RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

An attention graph stacked autoencoder for anomaly detection of electro-mechanical actuator using spatio-temporal multivariate signals

Health monitoring of electro-mechanical actuator(EMA)is critical to ensure the security of airplanes.It is difficult or even impossible to collect enough labeled failure or degradation data from actual EMA.The autoencoder based on reconstruction loss is a popular model that can carry out anomaly detection with only consideration of normal training data,while it fails to capture spatio-temporal information from multivariate time series signals of multiple monitoring sensors.To mine the spatio-temporal information from multivariate time series signals,this paper proposes an attention graph stacked autoencoder for EMA anomaly detection.Firstly,attention graph con-volution is introduced into autoencoder to convolve temporal information from neighbor features to current features based on different weight attentions.Secondly,stacked autoencoder is applied to mine spatial information from those new aggregated temporal features.Finally,based on the bench-mark reconstruction loss of normal training data,different health thresholds calculated by several statistic indicators can carry out anomaly detection for new testing data.In comparison with tra-ditional stacked autoencoder,the proposed model could obtain higher fault detection rate and lower false alarm rate in EMA anomaly detection experiment.

TWO APPROACHES TO THE DESIGN OF TIME-VARYING CASCADED FILTERS

Two approaches to the design of time-varying cascaded filters used in radar clutter rejection are presented. In the first approach, by fitting the cascaded filter to the noncascaded filter, the time-varying cascaded filter can be designed, which makes it possible that the time-varying cascaded filter behaves just like an optimum clutter filter. The second approach can be used to design the second-stage filter in a time-varying cascaded one by setting zeros in its equivalent overall frequency response. It has been shown that it is difficult to express the frequency response of the second-stage filter in the time-varying cascaded one, however, it is convenient to be involved in the overall response.

快速刀具伺服系统位置域重复控制设计及其数字实现

在非圆零件车削过程中,快速刀具伺服(Fast tool servo,FTS)的运动精度直接影响零件的加工质量.主轴变速加工使得FTS的参考目标信号周期时变而不确定,这对实现其渐近跟踪提出了极大的挑战.本文利用FTS的位置域周期特性,提出一种基于位置域重复控制和时域速度反馈镇定的FTS系统复合控制设计方法,并给出位置域改进型重复控制器(Spatial modified repetitive controller,SMRC)的数字实现算法,实现对时变周期参考目标信号的高精度跟踪.首先,建立包含位置相关时变周期参考目标信号内模的SMRC,并引入位置域相位超前装置对镇定补偿器引起的相位滞后进行补偿,在此基础上构建复合控制律.然后应用小增益定理和算子理论,推导出控制系统的稳定性条件,在保持系统采样频率不变的条件下,应用插值法建立SMRC的数字实现算法,确保位置域重复控制和时域镇定控制器的同步执行.最后,通过仿真验证所设计的FTS控制系统具有满意的时变周期跟踪性能和鲁棒性,并通过与其他位置域重复控制方法的比较,说明所提方法同时具有更好的暂态和稳态性能.

空间信息网络时变图建模方法

空间信息网络是星地融合网络中一个重要组成部分,其拓扑具有时变性和可预测性。采用一个合适的时变图模型来表征时变的空间信息网络,对于空间信息网络性能分析至关重要。首先阐述了空间信息网络的特性,然后介绍了多种空间信息网络时变图模型的建模方法,包括快照序列图、时间扩展图、存储时间聚合图和多功能时间扩展图,并且分析了它们的特征和优缺点,为6G星地融合网络的研究奠定基础,为网络性能分析提供了理论支撑。

世界增加值贸易网络演化及形成机理——基于复杂网络方法

全球价值链分工改变了世界生产范式,促使各国价值流动和贸易关系趋于复杂,对世界贸易网络体系和各国贸易网络地位产生了深刻影响。考察世界国内增加值贸易网络演化规律,探讨其形成机理,对揭示国际贸易网络分布格局,把握全球经贸格局演进趋势,推动新一轮全球经贸规则治理策略意义重大。文章基于UNCTAD-Eora增加值贸易数据,采用复杂网络方法评价了2003—2021年世界国内增加值贸易网络演变特征,结果表明:世界国内增加值贸易网络不断扩大、联系日趋紧密和区域化、融合度逐渐加深,已形成以德国、美国和中国为中心和“桥梁”的“多极化”分布格局和联结疏密差异明显但结构相对稳定的五大社团凝聚子群;“一带一路”与非“一带一路”国家贸易地位差距趋于波动收窄。采用TERGM模型进行网络模拟发现,关系偏好连接效应、三元传递闭合效应、各国GDP、制度质量、关税率、贸易便利化水平、共同边界、区域贸易协定网络和历史殖民关系网络是世界国内增加值贸易网络形成的决定因素,各因素的积极影响不仅在不同区域和阶段有明显差异,而且会随国家贸易网络地位的提升而增强。

基于时变滤波经验模态分解的非接触式心率变异性估计方法

民航从业人员的身体健康状况是影响航空安全的重要因素,其中呼吸和心跳是极其重要的健康状况表征。为解决接触式或穿戴式测量系统对人员工作时的局限与影响,可采用线性调频连续波(Frequency-modulated continuous wave,FMCW)雷达达到非接触式测量的目的。由于生命体征信号具有时变、非平稳的特点,针对经验模态分解(Empirical mode decomposition,EMD)在信号分解中存在模态混叠现象的问题,使用时变滤波经验模态分解(Time varying filtering based on EMD,TVF-EMD)自适应信号的局部截止频率,可有效提高信号分离性能,解决模态混叠问题。利用TVF-EMD分解出的本征模态函数(Intrinsic mode functions,IMF)分量重构心跳对应的时域信号,估计心跳信号的频率和心跳节拍间隔(Inter-beat interval,IBI),进一步对心率变异性(Heart rate variability,HRV)相关指标进行估计。仿真实验与实测数据处理结果表明,TVF-EMD可从毫米波雷达测量信号中有效分离出呼吸与心跳信号。同时,从模态混叠程度及信号分离性能两方面对TVF-EMD与EMD方法分解效果进行了仿真分析,结果表明TVF-EMD能够有效解决模态混叠问题。因此,TVF-EMD方法能够准确有效地从毫米波雷达测量信号中提取生命体征信息,为IBI估计和HRV分析提供准确的时域信息,具有广阔的应用前景。

利用离散时间聚合图的无人机编队最短时延路由协议

针对传统的无人机编队路由算法无法有效利用拓扑变化的可提前预知特性、以发送探测包的方式获取链路的连接情况会导致开销大等问题,该文引入时变图模型,提出了基于离散时间聚合图的无人机编队最短时延路由协议。首先,利用无人机编队网络的先验知识,如节点的运动轨迹以及网络拓扑变化情况,使用离散时间聚合图对网络的链路资源和拓扑进行表征。其次,基于该图模型设计路由决策算法,即在路由探索阶段将链路时延作为链路权重求解网络的源节点到目的节点的最短时延路由。最后,性能仿真结果表明,该路由协议与传统按需距离矢量路由协议相比提高了网络的分组投递率、降低了端到端时延和网络的控制开销。

基于改进样条Chirplet变换的结构瞬时频率识别

为了提高结构瞬时频率识别的精度,对样条Chirplet变换进行改进,并结合能量集中度原则优化窗函数的参数.利用解析信号和两层框架模型验证改进样条Chirplet变换识别瞬时频率的准确性,再结合试验验证改进样条Chirplet变换能有效识别实际工程结构的瞬时频率.

基于时空关联表征的空天地一体化网络资源精准管控方法

空天地一体化网络中,资源分布不均匀且随时间动态变化。传统基于静态资源表征的优化调度方法仅在单个时间片上进行调度优化,资源利用率低,并且在网络变化时需频繁地重新调整策略,带来较大迁移成本。为解决这一问题,提出一种空天地一体化网络资源时空关联表征方法。该方法通过在网络的结构邻域和时序邻域两个维度计算节点注意力表征,实现对空天地一体化网络资源时空关联关系的精准建模。在此基础上,提出基于时空关联表征的网络资源精准映射方法,采用置信域策略梯度优化方法,对算法参数进行自适应学习与优化。

线性时变时滞切换系统的区间指数稳定/镇定

研究了线性时变时滞切换系统的区间指数稳定与镇定问题,重点研究了收敛速度的反映与调整。首先,基于系统矩阵与线性时变时滞切换系统稳定性的关系,提出区间指数稳定性的定义。基于平均驻留时间法,给出了区间指数稳定性的一个新判据。然后,通过新的控制器设计方法,使控制器既能保证系统指数稳定又能约束系统的收敛速度。最后,通过数值算例和RLC电路验证了所提方法的有效性。

时变通信稀疏信道高精度估计方法仿真

无线信道环境愈发复杂,信道质量直接影响接收机对信号的处理性能与系统通信能力,由此提出一种基于压缩传感的时变通信信道估计算法优化策略。推算子载波接收信号信噪比,运用动态子信道选择方法检测信号特征;计算正交频分复用符号周期的抽头增益均值,利用周期前缀模式消除多信道码间干扰,建立时变通信信道频域传递模型,明确时变通信环境状态;使用压缩传感算法计算信号稀疏特征,采用步长因子参数控制稀疏度实施子空间迭代跟踪,搜索最恰当的输入信号稀疏性原子,最后使用模糊阈值优化原子特征并重构初始信号,得到高精度信道估计结果。仿真实验证明,所提算法通信信道估计误差小,能够为无线通信的可靠应用提供参考。

一类切换时滞神经网络的有限时间无源性

为了研究切换拓扑下的时滞神经网络有限时间无源性,通过设计状态依赖型切换信号和控制器的方法,得到在切换拓扑下的时滞神经网络具有有限时间无源性的结论,进而得到有限时间镇定的结论。当2个时滞神经网络系统都具有有限时间无源性时,通过设计切换信号的方法,得到它们的反馈互联系统具有有限时间无源性的结论,通过数值例子验证定理结果的有效性。

采用联合算法识别人-桥耦合系统时变模态参数

为更加准确地识别在役人行桥结构的时变模态参数,提出了一种基于联合方法的人行桥瞬时频率和时变阻尼比识别方法。首先,采用联合方法分别识别模态分量信号的瞬时频率和自由衰减振动信号的时频系数。然后,通过推导瞬时频率、时频系数模值和阻尼比三者之间的函数关系,估算获得人-桥耦合系统的时变阻尼比。最后,通过人-桥耦合数值算例和华威桥步行试验对联合方法进行了验证。结果表明:联合方法可以有效改善多种复杂因素下自由衰减振动信号提取结果不准确的问题,并且该方法识别的瞬时频率和时变阻尼比曲线可以清晰地表征行人空间位置变化对钢-混组合人行桥频率和阻尼比的变化趋势。

Distributed Fault Detection for Consensus in Second-Order Discrete-Time Multiagent Systems with Adversary

This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.

基于消息传递图神经网络的电力系统状态估计

近年来,随着电网规模日益增大,系统拓扑变化愈加频繁,拓扑变化组合向多样化趋势发展。然而,现有数据驱动状态估计模型仅能处理欧式数据,不能有效挖掘像拓扑信息这样的非欧式数据,因此现有数据驱动模型在拓扑频繁变化时的适应性较差。该文提出一种基于消息传递图神经网络(messagepassingneural network, MPNN)的电力系统状态估计模型。首先,利用拓扑参数和量测信息构建图数据集;其次,基于不同拓扑下的图数据训练消息传递图神经网络,得到状态估计模型;最后,在线应用时将该拓扑下的图数据输入已训练好的网络模型即可得到当前断面的状态量。通过对IEEE标准系统和中国某实际省网的算例测试,并将估计结果与加权最小二乘法、加权最小绝对值法以及深度神经网络算法和卷积神经网络算法进行比较。结果表明,该算法更能适应大规模电网中实时拓扑变化的特性。

信息跳时架构下分层捷变低截获概率通信波形

针对周期信号抗破译性能较差及非周期聚敛性带来的误码性能下降的问题,本文提出一种时宽-信息跳时-调频率分层波形架构。通过构建信息跳时,掩护时宽随机化的模式,解决了误码性能变时宽下降问题,加强了波形的低截获概率特征。本文以数学推导和仿真分析的方法,探究了此分层波形架构下的误码性能、功率谱类噪声性能、二次谱域周期聚敛性及先验信息抗破译性。理论分析和仿真验证表明:时宽-信息跳时-调频率分层波形可以保证与定时宽信号相比,在不降低误码性能的条件下,降低了波形的周期聚敛性,从而提高了波形的截获概率性能。