物联网体系及相关技术研究

当前物联网的定义、内在原理、体系结构和系统模型等方面还存在许多值得探讨的问题,为此,通过解析物联网的基本概念和特征,提出了物联网体系架构的感知层、网络层及应用层的三层架构,归纳了物联网涉及的射频识别、传感器、网络通信和云计算等关键技术,讨论了物联网在对象识别,跟踪及控制方面的具体应用。     

Direct 5-axis tool-path generation from point cloud input using 3D biarc fitting

In reverse engineering, geometrical information of a product is obtained directly from a physical shape by a digitizing device. To fabricate the product, manufacturing information (usually tool-path) must be generated from a CAD model. The data digitized must be processed and in most cases, a surface model is constructed from them using some of the surface fitting technologies. However, these technologies are usually complicated and the process for constructing a surface patch from a massive digitizing data is time-consuming. To simplify the process for getting tool-path information, a simple algorithm is proposed in this paper. The algorithm is used to generate a 5-axis machining tool-path. Instead of implementing any complicated surface fitting techniques, a direct method is proposed for constructing three-dimensional (3D) triangular mesh from the digitizing data with the mesh points considered as the tool contact locations. Depending on the locations of the points digitized, a decimation procedure is applied such that some of the digitizing data will be filtered out. Then, the tool axis orientations which must be determined in 5-axis tool-path are calculated and the tool center locations are determined accordingly. A 3D biarc fitting technique is applied for all the tool center locations so that a complete 5-axis tool-path is obtained.     

基于云理论的煤矿安全监测数据关联规则挖掘

本文主要研究如何运用关联规则来评价巷道瓦斯危险源的风险程度,作为分析煤矿事故危险源的补充.文章在云模型的基础上,针对煤矿安全监测数据的特点,提出一种基于云理论的属性空间软划分模型,然后在此基础上对Apriori算法进行了改进,提出适用于对煤矿安全监测数据进行关联规则挖掘的算法.最后通过实例测试,验证了改进算法的有效性.     

一种基于云模型的数字图像水印技术

提出了基于云模型的数字图像水印技术。利用云模型的模糊性和随机性,给定参数使用正向云发生器产生的云滴作为水印信息,将云水印嵌入到数字图像中。在提取水印时,除了可以将还原出的云模型三个参数与原始参数进行比较外,还可以直接对云图进行对比判断。即使部分云滴改变,对云模型影响也不大,基于云模型的水印具有较好的鲁棒性。     

信息系统及其综合集成技术

信息与信息系统无处不在,信息化已成为当今的时代特征。本文通过对这一时代特征的透视,首先阐明计算机信息系统的切确含义和基本结构;然后指出,为适应信息世界的不断变化和发展,一体化信息系统所需的综合集成是柔性的,并揭示支持随需应变的若干敏捷软件技术;进而就相关的平台软件,包括基础中间件和各类应用集成中间件,以及领域应用框架进行分类并作必要诠释;最后讨论信息系统的发展趋势和云计算等若干热点问题。     

网络时代的人工智能

五十多年来,人工智能在模式识别、知识工程、机器人等领域已经取得重大成就,但是离真正的人类智能还相差甚远。本文强调 在当今的网络时代,作为信息技术的先导,人工智能科学有着非常值得关注的研究方向,要在学科交叉研究中实现人工智能的发展与创新。要关注认知科学、脑科学、生物智能、物理学、复杂网络、计算机科学与人工智能之间的交叉渗透,尤其是重视认知物理学的研究;自然语言是人类思维活动的载体,是人工智能研究知识表示无法回避的直接对象,要对语言中的概念建立起能够定量表示的不确定性转换模型,发展不确定性人工智能;要利用现实生活中复杂网络的小世界模型和无尺度特性,把网络拓扑作为知识表示的一种新方法,研究网络拓扑的演化与网络动力学行为,研究网络化了的智能,从而适应信息时代数据挖掘的普遍要求,迎接人工智能科学与应用新的辉煌。     

基于OpenGL的三维牙颌模型可视化研究

为了实现牙颌模型的三维数据可视化,通过计算机逼真的显示牙列形态,并进行交互测量及变形等操作。在口腔修复体计算机辅助设计中,以OpenGL为渲染工具,结合自主设计的牙颌模型三维激光扫描仪获取的点云数据,采用基于B样条曲面的三维重建技术,以Visual C＋＋为开发工具实现数字化牙颌模型的三维可视化。以Visual C＋＋和OpenGL为工具,减少了程序设计难度,提高了执行效率,图形显示更加逼真流畅。本系统的设计满足了口腔医生的需求,给患者带来了便利,并促进了口腔修复体的开发制作。     

基于引导调制的彩色点云无参考质量评价方法

彩色点云(color point cloud, CPC)作为三维场景和对象的有效描述形式,在虚拟现实、增强现实等许多领域得到重要应用。CPC在其采集、压缩、传输、重建等过程中会引入相应的失真,需要设计有效的评价方法对失真CPC质量进行评测。本文提出一种基于引导调制的CPC无参考质量评价方法。考虑到几何信息与彩色纹理信息的联合失真,利用引导调制的方法联立两者,以综合考虑几何失真、彩色纹理失真、联合失真。结合人眼的多通道性,利用剪切波变换提取特征。最后,将所有特征构成的特征向量输入到支持向量回归模型(support vector regression, SVR)学习预测点云质量。实验结果表明,所提出的方法与人类主观感知具有很好的一致性。     

Secure communication in CloudIoT through design of a lightweight authentication and session key agreement scheme

Internet of Things (IoT) is a newly emerged paradigm where multiple embedded devices, known as things, are connected via the Internet to collect, share, and analyze data from the environment. In order to overcome the limited storage and processing capacity constraint of IoT devices, it is now possible to integrate them with cloud servers as large resource pools. Such integration, though bringing applicability of IoT in many domains, raises concerns regarding the authentication of these devices while establishing secure communications to cloud servers. Recently, Kumari et al proposed an authentication scheme based on elliptic curve cryptography (ECC) for IoT and cloud servers and claimed that it satisfies all security requirements and is secure against various attacks. In this paper, we first prove that the scheme of Kumari et al is susceptible to various attacks, including the replay attack and stolen-verifier attack. We then propose a lightweight authentication protocol for secure communication of IoT embedded devices and cloud servers. The proposed scheme is proved to provide essential security requirements such as mutual authentication, device anonymity, and perfect forward secrecy and is robust against security attacks. We also formally verify the security of the proposed protocol using BAN logic and also the Scyther tool. We also evaluate the computation and communication costs of the proposed scheme and demonstrate that the proposed scheme incurs minimum computation and communication overhead, compared to related schemes, making it suitable for IoT environments with low processing and storage capacity.     

IPC-Net: Incomplete point cloud classification network based on data augmentation and similarity measurement

Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness.