Recognition of dynamically varying PRI modulation via deep learning and recurrence plot

Recognition of pulse repetition interval(PRI)modulation is a fundamental task in the interpretation of radar intentions.However,the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences.The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification,while the actual situation is that radar pulses reach the receiver continuously,and there is no completely reliable method to achieve this division in the case of non-cooperative reception.Based on the above actual needs,this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model,which does not need to divide the PRI sequence in advance.Compared with the sliding window method,it can more effectively realize the recognition of the dynamically varying PRI mo dulation.

Nonlinear dynamics in Divisia monetary aggregates:an application of recurrence quantification analysis

We construct recurrence plots(RPs)and conduct recurrence quantification analysis(RQA)to investigate the dynamic properties of the new Center for Financial Stability(CFS)Divisia monetary aggregates for the United States.In this study,we use the lat-est vintage of Divisia aggregates,maintained within CFS.We use monthly data,from January 1967 to December 2020,which is a sample period that includes the extreme economic events of the 2007–2009 global financial crisis.We then make comparisons between narrow and broad Divisia money measures and find evidence of a nonlinear but reserved possible chaotic explanation of their origin.The application of RPs to broad Divisia monetary aggregates encompasses an additional drift structure around the global financial crisis in 2008.Applying the moving window RQA to the growth rates of narrow and broad Divisia monetary aggregates,we identify periods of changes in data-generating processes and associate such changes to monetary policy regimes and financial innovations that occurred during those times.

Comparison study of typical algorithms for reconstructing time series from the recurrence plot of dynamical systems

The three most widely used methods for reconstructing the underlying time series via the recurrence plots （RPs） of a dynamical system are compared with each other in this paper. We aim to reconstruct a toy series, a periodical series, a random series, and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis. The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm. It is verified that, based on the unthresholded RPs, one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm. It is shown that, in real applications, it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems. Moreover, rules of the threshold chosen in the algorithm are also suggested.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Middle ear reconstruction estimated by recurrence plot technique

Middle ear surgery techniques have enabled to improve hearing destroyed by a disease. Despite huge improvement in instrumentation and techniques the results of hearing improvement surgery are still difficult to predict. This paper presents the results of vibrations measurements in a human middle ear obtained at the Medical University of Lublin. Vibrations of the stapes in the case of the intact ossicular chain, after cement incus rebuilding and incus interpositions are compared each other. In this aim a new approach of ossicles vibrations observation is introduced in order to complete information obtained from classical approach which bases on the transfer function. Measurements of ossicular chain vibrations are performed on fresh human temporal bone specimen using the laser doppler vibrometer. Next, after classical research, the extended analysis with the recurrence plots technique is performed.

Analysis of Key Features of Non-Linear Behavior Using Recurrence Plots. Case Study: Urban Pollution at Mexico City

The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different approaches have been used to tackle this problem. The first approach is to show results according to monitoring network. The second approach is to show the results by particle type. This analysis shows the feasibility of using Recurrence Analysis for pollution monitoring and control.

Recurrence Quantification Analysis of Rough Surfaces Applied to Optical and Speckle Profiles

In this paper, Recurrence Quantification Analysis (RQA) is set as a practical nonlinear data tool to establish and compare surface roughness (Ra) through percentage parameters of a dynamical system: Recurrence (%REC), Determinism (%DET) and Laminarity (%LAM). Variations in surface roughness of different machining procedures from a typical metallic casting comparator are obtained from scattering intensity of a laser beam and expressed as changes in the statistics of speckle patterns and profiles optical properties. The application of the analysis (RQA) by Recurrence Plots (RPs), allowed to distinguish between machining procedures, highlighting features that other methods are unable to detect.

基于电容传感器的CO_(2)气液两相流流型识别

为了实现碳捕捉与封存(CCS)管道内CO_(2)流量的精确测量,需要对CO_(2)的流型进行辨识。基于递归定量分析和模糊C均值(FCM)聚类算法,建立了CO_(2)气液两相流的流型识别模型。提出利用小波变换在多尺度下分析CO_(2)气液两相流电容信号递归图结构特性的方法,对各个尺度下的递归图分别进行定量分析,提取出不同流型下CO_(2)流动的电容传感器递归特征参数。采用FCM算法,对特征参数进行流型聚类,完成了对CO_(2)气液两相流的分层流、气泡流和雾状流3种流型的识别。

基于尺度递归分析的恶性室性心律失常检测

为及时准确地检测恶性室性心律失常,本研究提出了一种结合心电多尺度分析与递归量化分析(recurrence quantification analysis, RQA)的方法。首先,通过固定频率经验小波变换滤波器组将心电信号(electrocardiogram, ECG)分解为两个子信号(0.5~10 Hz、10~30 Hz),并分别映射为递归图;然后基于0.5~10 Hz频段子信号与原信号构建交叉递归图,从三张递归图中提取RQA特征;最后将特征输入XGBoost分类器进行特征排序与筛选,实现ECG的准确分类。本研究使用MIT-BIT恶性室性心律失常数据库和Creighton大学室性快速性心律失常数据库进行实验。在10折交叉验证下,该方法对5 s心电的恶性室性心律失常检测的灵敏度、特异性、准确率分别为97.40%、99.01%和98.69%。本研究方法在检测恶性室性心律失常方面具有一定的可靠性。

极端事件冲击下原油期货市场有效性的演变特征

原油期货市场极易受到地缘政治冲突和金融危机等极端事件冲击的影响,基于多重分形降趋波动分析和递归图方法构建了4种指标,量化分析了上海原油期货市场的有效性,并以国际原油Brent和WTI期货市场作为对比,系统地研究了极端事件冲击下市场有效性的动态演变特征。最后,对WTI原油期货市场的长历史数据进行了分析。研究发现:在相同的样本期间内,由于中国原油期货的制度优势、审慎适时的风控政策和具有强大韧性的经济基本面,极端事件冲击对国际原油期货市场有效性的负面影响要大于国内市场,从而使得上海原油期货市场的有效性在不同时间尺度下都高于Brent和WTI原油期货市场;原油期货市场的有效性不是固定不变的,具有显著的均值回复特征,极端突发事件会对原油期货市场的有效性造成严重的负面冲击,由于原油期货市场系统具有自我修复的能力,外生冲击造成的短暂动荡会被逐渐吸收化解,以维持自身市场有效性的相对稳定。所构建的市场有效性指标对预警原油期货市场风险具有一定的效果。

Ti-48Al-2Cr-2Nb合金在流动电解液中的钝化行为及非线性动力学分析

探究Ti-48Al-2Cr-2Nb合金钝化膜的稳定性和形成过程以提高电解加工中的抗杂散腐蚀。通过循环伏安曲线确定了Ti-48Al-2Cr-2Nb合金的钝化电位,通过相空间重构、递归图及其定量分析确定系统的波动程度和稳定性,并运用形核曲线和功率谱密度分析钝化膜的形成过程。结果表明,Ti-48Al-2Cr-2Nb合金在20% NaNO_(3)溶液中的钝化区间为0~1.8 V,进而依次选取0.8、1.0、1.2、1.4、1.6 V这5个电位的钝化区间,分析动态流场下形核曲线,发现钝化膜的成膜过程主要是扩散的方式控制瞬间形核以及三维生长方式进行,然后通过功率谱密度发现钝化膜的生长过程受到粒子的分形布朗扩散影响。通过分析钝化过程的非线性动力学行为,发现相空间重构的吸引子整体波动程度较小的电位有1.2V和1.4V,且通过递归定量分析,发现1.2V电位下系统具有很强的周期性。

基于递归图和BP神经网络的桥梁损伤识别研究

为研究递归图和多层前馈(BP)神经网络在桥梁损伤识别方面的应用,以某大跨斜拉桥为例,采用ABAQUS有限元软件建立其三维模型,通过动力分析提取该三维模型的加速度曲线并进行递归图处理和BP神经网络分析。研究结果表明:递归图方法能够初步地识别主梁的损伤位置和损伤程度;BP神经网络分析能够精确识别主梁损伤的具体位置和损伤程度值,且识别准确率均大于85.0%。该方法可为类似桥梁工程的损伤识别提供借鉴。

基于ISAM-Drsnet的故障识别模型及其应用

针对滚动轴承故障诊断时网络模型在复杂环境下有效特征提取困难,无法充分挖掘具有周期性的滚动轴承故障数据时序特征的问题,提出了一种基于改进条纹注意力机制与深度残差收缩网络的滚动轴承故障诊断模型(ISAM-Drsnet)。首先,采用递归图(RP)编码方式生成了二维图像,使用ISAM和改进软阈值算法加强了Drsnet;然后,采取重叠采样的方式对数据集进行了增强处理,并将数据输入到ISAM-Drsnet中,实现了对不同故障类型的识别目的;最后,利用凯斯西储大学滚动轴承数据集进行了实验,选取了最佳数据截取长度,研究了改进软阈值、数据集规模、噪声对模型的影响;同时,将该模型与支持向量机(SVM)、反向传播神经网络(BPNN)、卷积神经网络(CNN)等进行了对比分析,并采用混淆矩阵等可视化方法对该模型进行了性能评估。实验结果表明:该模型(方法)的故障诊断性能明显优于SVM、BPNN、CNN等模型,其故障诊断精度可达99.79%,相比原始的Drsnet上升了1.60%;且在数据集规模有限和信号添加噪声的情况下,模型仍具有较高的故障诊断精度。研究结果表明:该轴承故障诊断模型不仅具有优秀的诊断性能,同时还具有较强的鲁棒性。

Study on Chaotic Characteristics of the Friction Process between High Hardness Alloy Steel and Cemented Carbide under C60 Nanoparticle Fluid Lubrication

Friction and wear phenomenon is a complex nonlinear system,and it is also a significant problem in the process of metal cutting.In order to systematically analyze the friction and wear process of tool material-workpiece material friction pair in the cutting process of high hardness alloy steel under different lubrication conditions,the chaotic characteristics of friction process between high hardness alloy steel and cemented carbide under the lubrication C60 nano-particles fluid are studied based on the chaos theory.Firstly,the friction and wear experiments of the friction pair between high hardness alloy steel and cemented carbide tool are carried out based on the ring-block friction and wear tester,and the results of friction force signal in time domain and wear width are obtained.Then,the friction signals in time domain are processed and transformed based on phase space reconstruction and recurrence plot theory,and the recurrence plots of different experimental groups under different lubrication conditions are generated.The evolution law of recurrence plot is further observed and studied,and the recursive quantitative index is analyzed.Finally,the cutting experiments of tool wear are carried out.The results show that the proposed method can intuitively and accurately reveal the wear evolution process and the wear feature identification law of the tool material-high hardness alloy steel pair under different lubrication conditions.Meanwhile,it is found that when the concentration of C60 nanoparticles is 200∼300 ppm,the stability of the friction pair system is best.The proposed method can provide a strategy for wear prediction in cutting process,and provide a theoretical basis and technical support for antifriction lubrication methods in practical cutting applications.

基于递归图多尺度特征的储能锂离子电池剩余寿命预测方法

锂离子电池在长期循环使用过程中不可避免地会出现性能退化,这直接影响储能锂离子电池系统的稳定运行。为此,本工作提出基于递归图多尺度特征的锂离子电池剩余寿命预测方法,用于解决从一维状态信号中提取关键退化特征的局限性。鉴于递归图像内蕴含丰富的时空退化特征,首先,构建深度学习多尺度特征提取架构,通过可变大小的感受野,识别同一电压区域在多个周期内的时间维度变化以及相邻电压区域之间空间维度的递归图像时空演变,进而提取深层多尺度特征,用于实现从多尺度特征到RUL的映射建模。再次,通过开展综合评估实验,对所提出方法的预测效果进行系统验证。研究结果表明,该方法使用有限数量的充电过程递归图作为输入,能实现模型快速收敛和准确预测。此外,在跨倍率预测场景中,相较于浅层指标,可实现在2C倍率下将绝对误差和均方根误差的指标性能提升约7倍和5.7倍。最后,通过开展与一维序列输入的对比实验,进一步验证基于递归图多尺度特征进行锂离子电池剩余寿命预测的有效性,实现了各评价指标约50%和43%的性能提升,同时成像所需时序电压采样点数据量相对较小。

采用递归图编码技术与残差网络的滚动轴承故障诊断

针对传统故障诊断方法未充分挖掘故障信号的时间序列间关联性特征的问题,将递归图编码技术引入故障诊断领域,提出了递归图编码技术与残差网络的滚动轴承故障诊断模型。采用递归图编码方式将振动信号转换为增强信号特征的二维纹理图像;将这些特征图像输入残差网络中,结合残差网络对二维图像数据优秀的自适应特征提取能力,对滚动轴承进行故障诊断。使用凯斯西储大学轴承数据集和某局机务段采集的真实机车轴承数据进行试验验证,结果表明:所提模型对轴承故障诊断的识别准确率为99.99%和99.83%;在输入不同的数据长度和变工况的试验中,所提模型均保持了良好的故障诊断效果;对比其他常见的故障诊断方法,所提模型拥有更好的泛化性能和识别准确率。

基于知识蒸馏与RP-MobileNetV3的电能质量复合扰动识别

针对复合电能质量扰动(power quality disturbance,PQD)识别中特征提取复杂、识别正确率低和模型难以轻量化等问题,提出一种利用递归图(recurrence plot,RP)对PQD信号可视化方法和基于知识蒸馏的模型训练方法。首先,基于RP挖掘PQD信号隐含特征并构建图像数据集,并利用深度残差收缩网络(deep residual shrinkage network,DRSN)对图像数据集进行更深层次特征提取并完成自主分类。然后,基于知识蒸馏(knowledge distillation,KD)让已训练的DRSN指导轻量化网络MobileNetV3进行训练,通过蒸馏实现知识的跨网络传输。最后,仿真实验和硬件实验表明,利用知识蒸馏训练的MobileNetV3能实现高精度且轻量化的复合扰动识别,同时在30 dB噪声环境下正确率能提升1.06%,对实际扰动信号识别效果良好,具有良好的噪声鲁棒性。

基于卷积神经网络和多变量递归图的斜拉桥拉索损伤识别

大跨索承桥梁在实际运营中除了承受自重和车辆荷载外,还易受到地震、强风和车辆超载等威胁,结构构件容易发生损伤。传统的损伤识别方法具有精度低和鲁棒性差等问题,具有一定的局限性。针对斜拉桥中的易损构件斜拉索,采用多变量递归图分析方法对包含非平稳和非线性成分的主梁加速度信号生成递归图,将其作为卷积神经网络的输入样本,实现了斜拉索损伤位置和程度的识别。模型斜拉桥数值模拟研究结果表明,在拉索单处损伤程度达15%时识别的召回率超过90%,准确率超过88%,损伤识别效果良好,具有较为广阔的应用前景。

基于递归图和张量分解的故障可诊断性评价方法

可诊断性反映了系统故障诊断的难易程度,对系统进行可诊断性评价是开展故障诊断工作的前提条件。针对可诊断性评价问题,将测点信号考虑在内,提出了一种基于递归图和张量分解的故障可诊断性评价方法。利用相空间重构技术对装备在不同状态下的测点信号进行图形化表示,形成递归图;对递归图进行分析以提取其中的特征,并将该特征视为原信号特征;通过张量分解的方法,计算不同状态下信号特征之间的相似程度,作为故障诊断难易程度的基本度量。通过仿真实验对所提出的方法与仅考虑系统模型的D矩阵的可诊断性评价方法进行对比,结果表明该方法在评价可诊断性方面具有准确性和客观性。

基于递归图的结构损伤识别

结构损伤检测是建筑、机械等工程领域中的一大热点,通过振动信号对结构进行整体评估,了解结构当前的状态,并基于此为结构的运营维护提供参考依据,具有重要工程意义。递归图具有能反映结构内部特性的特点,递归图定量分析方法常用于分析结构的信号递归图,本文使用五层钢框架模型实验探讨了利用该方法进行结构损伤识别的可行性。