Optimization of interior roller burnishing process for improving surface quality

Improving the surface characteristics of roller burnishing processes is one of effective approaches to decrease the machining costs and time. This paper systematically investigates the nonlinear relationships between machining parameters and surface characteristics, including surface roughness (R_a), surface hardness (H), and hardness depth (HD) of the interior roller burnishing using response surface method (RSM) model. Three process parameters considered include spindle speed S, feed rate F, and burnishing depth D. A set of physical experiments was carried out with AISI 1045 steel on a computer numerical control (CNC) milling machine using the roller burnishing tool. The target of the current complex optimization is to enhance the surface hardness and hardness depth, while the surface roughness is considered as the constraint. Finally, an evolutionary algorithm entitled archive-based micro genetic algorithm (AMGA) was used to generate a set of feasible optimal solutions and determine the best machining conditions. The results show that an appropriate trade-off solution can be drawn with regard to the low surface roughness and high the surface hardness as well as hardness depth. Furthermore, the integration of RSM model and AMGA can be considered as a powerful approach for modeling and optimizing interior roller burnishing processes.     

Rational Filters for Passive Depth from Defocus

A fundamental problem in depth from defocus is the measurement of relative defocus between images. The performance of previously proposed focus operators are inevitably sensitive to the frequency spectra of local scene textures. As a result, focus operators such as the Laplacian of Gaussian result in poor depth estimates. An alternative is to use large filter banks that densely sample the frequency space. Though this approach can result in better depth accuracy, it sacrifices the computational efficiency that depth from defocus offers over stereo and structure from motion. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. In addition, a depth confidence measure is derived that can be computed from the outputs of the operators. This confidence measure permits further refinement of computed depth maps. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.51.2% of the distance of the object from the imaging optics.     

物联网安全接入网关的设计与实现

文章通过分析物联网发展过程中存在的安全问题,提出了如何通过物联网安全接入网关,构建接入安全、管理可控的物联网系统架构,最后详细设计了物联网安全接入网关的功能组成和安全技术要求。     

A novel three-tiered visualization approach for firewall rule validation

Firewall is one of the most critical elements of the current Internet, which can protect the entire network against attacks and threats. While configuring the firewalls, rule configuration has to conform to, or say be consistent with, the demands of the network security policies such that the network security would not be flawed. For the security consistency, firewall rule editing, ordering, and distribution must be done very carefully on each of the cooperative firewalls, especially in a large-scale and multifirewall-equipped network. Nevertheless, a network operator is prone to incorrectly configure the firewalls because there are typically thousands or hundreds of filtering/admission rules (i.e., rules in the Access Control List file, or ACL for short), which could be set up in a firewall; not mentioning these rules among firewalls affect mutually and can make the matter worse. Under this situation, the network operator would hardly know his/her misconfiguration until the network functions beyond the expectation. For this reason, our work is to build a visualized validation system for facilitating the check of security consistency between the rule configuration of firewalls and the demands of network security policies. To do so, the developed validation system utilizes a three-tiered visualization hierarchy along with different compound viewpoints to provide users with a complete picture of firewalls and relationships among them for error debugging and anomaly removal. In addition, in this paper, we also enumerate the source of security inconsistency while setting ACLs and make use of it as a basis of the design of our visualization model. Currently, part of the firewall configuration of our campus network has been used as our system's input to demonstrate our system's implementation.     

融合深度信息的视觉注意计算模型

针对Itti模型在特征选择上的局限性,借鉴心理学中有关视觉注意的研究成果,提出一种融合深度信息的视觉注意计算模型。该模型在基于图像分割的自适应立体匹配基础上提取深度特征,与亮度、方向、颜色特征相结合,实现空间显著性度量,并采用侧抑机制和WTA机制得到注意焦点。实验结果表明,新模型能较好地反映空间立体视觉信息对注意的影响,使计算结果能更符合人类视觉。     

基于视觉伺服的自主微装配方法

研究基于视觉伺服的自主微装配方法,为实现深度信息估计,提出一种基于马尔可夫随机场的散焦特征参数模型,将散焦特征深度信息的估计转化为能量函数的优化问题,采用Broyden 图像雅可比矩阵实现视觉伺服控制。通过微夹钳自动定位与夹取实验,验证了该方法的有效性。     

基于80C186处理器的多功能实验板的研制

针对近年来飞机含80C186处理器电路板修理现状,为掌握该系列板件的修理能力,迫切需要开展其软件测试技术研究.从封装、类型以及时序分析角度,在对80C186处理器进行系统阐述基础上,设计并研制一块基于80C186处理器的多功能(包括串口通信、并口检测以及定时计数功能)实验板,给出该板的硬件电路图与软件流程框图,并基于汇编语言在QTH-2008PCI开发环境下进行功能调试,实验结果表明其有效性.所做的含80C186处理器的正向设计与软件编程研究工作对于逆向修理含80C186处理器飞机电路板板件有很大帮助.     

投影深度向量分解融合PEMS的视角不变人体动作识别

针对摄像机内部参数的不确定性和投影平面选择难的问题,提出一种新的投影深度算法用于视角不变的动作识别,该算法采用对称镜面提取平面(PEMS)策略,有效解决了投影平面选择难的问题。首先通过摄像机组观察获得3D动作姿势,然后运用PEMS策略从场景中提取平面,相对于提取平面估计身体点的投影深度,最后使用这个信息进行动作识别。该算法的核心是投影平面的提取和投影深度组成向量的求解。利用本文算法在CMU MoCap数据集、TUM数据集和多视图IXMAS数据集上进行测试,精度可分别高达94%、91%和90%,且在较少动作实例情况下,仍然能够准确定义新动作。比较表明,本文算法的人体动作识别性能明显优于其他几种较新的算法。     

基于GPU的后置处理运动模糊算法

针对虚拟现实系统中运动模糊的真实感绘制问题,着重研究了一种基于图形处理器（GPU）后置处理的运动模糊改进算法. 该算法通过将场景渲染时前后两帧全分辨率纹理图的深度值存入深度缓存区,使用其深度信息与当前帧的映射矩阵提取物体的世界中心坐标,并获取运动的速度矢量;沿速度矢量进行采样、高斯滤波,最后引入模糊因子,并对线性化采样值进行加权融合,力求实现最真实的运动模糊效果. 实验结果表明：所提算法不仅不会影响大型虚拟现实系统的渲染流畅度,更改善了过分重影或者拖影,具有良好的实时性和真实感.     

基于说话人辨识的自上而下听觉显著性注意模型

为体现听觉注意神经信息处理计算机制对听觉场景内容的自动分析与理解功能,本文基于人耳对频率变换的感知特性,结合深度信念网络的说话人辨识与听觉显著模型,提出了一种自上而下的听觉显著性注意提取模型.仿真结果表明:该模型具有可行性,同时在利用深度信念网络的说话人辨识技术中能够有效地凸显目标说话人的显著度.