Design of robotic visual servo control based on neural network and genetic algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.     

基于MQTT的企业消息推送系统

消息推送系统在实现企业办公自动化方面有着重要的作用,通信协议和系统架构的设计是企业消息推送系统性能优化的关键因素.传统的标准化协议方案在移动互联网领域的应用存在着实现复杂和流量消耗大等缺点.针对这一问题,结合企业消息推送系统的特点,采用物联网领域的轻量级协议MQTT来设计企业消息推送系统的通信协议,通过设计MQTT话题区分社交通知、即时消息和状态呈现等功能,在满足了应用需求的同时保证了协议的简单易实现和流量消耗小等适用于移动互联网的优势.另一方面,在传统的对称和非对称设计的服务器架构之上,提出了信息上非对称和功能上对称的系统架构,为企业消息推送系统的构建提供了一个详细的解决方案.该方案在满足系统设计需求的同时提高了系统的可扩展性.     

基于YOLOv5的交通标志识别

交通标志识别是自动驾驶技术中的关键一部分.针对交通标志在道路场景中目标较小且识别精度较低的问题,提出一种改进的YOLOv5算法.首先在YOLOv5模型中引入全局注意力机制(GAM),提高网络捕获不同尺度交通标志特征的能力;其次将YOLOv5算法中使用的GIoU损失函数更换为更具回归特性的CIoU损失函数来优化模型,提高对交通标志的识别精度.最后在Tsinghua-Tencent 100K数据集上进行训练,实验结果表明,改进后的YOLOv5算法对交通标志识别的平均精度均值为93.00%,相比于原算法提升了5.72%,具有更好的识别性能.     

Mechanical properties of concrete containing phase-change material

This study aimed to investigate the mechanical properties of concrete containing solid–liquid phase-change material (PCM) and focused on two key factors. First, a systematic study on the mechanical performance of PCM-modified concretes was conducted, including compressive, elastic modulus, and shrinkage tests. Second, because PCM provides high latent heat during the solid–liquid phase change, the effects of the solid phase and liquid phase on the mechanical properties of concrete were also explored. Results of this study showed that the solid–liquid phase of PCM affected the mechanical properties of concrete. For example, the compressive strength of 10% PCM concrete in solid phase (23 °C) and liquid phase (40 °C) at 28 days was 29.30 and 19.57 MPa, respectively. In addition, with increasing PCM content, the mechanical properties were degraded. For example, 10, 20, and 30% of PCM content lowered the compressive strength by 35.4, 58.4, and 74.3%, respectively. Therefore, concrete with PCM may not be suitable for structural elements. However, PCM is an important solution for optimizing energy consumption in modern buildings. It can absorb or emit large amounts of heat to store or release thermal energy. These properties can be used to control building temperatures resulting in energy saving and carbon reduction.     

Model Transduction for Triangle Meshes

This paper proposes a novel method, called model transduction, to directly transfer pose between different meshes, without the need of building the skeleton configurations for meshes. Different from previous retargetting methods, such as deformation transfer, model transduction does not require a reference source mesh to obtain the source deformation, thus effectively avoids unsatisfying results when the source and target have different reference poses. Moreover, we show other two applications of the model ...     

一种基于边界约束的流形展开方法

在流形学习的谱方法中, 流形展开被表述为优化问题. 这些优化问题的解是退化的, 即所有的样本将被嵌入到同一个点. 为了避免退化解, 谱方法对嵌入坐标人为地强加了一个单位协方差矩阵约束. 然而, 该约束往往导致流形展开的失真非常明显. 本文提出一种新的流形学习方法, 彻底抛弃了人为的单位协方差矩阵约束. 主要思路是先对流形边界进行嵌入, 然后再求流形内部的嵌入; 流形边界的嵌入位置被确定后, 流形内部样本的嵌入位置将被边界拉开, 使得它们不会都收缩到一个点上, 从而避免了退化解的出现. 将流形边界的嵌入位置作为边界条件, 求解一个线性方程组来得到内部样本的嵌入; 该线性方程组反映了尽量保持邻近样本间距离不变的要求. 流形边界的嵌入由简化流形的嵌入求出; 为此, 本文还设计了一种流形边界检测算法以及一种流形简化算法. 与目前代表性的几种流形学习方法进行了比较实验, 结果表明了本文方法的有效性, 其展开失真比谱方法明显要小.     

基于身份自证实的秘密共享方案

为了解决现有秘密共享方案中秘密份额的安全分发问题,基于Girault密钥交换协议,结合基于身份(ID)的公钥密码技术提出了一个新的秘密共享方案,并对其进行了安全性和性能分析.该方案中,用户的私钥作为其秘密份额,无须秘密分发者为每个用户分发秘密份额.用户的私钥可以由用户自己选取,可信第三方无法获取其私钥.同时,任何人都可以以离线方式验证每一个参与者公钥的合法性.分析表明,文中所提出的基于身份的秘密共享方案具有更高的安全性和有效性,能更好地满足应用需求.     

Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.     

Nonlinear Dimensionality Reduction by Local Orthogonality Preserving Alignment

We present a new manifold learning algorithm called Local Orthogonality Preserving Alignment (LOPA). Our algorithm is inspired by the Local Tangent Space Alignment (LTSA) method that aims to align multiple local neighborhoods into a global coordinate system using affine transformations. However, LTSA often fails to preserve original geometric quantities such as distances and angles. Although an iterative alignment procedure for preserving orthogonality was suggested by the authors of LTSA, neither the corresponding initialization nor the experiments were given. Procrustes Subspaces Alignment (PSA) implements the orthogonality preserving idea by estimating each rotation transformation separately with simulated annealing. However, the optimization in PSA is complicated and multiple separated local rotations may produce globally contradictive results. To address these difficulties, we first use the pseudo-inverse trick of LTSA to represent each local orthogonal transformation with the unified global coordinates. Second the orthogonality constraints are relaxed to be an instance of semi-definite programming (SDP). Finally a two-step iterative procedure is employed to further reduce the errors in orthogonal constraints. Extensive experiments show that LOPA can faithfully preserve distances, angles, inner products, and neighborhoods of the original datasets. In comparison, the embedding performance of LOPA is better than that of PSA and comparable to that of state-of-the-art algorithms like MVU and MVE, while the runtime of LOPA is significantly faster than that of PSA, MVU and MVE.