Semi-supervised radio transmitter classification based on elastic sparsity regularized SVM

Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.     

基于DSP与BIOS内核的运动控制器软件系统架构

在以德州仪器(TI)公司的DSP为主控芯片,FPGA为辅助控制的运动控制器硬件平台上,采用TI公司的BIOS实时内核,提出一种运动控制器软件架构方案。该方案可以针对不同用户需求进行功能组件二次开发和移植,软件源代码采用模块化设计,具有标准化函数接口,可维护性良好。经过实验测试,能满足开放性、实时性、可移植性的要求。     

叶轮刀位轨迹的后置处理与数控仿真

研究了在VERICUT中构建叶轮的虚拟加工仿真平台,通过叶轮整个加工过程的仿真模拟,验证NX8.0加工模块后处理器生成的专用VHP800-5AX五轴联动加工中心加工叶轮数控程序的正确性,优化了加工程序,防止干涉和过切,提高了数控程序的可用性与通用性。     

Electrode wear-compensation of electric discharge scanning process using a robust gap-control

Robust gap-control is applied to compensate for electrode wear in an electric discharge scanning (ED-Scanning) process. This control compensates for the wear without reference to the wear ratio of the electrodes. An auxiliary gap-control loop in the vertical direction can compensate for the wear of the electrode, according to the discharge voltage when the electrode is driven along the two horizontal axes. A proportional controller for a gap-control system with maximal robustness is designed and robustly analyzed to override the non-linear and time-varying feedback. The simulated and practical responses of such a robust gap-control system yield favorable performances. The controller can maintain the location of the discharge on the vertical axis and fix the depth of the removal to that of a particular layer. The practical scanning process reveals that the dimension of a hole can be guaranteed only if the data concerning the depth of removal under specific discharge conditions and electrode diameters are applied.     

基于稀疏最小二乘支持向量回归的非线性自适应波束形成

该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。     

基于仿射梯度方向直方图特征的目标识别算法

针对基于传统梯度方向直方图特征的目标识别算法(HOG+SVM)在目标发生仿射变化时识别效果较差的问题,该文提出一种基于仿射梯度方向直方图特征的目标识别算法(AHOG+SVM).通过提取多尺度金字塔梯度图像的 HOG 特征,提高了算法的尺度不变性;通过将平面 HOG 栅格拓展至3维 HOG 栅格,并根据目标的世界坐标系与图像坐标系的映射关系将3维 HOG 栅格映射为2维 HOG 仿射栅格,最后对仿射栅格内的 HOG 特征进行仿射逆变换,以达到增强算法旋转不变性与错切不变性的目的.多组实验结果表明,该文提出的算法能够解决在目标识别过程中由尺度变化、旋转变化和错切变化(3D 视角变化)所造成的识别率较低的问题,性能优于 HOG+SVM算法.     

SPWM变频器FR-A540在数控机床上的应用

本文从SPWM变频器结构原理,FR-A540变频器,FR-A540变频器在数控机床的应用三个方面介绍了PWM变频器的结构、工作原理;FR-A540变频器的性能、接线端子;FR-A540变频器在数控机床的应用;来供广大电气同行参考使用。     

运用英语流程图诊断数控机床故障问题

英语已成为数控专业高技能人才的基本素质要求之一,而诊断数控机床的故障问题是数控专业学生常见的工作任务,尝试基于“工学一体化”教学模式,以期为培养数控专业学生英语运用能力提供一些参考。     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

基于路径特征和支持向量机算法的硬件木马检测技术

硬件木马攻击成为当前集成电路(IC)面临的严重威胁。针对硬件木马电路具有隐蔽、不易触发以及数据集不均衡等特点,该文提出对门级网表进行静态分析的硬件木马检测技术。基于电路可测性原理建立涵盖节点扇入数、逻辑门距离、路径数、节点扇出数的硬件木马路径特征,简化特征分析流程;基于提取的路径特征,使用支持向量机(SVM)算法区分电路中的木马节点和正常节点。提出训练集双重加权技术,解决数据集不均衡问题,提升分类器的性能。实验结果表明,分类器可以用于电路中的可疑节点检测,准确率(ACC)达到99.85%;训练集静态加权有效提升分类器性能,准确率(ACC)提升5.58%;与现有文献相比,以36%的特征量,真阳性率(TPR)降低1.07%,真阴性率(TNR)提升2.74%,准确率(ACC)提升2.92%。该文验证了路径特征和SVM算法在硬件木马检测中的有效性,明确了数据集均衡性与检测性能的关系。