一种小型化多协议超高频RFID读写器模块

设计了一种兼容国际标准ISO/IEC 18000-6C 和中国标准GB/T 29768-2013 的超高频射频识别读写器模块。基于定向耦合器的双调谐射频收发电路以紧凑的结构实现了较高的隔离度,降低了对接收前端电路的线性度要求。利用定向耦合器的耦合信号作为解调本振,以提高其与射频自干扰的相关性,降低下变频后的基带残留相位噪声。设计的读写器模块工作在920~925 MHz 频段,饱和输出功率为29.6 dBm,对6C和国标标签的盘存距离均超过15 m。读写器模块的小型化通过采用小尺寸的收发隔离电路和电路板的三维堆叠封装得以实现,其体积仅为70 mm*60 mm*15 mm。     

烟幕对光电制导目标识别能力干扰效果研究

光电制导武器对目标的战场生存造成了巨大威胁,烟幕是对其有效干扰的一种重要手段。通过分析烟幕对可见光和红外辐射的作用原理,搭建了烟幕干扰实验平台,获得了烟幕对红外成像制导和景象匹配制导的干扰数据。对实验数据分析处理后的结果表明:烟幕能够有效降低红外成像制导系统的作用距离和目标识别能力,严重影响景象匹配制导算法的性能,甚至使其完全失效。     

EM-based blind LDPC identification in multipath channels

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.     

Research on traffic identification and scheduling based on optical interconnection architecture in data center

In order to solve the data center link congestion problem,based on the characteristics of the flow distribution and flow types,a flow identification and scheduling scheme based on optical interconnect structure,named HCFD (host-controller flow detection),was proposed to identify the elephant flow which has a large impact on the network performance,and use the SDN controller to make forward strategy,and schedule the network traffic reasonably.The implementation of the scheme was to use the Netfilter framework in Linux kernel protocol on the host side to mark the flow that exceeds the threshold amount.Then,the classification model was used in the controller side to classify the marked flow.Finally,the appropriate forwarding strategy was developed based on the above results.With the advantage of the photoelectric network,mechanisms of flow depth fusion and switching could be realized.The scheme which integrates the advantage of the existing research results,was expected to identify elephant flow more accurately and comprehensively.It can effectively alleviate the network congestion,make full use of network bandwidth,reduce end-to-end delay and packet loss rate.     

一种基于动态角色标识和张量分解的推荐模型

社交网络推荐中,通常未依据用户兴趣变化进行用户角色动态标注,会造成推荐预测误差,并且用户评分数据稀疏造成评分预测不准确.根据以上问题,本文提出一种基于动态角色标识和张量分解的推荐模型.首先,针对用户角色无差别标识问题,引入信息熵指标度量用户兴趣多样性,对目标用户进行角色定量标识.其次,考虑到用户兴趣漂移现象,提出基于时间窗的动态角色标识方式,解决静态角色标识产生的个体评分数据无偏好差异问题,实现用户评分数据层次化处理.最后,为提高评分预测准确率,通过引入张量分解在数据维度转换和数据压缩的特性,构建基于"用户-项目-角色"张量分解的评分预测模型.同时,在张量分解的过程中,通过对数据缺失值处理,提高评分预测精度,生成目标用户推荐列表.实验表明,该方法缓解了用户无角色差异形成的预测误差问题,并能够有效改善评分数据稀疏情况下传统方法推荐精度不足的问题,提高推荐效率.     

Wave Surface Identification Based on Stereo Vision and Wave Theory: an Initial Attempt北大核心CSCD

波高是波浪信息最基本的元素,对波高的精确测量无论是对波浪理论的研究还是数值方法的拓展,都起着指导和验证的作用。文中基于双目立体视觉原理自主搭建了波面光学测量系统,突破了传统测量设备如浪高仪等单点测量的局限性,并将波浪理论融入到数据后处理方法中,对常用的单纯依赖图像的光学测量方法进行了改进。通过在拖曳水池中对单向规则波瞬时波面的识别和重构,并将结果与浪高仪以及理论来波参数进行了对比验证,结果表明该测量系统在大范围波面的测量中误差在1%左右,最后对其在非规则的来波下进行了初步尝试。     

Structural Crack Identification Based on the Variational Mode Decomposition北大核心CSCD

In order to enrich the bridge damage detection method and further improve the accuracy of bridge damage identification, a detection method for simply supported beams with cracks under dynamic loads was proposed not based on the complete finite element model. Under the premise of not blocking traffic, the method only needs to analyze and deal with the acceleration responses of the simply supported beam span, which reduces the mounting, dismounting and maintenance of sensors in practical engineering. At the same time, based on the model, an analytical formula of the acceleration at the midspan of the simply supported cracked beam was derived. Based on the theoretical derivation, the instantaneous energy and the mean energy difference were constructed through the variational mode decomposition and the Hilbert transform, and these 2 crack identification indexes were used to effectively identify small cracks with a crack depth ratio of only 5%. Then the influences of different wheel loads, environmental noises and damage degrees on detection results were studied. The results show that: ① the instantaneous frequency has a better recognition effect for crack positions; ② the mean energy difference is sensitive to crack depth ratio δ and the wheel load magnitude; ③ this method has strong noise robustness. © 2022 Editorial Office of Applied Mathematics and Mechanics. All rights reserved.     

基于多率采样的永磁同步电机控制方法设计

在工业自动化领域,永磁同步电动机在中小功率控制系统中发挥着重要作用。本项目采用多采样率方法构建永磁同步电机控制系统,其主要解决的问题包含多采样率下的速度及参数辨识、永磁同步电机多采样率滑模变结构控制系统设计。     

一种协同问答信号配对规则提取方法

针对通信协议未知或部分已知条件下询问和应答信号配对难、配对准确度不高等问题,利用询问与应答信号间时序的模式规律,提出了一种询问应答信号配对规则提取方法,实现了问答信号精准配对。以非合作侦察获得的询问和应答信号特征参数时间序列为基础,首先根据询问、应答节点定位信息和侦察节点位置信息估计时间窗,在此基础上以询问信号到达时间为起始,在应答信号序列中搜索时间窗范围内的应答信号,交叉构建询问-应答信号2-项候选项集;再采用DBSCAN(Density-based Spatial Clustering of Applications with Noise)算法对候选项集进行特征聚类并挖掘频繁项集,通过频繁项集中问答信号频率、到达时间、脉冲宽度等参数,提取信号时序和特征配对规则;最后利用规则进行问答信号配对。计算机仿真和实采数据验证了该方法的有效性,当设置时间窗大小符合信号传播时间差且规则偏差小于50%时,置信度可达90%以上。     

Automatic modulation classification of noise-like radar intrapulse signals using cascade classifier

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at −2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.