计了一种校园网DDoS攻击防御平台的设计方案。并分析了DDOS攻击清洗方案的流量牵引技术、触发技术、流量清洗技术与流量回注技术。     

Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

Safety helmet-wearing detection is an essential part of the intelligent monitoring system. To improve the speed and accuracy of detection, especially small targets and occluded objects, it presents a novel and efficient detector model. The underlying core algorithm of this model adopts the YOLOv5 (You Only Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (Complete Intersection Over Union) Loss function, and the Mish activation function. First, it applies the attention mechanism in the feature extraction. The network can learn the weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accurate bounding box regression. Third, it utilizes Mish activation function to improve detection accuracy and generalization ability. It builds a safety helmet-wearing detection data set containing more than 10,000 images collected from the Internet for preprocessing. On the self-made helmet wearing test data set, the average accuracy of the helmet detection of the proposed algorithm is 96.7%, which is 1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.     

并行算法的FP描述及其脉动化的判定

本文通过引入流及流上的递归方程,增强了FP表达并行算法的能力,有效地克服了用FP描述循环数据依赖及状态记忆的困难,文中给出了并行算法的FP描述及其可脉动化的判定定理。同时说明,许多在图上研究的脉动方法可以方便地应用到用FP描述脉动化的研究中去。     

形变冗余的改进GHT目标双层定位方法研究

经典广义Hough变换可以较好地解决非形变目标定位问题,但对于存在形变的目标定位问题存在不少困难。为解决该问题,同时考虑如何提高检测定位速度与减少存储消耗, 在粗定位与精确定位两级框架下提出基于改进GHT形变目标两层定位快速算法。粗定位过程首先利用图像的局域二进制模式的直方图特征对图像进行全局搜索,检测出目标大致范围 ;在精确定位过程中,通过建立模板图像边缘像素的R表,使待检测图像边缘像素在约束的参数范围内依据该R表进行局部搜索,并通过一个投票结果散布窗对得到的累积矩阵进行 集中化处理,达到把每一点邻域内投票结果集中在某点的目的,从而给出最后的检测结果。实验表明,本文算法能够较好的解决一定程度形变目标的定位问题,同时减少了运算时 间以及存储消耗,检测稳定性高,具有一定应用意义。