疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

广义正交码索引调制系统的性能分析

摘 要：为了提高码索引调制（code index modulation，CIM）系统的传输效率，提出了一种具有更低复杂度的单输入单输出（single input single output，SISO）的广义正交码索引调制（generalized orthogonal code index modulation，GQCIM）系统。CIM 系统使用扩频码和星座符号传输信息，但只能激活两个扩频码索引和一个调制符号。而 GQCIM 系统以一种新颖的方式克服了只激活一个调制符号的限制，同时充分利用了调制符号的正交性，增加扩频码索引以传输更多的额外信息位，提高了系统的传输效率。此外，分析了GQCIM系统的理论性能，推导了误码率性能的上界。通过蒙特卡罗仿真验证了GQCIM系统的性能，对比发现GQCIM系统的理论和仿真性能一致。而且在相同的传输效率下，结果显示GQCIM系统的性能优于同样具有正交性的调制系统，如广义码索引调制（generalized code index modulation，GCIM）系统、CIM系统、码索引调制-正交空间调制（code index modulation aided quadrature spatial modulation，CIM-QSM）系统、码索引调制-正交空间调制（code index modulation aided spatial modulation，CIM-SM）系统、脉冲索引调制（pulse index modulation，PIM）系统。     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

基于SAOR的Massive MIMO系统信号检测算法

大规模多输入多输出(Massive multiple input multiple output, Massive MIMO)系统采用最小均方误差(Minimum mean square error, MMSE)接收检测方法时存在矩阵求逆复杂度高的问题，已有较多降低复杂度的研究。在降低检测算法复杂度的同时，如何提高算法收敛速度和检测性能一直是人们关注的焦点。本文将对称加速超松弛(Symmetric accelerated over-relaxation， SAOR)迭代算法应用于Massive MIMO系统信号检测中，避免了复杂的矩阵求逆计算，实现了复杂度较最小均方误差算法降低了一个数量级。仿真结果表明，基于SAOR的检测方法通过较少的迭代次数就能逼近最小均方误差（Minimum mean square error, MMSE）算法的检测性能，为Massive MIMO系统中接收信号的快速检测提供了较好的实现方法。     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

Machining accuracy improvement for a dual-spindle ultra-precision drum roll lathe based on geometric error analysis and calibration

This paper performs a comprehensive analysis and calibration on the geometric error of the ultra-precision drum roll lathe with dual-spindle symmetrical structure and cross slider layout. Firstly, the volumetric error model which contains all geometric errors of the dual-spindle ultra-precision drum roll lathe (DSUPDRL) is developed based on the combination of the homogenous transfer matrix (HTM) and multi-body system (MBS) theory. Secondly, sensitivity analysis for the volumetric error model is conducted to identify the sensitive geometric error components of the DSUPDRL using an improved Sobol method based on the quasi-Monte Carlo algorithm. The result of sensitivity analysis laid the foundation for the subsequent geometric error calibration. Then, some sensitive error components along the X and Z directions are calibrated using a laser interferometer and a pair of inductance displacement probes. Besides the volumetric error model, the concentricity error caused by dual-spindle symmetrical structure is proposed and calibrated by the on-machine measurement using a classic reversal method. Finally, a large-scale roller mold with a diameter of 250 mm and a length of 600 mm is machined using the DSUPDRL after calibration. The experimental result shows that 1.4 μm/600 mm generatrix accuracy is obtained, which validate the effectiveness of the geometric error analysis and calibration.     

Beta-Binomial删除信道中的喷泉码文件传输协议

由于命名数据网络(NDN,Named-Data Networking)无环路、逐包、逐跳转发的特点,使得数据包回传成功率降低,而传统的TCP/IP协议中的ARQ和ACK机制对于多播会话不再适用.由于NDN中的传输信道可以等效为二进制删除信道,因此可以通过应用层编码来实现文件的可靠传输.传统的信道编码技术如卷积码、级联码和RS码等复杂度较高,而将NDN与低复杂度的喷泉码的结合可以实现分布式的存储架构,因而可通过喷泉编码在应用层协议中实现可靠的纠删机制,保证整体文件的传输可靠性.以往的研究一般是基于确定的删除概率信道模型,但是由于网络的异构性和信道噪声等因素影响,可能会造成信道丢包概率呈随机性分布.因此,本文在Beta-Binomial分布模型的前提下,根据贝叶斯统计的先验信息和中心极限定理,对随机概率下的纠删信道的文件可靠传输协议进行了数学建模和理论推导.仿真结果显示此模型更具有普适性,此传输协议可在信道状况未知的前提下从理论上求出最小发包数,减少冗余编码包,提高文件整体的投递成功率,在保证传输可靠性的同时有效提升协议传输效率.     

改型Wollaston棱镜干涉光谱仪的调制度研究

改型Wollaston棱镜(MWP)无需中继,可构建微型傅里叶变换光谱仪。回顾了MWP的基本原理,从调制度定义出发,计算得到准单色光源的复相干度,找到调制度的影响因素——扩展视场、剪切结构、焦面探测等。建立一般的干涉定域分析模型后,由像点逆光路追迹给出MWP的厚度计算公式,代入调制度约束参数得到不同参量的误差容限。通过FRED记录不同视场扩展情况的灰度与辐照度,结果显示,调制度为0.9时,视场与理论估算值1.24°基本相符;不同剪切量的灰度与辐照度变化特性表明,结构角会使定域垂直系统光轴的入射角发生变化,与晶轴倾角共同影响双折射率差;2.5和8μm像元探测采样表现出调制度差异,描述了定域深度或离焦的影响。以准单色光源、复相干度表示调制度,可将不同影响因素统一至约束参数中,相关变量误差容限可作为MWP-FTS的设计依据。