Adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change rate.In order to overcome the shortcomings,the STFrFT method with adaptive window function is proposed.In this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired signal.This method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

Estimation of structural modal parameters by fourier transform with an optimal window

An adaptive Fourier Transform （FT） with an optimal window has been proposed for the time-frequency analysis of nonstationary time series. The method allows for a good estimation of both frequency and amplitude of the spectrum and can be easily applied to the general case of time-varying signals. The evaluation of the proposed approach has been performed on measured time-varying signals from a suspension bridge model and a steel frame model whose data have the typical non-stationary characteristics. The numerical results show that the proposed approach can overcome some of the difficulties encountered in the classic Fourier transform technique and can achieve higher computation accuracy.

Improved Spectral Representation for Birdcall Based on Fractional Fourier Transform

A novel spectral representation based on fractional Fourier transform （FrFT） is proposed and applied to birdcall analysis. The FrFT-based spectrogram of a signal is derived and compared with its FT-based counterpart, and the spectrum gathering method is used to show the energy distribution related to the pitch frequency. The fixed transform order and adaptive orders for FrFT are tested. The fixed order can be obtained empirically or calculated according to the known chirp rate. The adaptive optimal orders are determined by using ambiguity function. Experimental results with birdcalls show that the FrFT-based spectrogram with an optimal transform order has higher resolution than its STFT-based counterpart, and the better performance can be achieved if adaptive orders are used.

分数阶Fourier变换在多分量信号谱图分析中的应用

作为时频分析方法的一种,谱图对多分量信号分析时受交叉项影响,特别是当信号相隔很近时尤为严重,而且频率分辨率会受影响。给出了结合分数阶Fourier变换(FrFT)对多分量信号进行谱图分析的方法。首先利用分数阶二阶矩极值点而找到相应的最优旋转阶数,对所给多分量信号按此阶数做分数阶Fourier变换,再在此基础上做谱图分析。仿真实例表明,该方法对初始频率、调频率很接近的多分量的chirp信号能有效识别,交叉项可得到较好的抑制。

Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN

The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

SRMD:Sparse Random Mode Decomposition

Signal decomposition and multiscale signal analysis provide many useful tools for timefrequency analysis.We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram.The randomization is both in the time window locations and the frequency sampling,which lowers the overall sampling and computational cost.The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes,and thus leads to a new data-driven mode decomposition.The applications include signal representation,outlier removal,and mode decomposition.On benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.

基于快速傅里叶增强深度网络的树皮种类识别

快速且准确的树种识别为生态系统分析提供重要指导,但现有基于深度学习的方法无法满足林场实际作业需求,需要更简化、可部署的模型,为此笔者提出一种基于快速傅里叶增强深度神经网络的双分支网络。在第1个主干分支ResNet34中引入了门控通道注意力模块(GCT),以保证使用轻量模型且识别精度稳定;第2个分支利用快速傅里叶变换进行辅助监督,将树皮图像的灰度分布转换为频谱图分析图像纹理,通过辅助监督主干网络学习纹理差异性特征。本方法在3个树皮图像数据集上进行实验,其中,在23类树皮图像数据集中,准确率、预测精度和召回率3项评估指标分别达到了91.01%,89.65%和88.60%。此外,本方法显著减少了模型参数量,与原模型ResNet34相比,减少了33%的权重参数量。结果表明,该方法不仅提高了识别效率,降低了人力资源浪费,而且通过减少模型参数量,便于在各种终端设备上进行部署。

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

基于生成对抗网络的数字音频信号多声道增强方法

基于生成对抗网络的数字音频信号多声道增强方法,提升数字音频清晰度、降低噪声对其的干扰。以结合傅里叶的双边语谱图滤波算法初步实现数字音频多声道信号去噪;构建基于对抗神经网络的数字音频信号多声道增强模型,将去噪后的数字音频多声道信号作为该模型的输入数据,输出增强的多声道数字音频信号,完成数字音频信号多声道增强。实验结果表明:该方法增强后的数字音频信号传输效果较好,同时信噪比和音频客观评判基准较高,且去噪后语谱图中噪点以及模糊区较少,能够有效去除信号中的噪声,提升数字音频信号质量。

Rolling bearing fault diagnostics based on improved data augmentation and ConvNet

Convolutional neural networks(CNNs)are well suited to bearing fault classification due to their ability to learn discriminative spectro-temporal patterns.However,gathering sufficient cases of faulty conditions in real-world engineering scenarios to train an intelligent diagnosis system is challenging.This paper proposes a fault diagnosis method combining several augmentation schemes to alleviate the problem of limited fault data.We begin by identifying relevant parameters that influence the construction of a spectrogram.We leverage the uncertainty principle in processing time-frequency domain signals,making it impossible to simultaneously achieve good time and frequency resolutions.A key determinant of this phenomenon is the window function's choice and length used in implementing the shorttime Fourier transform.The Gaussian,Kaiser,and rectangular windows are selected in the experimentation due to their diverse characteristics.The overlap parameter's size also influences the outcome and resolution of the spectrogram.A 50%overlap is used in the original data transformation,and±25%is used in implementing an effective augmentation policy to which two-stage regular CNN can be applied to achieve improved performance.The best model reaches an accuracy of 99.98%and a cross-domain accuracy of 92.54%.When combined with data augmentation,the proposed model yields cutting-edge results.

Wind turbine clutter mitigation using morphological component analysis with group sparsity

To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.

联合时频分析应用于局部放电信号处理的研究

将短时傅立叶变换谱、Wigner分布及 Gabor谱等 3种联合时频分析方法应用于离散时间域 ,分析并处理了油中沿面放电波形。结果表明 ,时频分析方法可以较好地描述局部放电信号频谱的时变特性。其中 ,Gabor谱不仅在时频域具有和 Wigner分布相同的高分辨率 ,优于短时傅里叶变换谱 ,而且能有效地消除 Wigner分布中交叉干扰的影响。对时频谱的分析还发现局部放电信号的频率分量可达到 GHz,为超高频和超宽频带局部放电检测的研究提供了理论依据。

基于短时分数阶傅里叶变换的时频分析方法

本文研究了短时分数阶傅里叶变换(STFRFT)时频分析方法的分辨率精度和算法性能.首先,文中给出了一种STFRFT时频分辨率的数学计算表达式,其有利于时频分辨率的量化比较,仿真结果表明该理论量化值与观察值基本吻合;其次,针对算法运算量大的问题,提出了一种STFRFT的快速计算方法,它较传统的穷举搜索方法运算量约降低1个数量级;最后,给出了算法估计误差的理论分析并运用该方法对多目标信号进行了分析,仿真表明该方法可有效抑制交叉项和解决多分量时频信号的分离问题.

基于短时傅里叶变换的纺织材料纤维取向度测量方法

为了更准确地表征纺织材料的纤维取向分布,基于纺织材料纤维扫描电镜(SEM)图像,提出了一种利用短时傅里叶变换的纺织材料纤维取向度测量方法.把纤维图像看作总体非平稳、局部平稳的信号,首先通过对纤维图像加窗并使窗口平移覆盖整个时域,实现纤维图像的短时傅里叶变换;然后选取频谱图中的感兴趣区域;接着统计频谱图中感兴趣区域沿不同方向角的能量并找出每个子块中纤维的主方向;最后对所有子块的主方向进行统计得到纺织材料纤维取向度.对4个样品的测试结果表明:使用本文方法得到的测量结果与传统的FFT方法测量结果相比较,90°方向和180°方向的纤维比例平均分别减少14.75%和15.25%,说明本文方法能有效减少边界效应,更准确地表征纺织材料纤维的取向度.

基于短时傅立叶变换语言信号分析算法

文中利用基于短时傅立叶变换的频谱分析技术快速地在普通PC机上完成语音信号的实时频谱分析。使用该方法所需的编程简单,对硬件设备的要求低,易于实现。

一种改进的基于FrFT的SAR运动目标检测与成像方法

针对传统的基于FrFT的SAR地面运动目标检测方法的不足,该文结合分数阶Fourier域滤波、重排谱图以及CLEAN思想,提出了一种改进的方法。该方法能够以较高精度估计目标的多普勒起始频率和中心频率,并能分辨在时频平面位于同一直线的多个目标。最后给出了FrFT数值计算结果与有量纲的多普勒参数之间的转换公式。仿真结果表明了该方法的有效性。

基于现场可编程门阵列的语谱图显示与增强

在基于现场可编程门阵列(FPGA)的语谱分析研究与设计中,直接显示的语谱图不能够体现语谱的细节变化情况,针对这一问题,提出了一种适合FPGA实现的语谱图增强显示的方法。该方法通过非线性变换,将高灰度分辨率图像压缩为低灰度分辨率图像,能更好地体现语谱图的细微变化。由于人眼对灰度的分辨能力远低于对色彩的分辨能力,对灰度图像进行了伪彩色处理,并将结果通过视频图形阵列(VGA)方式显示。实验结果表明,通过该方法可以获得更多的语谱图所表达的视觉细节信息。

基于时频表示的跳频信号分析

针对跳频信号的非平稳性 ,提出了基于时频表示的跳频信号分析方法。该方法能够有效地描绘出跳频信号的频率随时间跳变的规律 (跳频图案 ) ,具有很高的时间 -频率分辨率 ,同时能够有效抑制或减小交叉项的干扰 ,计算时可采用递归算法 ,运算量较小 ,因此能够满足跳频系统的快速分析、跟踪、测量等要求。计算机仿真验证了应用时频表示法分析跳频信号的有效性和实用性。

基于反褶积短时傅里叶变换的薄储层流体识别——以元坝为例

元坝地区长兴组台地边缘礁滩相储层油气资源潜力巨大,礁后滩储层厚度薄,气水识别是该区的勘探难点之一。低频伴影是进行流体识别的有效方法,但对薄储层进行流体识别难度巨大。为此,引入了反褶积短时傅立叶变换。相较于传统时频分析方法有更高的时间分辨率和频率分辨率。由于该方法利用了信号短时傅里叶变换,在理论模型和实际数据分析中发现同一地震剖面取不同时窗时其"低频阴影"油气检测结果存在很大差异。为此,通过建立模型以及实际地震数据实验,得到了最佳时间和频率分辨率的地震主频与时窗组合关系式。在此基础上可进行高分辨的低频伴影分析。实例应用表明该方法效果明显。